Medical Care |

Medical Care



Hot zone identification: analyzing effects of data sampling on spam clustering

Hot Zone Identification: Analyzing Effects of Data Sampling On Spam ClusteringRasib KhanUniversity of Alabama, Birmingham Mainul MizanUniversity of Alabama, Birmingham Ragib HasanUniversity of Alabama, Birmingham Alan SpragueUniversity of Alabama, Birmingham Follow this and additional works at: Part of the nd the Recommended CitationKhan, Rasib; Mizan, Mainul; Hasan, Ragib; and Sprague, Alan (2014) "Hot Zone Identification: Analyzing Effects of Data SamplingOn Spam Clustering," Journal of Digital Forensics, Security and Law: Vol. 9: No. 1, Article 5.
Available at: This Article is brought to you for free and open access by ERAU ScholarlyCommons. It has been accepted for inclusion in Journal of DigitalForensics, Security and Law by an authorized administrator of ERAUScholarly Commons. For more information, please contact Journal of Digital Forensics, Security and Law, Vol. 9(1) HOT ZONE IDENTIFICATION: ANALYZING EFFECTS OF
Department of Computer and Information Sciences University of Alabama at Birmingham 115A Campbell Hall, 1300 University Boulevard Birmingham, Alabama 35294-1170 ABSTRACT
Email is the most common and comparatively the most efficient means of exchanging information in today's world. However, given the widespread use of emails in all sectors, they have been the target of spammers since the beginning. Filtering spam emails has now led to critical actions such as forensic activities based on mining spam email. The data mine for spam emails at the University of Alabama at Birmingham is considered to be one of the most prominent resources for mining and identifying spam sources. It is a widely researched repository used by researchers from different global organizations. The usual process of mining the spam data involves going through every email in the data mine and clustering them based on their different attributes. However, given the size of the data mine, it takes an exceptionally long time to execute the clustering mechanism each time. In this paper, we have illustrated sampling as an efficient tool for data reduction, while preserving the information within the clusters, which would thus allow the spam forensic experts to quickly and effectively identify the ‘hot zone' from the spam campaigns. We have provided detailed comparative analysis of the quality of the clusters after sampling, the overall distribution of clusters on the spam data, and timing measurements for our sampling approach. Additionally, we present different strategies which allowed us to optimize the sampling process using data-preprocessing and using the database engine's computational resources, and thus improving the performance of the clustering process. Keywords: Clustering, Data mining, Monte-Carlo Sampler, Sampling, Spam, Step Sequence Sampler,
Stepping Random Sampler, Hot Zone
Spam emails are mostly generated by malware bots on different computers across the Internet. However, Advancement of the IT infrastructure significantly malwares installed by the same spammer exhibit a affects the way people communicate. Social specific pattern in the spam emails (Nhung and interaction and information exchange are highly Phuong 2007; Ying et al., 2010). The content of the dependent on emails and other such forms of media. spam is usually generated using a common template. At the same time, such medium of communication Therefore, the identification of the pattern in these has been the target of misuse since the beginning. spam emails is significantly important to IT forensic Thus, the negative motives from spammers have experts. The identified pattern can then help identify been a serious issue, which have led to phishing, a specific spammer and follow through with proper viruses, malware bots, and other such attacks. investigations (Dagon et al., 2007; Ono et al., 2007). Mining spam emails helps discover and correlate Journal of Digital Forensics, Security and Law, Vol. 9(1) useful patterns. Most of the mining techniques are measurements for the different operations in the text-based, given that such spam emails are mostly algorithm. The paper also includes a different text-oriented. Once the emails are scrutinized for approach to optimize the sampling process, utilizing such patterns, different clustering techniques and the efficiency of the database engine, which allowed algorithms can be applied over the email data to us to enhance the resulting performance of the group the spams based on some similarity criteria. The speed of producing faster clusters from large Contributions: The contributions in this paper are
datasets depends on efficient algorithms. However, in case of very large datasets, it might be required to reduce the size of the data prior to the clustering  We evaluate the sampling methods on actual spam emails from the UAB Spam Data Mine. The validation and effectiveness of In this paper, we focus on the evaluation of sampling is based on the following: (a) clustering performed on sampled spam emails. The quality of the clusters produced, (b) the data data used is from the Spam Data Mine at the cover/distribution of spam emails within the University of Alabama at Birmingham (UAB) data mine, and (c) the timing performance (UAB-CIS, 2013). The UAB Spam Data Mine is a for the clustering operation. All the large and widely researched repository for spam sampling models have been validated for emails, and is used as a helpful resource by varying sampling rates against the clusters researchers from different global organizations. created using the complete data set. Our Given the huge number of spam emails collected results show that we are successfully able to every day, the clustering of the spams take a long highlight the ‘hot zone' from the spam time. However, in this work, instead of focusing on emails with a significant improvement in algorithms to optimize the clustering process, we timing performance. considered sampling the dataset prior to fetching it  We present techniques and strategies for the to clustering algorithms. Once we are able to prove most efficient way to implement the sampling as an efficient and applicable solution for sampling process and retrieve the huge data reduction, we believe appropriate clustering number of spam emails from the data mine, algorithms can be applied accordingly. We have which are then used to execute the clustering adopted the previous work done by Chun Wei et al., algorithm. The experimental measurements to create the clusters based on patterns in the subject using our optimization strategies illustrate header of the spam emails (Wei et al., 2009). that there are further improvements in In this work, we have utilized four simple methods performance, compared to naïve SQL query of sampling that we have applied on the spam data based retrieval of sampled spam records from the data mine. As a result, we aim in making from the UAB Spam Data Mine. the process of clustering more efficient and less time The rest of the paper is organized as follows. The consuming. Furthermore, we provide the results to motivation for the work is presented in Section 2. illustrate that the sampled data from the UAB Spam Section 3 describes the organization of the UAB Data Mine preserves the information contained for forming clusters and highlight the ‘hot zone'. In this Spam Data Mine, including the clustering algorithm context, we refer to ‘hot zone' as the most prominent from the work of Wei et al. (2009). The different sampling models are described in Section 4. The clusters with respect to spamming activities. We results and corresponding analysis are presented in have presented the results in order to support our Section 5. Section 6 includes the optimization claim of using sampled spam data to allow strategies to improve the efficiency of the sampling investigators a faster and better opportunity to identify the ‘hot zone' in spam clusters. We process. Finally the related works and conclusion are presented in Section 7 and Section 8 respectively. illustrated the resulting clusters from the sampled data, and performed extensive comparative analysis 2. RESEARCH MOTIVATION
with the clusters formed using the whole data set. Our evaluation includes an analysis of the data The increasing number of Internet users has attracted distribution on the spam data, and also the time criminals to the field of online crimes. eCrimes have Journal of Digital Forensics, Security and Law, Vol. 9(1) been significantly on the rise since the last few years. indicate that the network for criminal activities have This section illustrates the issue of eCrimes on the outgrown the authorities dealing with eCrimes. Internet, and the research motivation behind the work on investigating spam clusters, and the 2.2 Spam Investigation
importance of identifying the hot zone. Spam emails are perceived as being analogous to junk mails. These emails are generally advertising 2.1 eCrimes on the Internet
emails, or with other forms of undesired content. Information security and economics have become However, spam emails are not as innocent as junk interdependent in recent times. Corporations employ mails. They are sent to a large number of recipients, information security specialists, as well as and usually have hidden motives along with the economists and lawyers to deal with the rising content of the email. They are considered as the concern of eCrimes. The network of criminal primary channel for attackers to deploy Trojans, activities has become more organized with worms, viruses, spyware, and botnets on other structured online black markets, where the criminals machines across the Internet. trade insider information. Data and information, The email body of spams has hidden scripts, cookies, such as credit card and PIN codes, are sold to online and other attached content to attract the recipient of anonymous brokers in these underground eCrime the email. Once the user opens the email, the scripts markets. According to Moore et al. (2009), credit may use the current information from the browser to card information are sold at advertised prices of expose the identity of the user to the attacker. This is $0.40 to $20.00 per card, and bank account the easiest and a very well-known approach, but still credentials at $10 to $100 per bank account. Social the most common scenario where users are victims security numbers and other personal details are sold of identity thefts on the Internet. This information for $1 to $15 per person, while online auction can be used to remotely access the user's machine credentials fetches around $1 to $8 per identity. and install unwanted malwares as botnets. The Subsequently, the brokers sell the information to malware can then operate from the infected machine specific expert hackers, who perform the final act of using the identity of the user, and send further spam money laundering. emails or perform other unwanted tasks. The information collected in these online criminal When an attacker sends a spam, he generally uses a template to generate the content of the email. The Usually, Internet users are driven to false websites format of the content is thus prevalent in all the spam with the help of advertising emails. These bulk emails those are being sent. However, the spammers emails are generally classified as spams, which are replace some words or phrases to introduce variation sent by spammers, using malicious software running and hence bypass the spam filters. Thus, it becomes on infected machines. The infected computers are a non-trivial task for such filtering services to detect used by the spammers to record keystrokes and send all the spam. Data mining from spam emails is useful further spam emails. to detect and investigate these patterns. The spam The monetizing channel for spam emails includes emails are scrutinized and parsed into different text- multiple organizations. It is illustrated by Levchenko based segments. Each email comprises of certain et al. (2011), the spam value chain has multiple links attributes, such as the sender email, subject header, between the money handling authorities and the and the mail body. These individual attributes can be investigated to match other spam emails, and thus approximate consensus, 5% of online devices on the grouping similar spam emails. Once a pattern is Internet are susceptible to being infected with observed, they can be clustered and classified as a malware. At least 10 million personal computers specific spam campaign (Caruana and Li 2008; have been assumed to be infected with malware in Kyriakopoulou and Kalamboukis 2008; Sasaki and 2008, the number for which should have had Shinnou 2005; UAB-CIS 2013; Wei et al., 2009; increased significantly over the last few years Ying et al., 2010). The individual clusters obtained (Moore et al., 2009). Thus, these figures easily from grouping spam emails allow the eCrime investigators to identify a particular spammer. The Journal of Digital Forensics, Security and Law, Vol. 9(1) clustered spams are examined to classify the law enforcement authorities to study the criminals. It spammer and obtain further track-down information. is more important to identify the largest clusters eCrime investigators use these collected data to hunt rather than obtaining an extensive number of clusters down online criminals and take appropriate actions for the huge amount of spam from the data mine. It against the involved personnel. might not be the same scenario when it comes to user privacy protection and spam filters on web browsers The Spam Data Mine at UAB collects approximately and email clients, where more fine-grained spam 1 million spam emails each day (UAB-CIS, 2013). filtering is required to protect the users on the The spam emails can then be used to find the patterns Internet. Therefore, when it comes to criminal and perform clustering on the collected data. The investigations and law enforcement, the prominent identified clusters are assumed to be individual spam clusters are the ones of interest, while the smaller campaigns by an attacker. The extracted patterns ones can be classified as outliers. from the spam emails are dependent on the template used by the spammer to generate the spam. 3. CLUSTERING SPAM DATA
However, it should also be noted that an attacker generally uses a given spam template for a few days, For our work in this paper, we have adopted an after which he changes the format of the emails. This existing clustering algorithm proposed by Wei constant change in the format of the spams makes it (2010) and Wei et al. (2009). The algorithm has been difficult to identify a particular attacker. As a result, executed using data from the UAB Spam Data Mine spam emails collected over a small duration of time (UAB-CIS, 2013). In this section, we discuss the exhibits the specific pattern, after which the background and the description of the data mine, extracted cluster information does not apply any including the clustering technique proposed by Chun Wei et al. (2009, 2010) on the spam data. From the above scenario, we have observed the 3.1 Background
following requirements for investigating eCrimes The initial research issue for knowledge extraction using spam clusters. First, it is important that the or data mining is classifying data and creating identification of the spam campaigns should be done representations of the feature space. Clustering is as early as possible. The multitude of financial loss most commonly used for feature compression and resulting from eCrimes requires the investigation to proceed quickly. The sooner a particular spam Kalamboukis, 2008). Specific features are compared campaign is taken down, the lesser is the financial and clustered into groups which represent a loss. A quick action against a spam campaign would commonality among all of its data items. The task of also mean that lesser people will fall as victims to measuring the similarity of data items can be the campaign on the Internet. However, given the performed in different ways. The most common huge amount of data, it requires a lot of time to methods for measuring similarity/dissimilarity are execute the clustering operation. Thus, the inherent Jaccard and Levenshtein coefficients (Jaccard 1901; requirement to act quickly against such eCrimes is Levenshtein 1966). The distances can then be used not fulfilled with the current approaches for in other clustering algorithms to create and evaluate clustering spam emails. Moreover, the quickly clusters (Caruana and Li 2008; Kanungo et al., 2002; changing pattern of templates by the spammers Hartigan and Wong 1979; Wei 2010; Ying et al., makes it more difficult to extract the information 2010). The clustering algorithms thus use the from the spams and act on it accordingly. similarity or dissimilarity of individual data items Second, the ‘hot zone' of the spam campaigns are based on the feature space, and group them into a the ones about which conclusive remarks can be common cluster based on preset threshold made about an attacker. Here, we refer ‘hot zone' as the group of largest clusters and the most prominent 3.2 The Spam Data Mine
spam campaigns on the Internet. The largest spam clusters imply a large number of similar spam We utilized the UAB Spam Data Mine (UAB-CIS, emails. As a result, the larger clusters incorporate 2013) for the purpose of our research evaluation. more information for the eCrime investigators and The UAB Spam Data Mine is a research project Journal of Digital Forensics, Security and Law, Vol. 9(1) under The Center for Information Assurance and received from numerous sources and honey-pots, Joint Forensics Research (CIS-JFR)1. The Center and collects approximately 1 million spam emails generates information about currently on-going campaigns by spammers. It archives spam emails The collection of spam emails from the sources is Subsequently, the spam data mine stores the data collected in a batch-wise operation. General users on regarding spam emails parsed into different the Internet, upon receiving a (suspected) spam attributes. The current database design holds the email, marks the email as spam, and forwards it to following attributes for each spam email: the honey-pot email address for archiving. message_id, sender_name, Additionally, numerous other honey-pots are placed sender_username, sender_domain, sender_ip, at different points in the network which dedicatedly receiving_date, time_stamp, word_count. receive and archive spam emails. The archived spam 3.3 Algorithm for Clustering
emails are collected batch-wise at specific time intervals during the day. Thus, due to the manner The method employed by Wei et al. (2009) for these spam emails are stored and collected in the clustering the spam data is specific to the data from data mine, the records do not display a shuffled the UAB Spam Data Mine (UAB-CIS, 2013). In this organization in their sequence. section, we present the clustering algorithm designed and implemented by Wei et al. (2009) and 1 The Center (CIS-JFR), Journal of Digital Forensics, Security and Law, Vol. 9(1) also included as a part of the work in Wei (2010). hash of the sender's email. Similar items were For our purpose, we chose the rather ‘fast-n-dirty' clustered into a common group. From within the version of the clustering algorithm by Wei, which is clusters, some of them are set aside using a bounded shown in Algorithm 1. The clustering algorithm threshold, which was set at a minimum of (mean + matched spam emails on exact similarity of sender email addresses. They are matched using the MD5 Figure 1 Sampling Methods: Step Sequence Sampler (SSS), Stepping Random Sampler (SRS), and Monte Carlo Sampler (MCS) Next, the process was repeated for the word_count initializes using a 48-bit long random seed. of the email body for all the small clusters, and Subsequently, it is modified using a linear further clusters were created. As a result, some of the congruential formula to generate a stream of pseudo- clusters had both the sender_name and the random numbers (Knuth, 2006). Alternatively, word_count in the feature space, while some only Mersenne Twister is another method for polynomial had the word_count criteria. Finally, a Levenstein calculations over two-element fields to generate index is calculated to create a common pattern for uniform pseudo-random numbers (Matsumoto and the subject header for each of the clusters. The Nishimura 1998). However, our random generator output patterns of subject headers for the spam uses the linear congruential formula due to the emails are produced in the form ‘ similar simplicity of the model, and serves the purpose of word'. Here, the blank spaces are the words which could be substituted for other words. The blank spaces together with the words ‘ The simple random sampler takes in a range of similar' and ‘word' values within a begin/end index for message_ids. define the basic template of the subject headers for Subsequently, it generates the random indexes each of the clusters of similar spam emails. within the given range, according to the desired sampling rate. However, the generated random 4. SPAM DATA SAMPLING
indexes may or may not be evenly distributed across Sampling is a well-known technique for data the range of values for the message_ids. reduction, given that it preserves the information 4.2 Step Sequence Sampler
from the original data set. In this section, we present our approaches to create the sampled data. We have The step sequence sampler is another method of presented four different schemes for creating the sampling which we utilized for our spam data. As sampled data, which have been discussed in the shown in Figure 1a, given the sampling rate r, we following sections. For each of the models, we initially calculated the step frequency f. The range of invoke the sampling method with the begin index, values for the message_ids is then divided into f- end index, and sampling rate parameters. segments, and the boundary index values are returned as the sampled indexes. As a result, the 4.1 Simple Random Sampler
obtained sampled data is evenly distributed, and The simple random sampler is implemented using sequentially selected from the data set.
the Java Random class2. The Java Random class 2 Java Random class, Journal of Digital Forensics, Security and Law, Vol. 9(1) 4.3 Stepping Random Sampler
to probabilistically generate some random indexes for choosing the sampled message_ids, as illustrated The stepping random sampler is an extension of the in Figure 1c, and presented in Algorithm 2. step sequence sampler, as shown in Figure 1b. As before, we calculated the step frequency f for the In the Monte Carlo sampler, for each index i, where given range of message_ids based on the sampling i is between begin and end, we ‘roll' between 0 -100. rate. After that, we utilized the Java Random class to If the random ‘roll' is less than or equal to the randomly select an index from within each block. sampling rate r, we select the specific index i. Thus, Thus, the sampled index values for the message_ids the sampled indexes are sequentially selected or are evenly distributed with the frequency f, and discarded from within the range of begin and end randomized within each blocked segment, thus indexes for message_ids. However, the number of ensuring unbiased results. index values that we receive from the Monte Carlo sampler is not exact, but probabilistically close to 4.4 Monte Carlo Sampler
match the sampling rate r. The success or fail events Monte Carlo methods refer to computational in Monte Carlo models are usually executed for a algorithms which are based on repeated random large number of events. Therefore, according to the sampling to obtain a desired goal. It is a process of model, the larger the range of message_ids, the calculating heuristic probability for a given scenario closer we get to the desired value for the number of which is defined by the specific validation of a sampled items (Hammersley et al., 1965). success or fail event (Hammersley et al., 1965). In 4.5 Comparison of Sampling Methods
our case, we designed a simple Monte Carlo sampler Table 1 Comparison of properties for the Random Sampler (RS), Step Sequence Sampler (SSS), Stepping Random Sampler (SRS), and the Monte Carlo Sampler (MCS) Number of samples The properties of the different sampling methods are Repetition in sampling means the possibility summarized in Table 1. In this context, we define the of an index being chosen more than once. following properties for the different sampling Data cover represents the feature of the chosen sampled indexes being evenly distributed over the range of values from the Randomness in the sampling process original data set. implies the probability of a particular index Number of samples refers to the number of being chosen in the sample. indexes chosen, given the total number of Sequential sampling refers to the criteria of indexes n, and the sampling rate r. the chosen indexes being in order once the sampling process has completed. Journal of Digital Forensics, Security and Law, Vol. 9(1) As shown in Table 1, the simple random sampler of the data. The rightmost bar on Figure 2 shows the provides good randomness, as it depends on a simple distribution of the clusters which were created from linear congruential formula to generate the pseudo- complete data set for the given range of days. It can random number stream. However, it is not be seen that the ten largest clusters actually represent sequential, as the chosen index samples are almost 25% of the whole data set, with three largest generated at random, and does not preserve order. clusters representing approximately 9%, 8%, and 3% Additionally, the simple random sampler does not guarantee uniqueness, as the same number can be Next, we executed the clustering algorithm on generated more than once. Therefore, the already sampled data with each of our samplers. The mentioned properties can be utilized to state that the sampling was performed at varying rates of 1%, 2%, simple random sampler does not provide a 3%, 5%, and 8% respectively. For each of the cases, guaranteed data cover either. The step sequence we analyzed the clusters created with the sampled sampler does not provide any randomness and is data. To visualize the clustering quality with better purely sequential. However, we are able to ensure no understanding, we normalized each of the sampled repetition and full data cover. Using the stepping clusters using the size of the sample to calculate the random sampler allows mediocre randomness, but clustering factor for each. Using a normalized view contains sequence, ensures uniqueness, and also for the sampled clusters thus makes it easier to provides a full data cover. Finally, the Monte Carlo evaluate the quality of the clustering with respect to method provides good randomness and ensures the clusters formed using the full data set. The sequentiality with no repetition. However, it has a clustering factor for each of the sampling methods at probabilistic sample size of approximately (n*r), varying sampling rates is illustrated in Figure 2. where n is the data size and r is the sampling rate. The probability of the sample size will get closer to From the results, it can be seen that random (n*r) with a greater range of values for the indexes. sampling, step sequence, and stepping random create the clusters with a similar clustering factor as that of 5. RESULTS AND ANALYSIS
the full data set. Thus, the more similar the clustering In this section, we present the results obtained from factors and distributions are, the better they can be claimed to have performed. It should also be noted previously. The sampled data were mined and used that all the three sampling methods perform in a to create clusters, based on the algorithm of Wei et stable manner with their varying sampling rates. al. (2010) (Ying et al., 2010). We also provide an Additionally, we verified that each of the ten largest analysis of the results and comparison of each of the clusters from the sampled data actually coincides sampling methods against clustering performed on with at least eight of the largest clusters from the full the full data set. The results presented have been dataset. However, they might sometimes be slightly generated using two days' spam data. As mentioned out of order in the sampled cluster sizes. Moreover, earlier, the data mine collects a huge number of spam the top three to five clusters as shown in Figure 2 is emails, and there were a total of approximately 1.8 always the same clusters in all the cases, which million spam emails in these two days. verifies that the sampling effectively allows us to identify the ‘hot zone' of spam campaigns. Table 2 5.1 Clustering Quality
describes the patterns of subject headers for each of Initially, we performed the clustering on the whole the top ten clusters created in order of their sizes. It spam data for a range of two days. With the clusters can be seen that most of the clusters created from the formed, we selected the ten largest clusters and 2% step sequence sampling are exactly in the same analyzed their statistics. We recorded the number of order if compared to the clusters created using the data points, pattern of the subject within the cluster, full data set. However, there are minor interchanges and the percentage of data that each of the clusters in the position of the clusters in their ordering. has with respect to the data size. We refer to Nonetheless, they are not the top clusters, and are clustering factor as the value between 0 and 1, which usually of similar sizes and hence tend to swap represents the size of the cluster in terms of the size places with minor changes in the order. Journal of Digital Forensics, Security and Law, Vol. 9(1) Table 2 Subject Header Patterns of Ten Largest Clusters Compared using Full Dataset Vs. 2% Sampled Data Clustering on full data set
Clustering using 2% Step Sequence
Canadian Pharmacy: BUY NOW VIAGRA & CIALIS ! Canadian Pharmacy: BUY NOW VIAGRA & CIALIS ! Corporate eFax message - pages Corporate eFax message - pages United Parcel Service notification United Parcel Service notification Purchase your Levitra from one of our drugstores today. Levitra/Viagr/Cialis from $1.25 However, with the Monte Carlo sampler, it can be Figure 3 illustrates the graph to help visualize the seen that the sampled data had some skewness distribution for the complete dataset. The x-axis towards the clustering data points. This can be corresponds to the total number of message_ids for claimed as both positive and negative. Given that the the given date. The y-axis specifies the number of results tend to have a greater clustering factor for the spam emails in the cluster to which the larger clusters and represent almost 45% of the corresponding message_id belongs to. The colored sampled data, it can be argued that Monte Carlo lines are formed by very closely placed data points, sampling makes it easier to focus on the largest and each of the colors represents a different cluster. clusters. However, they tend to distort the actual We also present the data cover graphs generated distribution of clusters and misrepresent the from the clusters created using the four different clustering factor for each of the clusters compared to sampling methods, shown in Figures 4, 5, 6, and 7 the full data. An interesting convergence towards the respectively. The sampled graphs have been desired clustering factor distribution can be seen as produced only for a sampling rate of 2%, which is the sampling rate is increased. sufficient to prove the effectiveness of sampling. It Therefore, from the clusters created and the can be seen that each of the sampling methods have clustering factors, we are able to infer the effect of been equally capable to successfully identify the the different sampling methods. It can be seen that same top clusters which have been created by the random, step sequence, and stepping random complete data set. Additionally, it can be seen that sampling tends to preserve the distribution of the most items which belong to the same cluster reside original data set of spams. Therefore, we can say that closely in the data set. This observation is useful in the sampling models for the above three are asserting the fact that sampling the data which representative sampling. On the other hand, Monte preserves the sequentiality is also able to preserve Carlo seems to perform well in highlighting larger the representation of the dataset. clusters and removing noise from smaller clusters. An interesting observation is the comparison of Hence, we call it noise suppressive sampling. Given tailing or sparse data from Figure 3 compared to any the context and the requirement, each of the of the other Figures 4, 5, 6, and 7. All the sampling sampling methods can be utilized accordingly. methods have nicely cleaned the scattered data 5.2 Data Cover
We utilized the clusters created from our However, the sampled data for step sequence experiments to analyze the distribution of the data in sampler and Monte Carlo sampler (Figure 5 and 7) the spam data mine. We are interested to visualize still shows some minor traces of the existence of the how the spam emails have been archived in the data scattered data in comparison to the original data. In mine, with respect to the cluster each spam email all the cases, the leveling clusters at the bottom are belongs to. In this context, data cover refers to the cluttered together. However, these are the smaller distribution of the spam emails in the data set. Journal of Digital Forensics, Security and Law, Vol. 9(1) clusters and do not play any interesting role in the Additionally, such a pattern of data arrival identification of the ‘hot zone'. strengthens ours claim of sampling being sufficient and effective to preserve the characteristics of the Thus, Figures 3, 4, 5, 6, and 7 illustrates the way the dataset and the largest clusters from the spam emails data set is organized. This can lead us to generalize in the data mine. a pattern of arrivals of spam emails into the archive. Figure 2 Clustering Factor for Ten Largest Figure 3 Spam Distribution based on Clusters for Complete Dataset Figure 4 Spam Distribution based on Clusters for Figure 5 Spam Distribution based on Clusters for Simple Random 2% Sampling Step Sequence 2% Sampling Figure 6 Spam Distribution based on Clusters for Figure 7 Spam Distribution based on Clusters for Stepping Random 2% Sampling Monte Carlo 2% Sampling Journal of Digital Forensics, Security and Law, Vol. 9(1) Figure 8 Timing Performance for Application Level Figure 9 Timing Performance for Database Filtering using Naive SQL Query 5.3 Timing Performance
processing time required for each of the cases of reduced data size using varying sampling rates. Once Here, we present the timing performance the data have been loaded and sampled, the enhancement from mining and clustering the clustering algorithm (Wei 2010; Ying et al., 2010) sampled data compared to using the whole dataset. creates the clusters based on the given data. It can be The database was deployed on a x86 64-bit machine, distinctively seen that the time required for the using Intel 2.4 Ghz processor, with 6 processing whole data set is very high, compared to the sampled cores and 12 GB RAM. Additionally, we executed data clustering. Additionally, the algorithm adapted the Java program to perform the clustering on the from Chun Wei et. al.'s work is the simple and faster same machine. Hence, all timing measurements have version, which still is significantly high compared to been recorded based on the corresponding execution the measurements obtained for the sampled data. times. Figure 8 illustrates the timing measurements The increase in time required with increasing from the different sampling rates, including the sampling rate is not exactly linear, but not quadratic timing for the complete data set. either. Thus, the reduction in the amount of time to The mean time required for loading the data from the perform a whole data set clustering can be reduced database is 4261 milliseconds, and is depicted by the by a factor greater than linear if a sampled data set is lower block in the timing bars in Figure 8. The loading time of the data is almost constant for all 6. SAMPLING OPTIMIZATION
cases. This is because the query executed on the database from the application requests for the For further research, we explored some strategies to complete dataset for the specified day(s). Once the optimize the process of sampling. In our opinion, the data is received, the application then performs an timing performance of sampling can be improved if application level filtering of the data, by either we are able to perform the operation on the database selecting or discarding the item, based on the engine. The following sections illustrate our process sampled indexes generated separately. Thus, given of investigation and the methods we adopted to that the machine executing the program had fulfill the requirements. sufficient main memory, the task of on-memory 6.1 Data Preprocessing
filtering of the data was performed within a very short time. Given the huge number of spam emails gathered every day, reading the data items from the database The interesting measurement to be noticed is the required a significant amount of time. In the upper segment in Figure 8, which corresponds to the clustering implementation by Chun Wei et. al. (Wei Journal of Digital Forensics, Security and Law, Vol. 9(1) et al., 2009), they performed a read operation on the queries take an exceptionally long time to load the whole data for a specific date. As a result, this sampled data. Thus, as we failed to improve the incurred to a huge number of read operations on the performance using the naïve SQL query, we database server. investigated further options to optimize the sampling process. We performed some initial data preprocessing to reduce the number of read operations while 6.3 Cross-Product with Temporary Table
retrieving the data items from the database. We Next, we considered executing the query in a created a new table, namely daily_index, with fields
different fashion. In this approach, similar to the receiving_date and message_id. The table was previous, we performed the sampling selection using populated using the minimum values for the the daily_index table. However, the next operation
message_id for each date from the spam table. With included creating a temporary table with only the the daily_index table created, we can now easily
selected message_ids. A query was then executed on retrieve the range of values for message_id for the the database to return the cross-product of the given dates for which we will perform the clustering. temporary table and the spam table. The execution For each sampling method, we initially provide the of cross-product operation is optimized by the message_id range, get the sampled indexes, and database itself, and therefore, the database is able to subsequently, retrieve only the required data items return the resulting records in split seconds. The from the database based on the desired sampling rate timing measurements from using a temporary table r. As a result of this operation, we are able to save and cross-product operation are shown in Figure 10. (n-(n*r/100)) read operations from the database; where n is the total number of records for the given It can be seen that the total time required for the sampled data is much lesser than the time required for the complete data set. As it was seen previously 6.2 Naïve SQL Query
in Figure 9, the load times for the sampled records The initial time measurements were taken based on were significantly high compared to the full data an application level filtering for the sampling retrieval. However, in this case, it can be seen from process. On the contrary, with the data pre- Figure 10 that the load times for sampled processing and the daily_index table created, we
message_ids are around a few hundred milliseconds, initially generated indexes for the sampled which are much lesser compared to the full data. The message_ids. Subsequently, we queried the database maximum load time was required when we reached with a long matching clause of the sampled a sampling rate of 8%, which was still equal to the message_ids to retrieve the required rows. However, load time for the whole data set. If we compare our in this form of queries, we failed to improve the results from the initial timing measurements timing requirement. The size of the query was itself presented in Figure 8, it can be seen that the times very large, and the database took a very long time to for sampling rates 1%, 2%, 3%, and 5% are all much select and load the sampled records. The lesser in our optimized sampling operation. In the measurements from the naïve SQL query are case of 8%, it is still lesser, but maybe comparable illustrated in Figure 9. It can be seen clearly that even to the previously recorded measurements.
though the processing time is reduced, the sampling Journal of Digital Forensics, Security and Law, Vol. 9(1) Figure 10 Timing Performance for Database Filtering using Temporary Table Therefore, with the given results, we can argue that The performance of the clustering process and the the proposed approach is significantly better than the quality of the resultant clusters depends on the original application layer filtering. We have corresponding clustering algorithms. In this paper, successfully illustrated that the processing time for we have successfully illustrated that we are able to the sampled clustering using a temporary table is identify the prominent spam clusters from the much better for reasonable sampling rates. sampled data, with radical improvements in timing Additionally, sampled clustering using this strategy performance for clustering algorithms. There are reduces a lot of task load on the machine which multiple clustering algorithms which explore the executes the clustering algorithm. Even though we text-based patterns in spam emails (Kyriakopoulou had both the program and the database on the same and Kalamboukis 2008; Ramachandran et al., 2007; machine, it can be surely assumed that the database Sasaki and Shinnou 2005; Wei 2010; Wei et al., server is usually a separate machine with more 2009), including clustering algorithms specifically processing power. Therefore, the described method applicable for large datasets (Ganti et al., 1999). of optimizing the process of sampling takes Halkidi et al., proposed further techniques, which advantage of the processing power of the database can be used to validate the clustering quality (2001). engine, and keeps the machine running the clustering Therefore, given that we have proved sampling to be algorithm much lighter in its operation. an effective data reduction process, our following research will focus on optimizing the clustering 7. RELATED WORKS
Researchers have been working on interaction with We have explored different strategies and related large databases for a long time. Data mining and works on clustering mechanisms. The oldest knowledge extraction technologies have been a centroid based clustering method is the k-means rather new addition to the list of research works on algorithm (Hartigan and Wong, 1979). Later, many large data sets. The clustering algorithm used here optimized and efficient versions of the k-means has been the ‘fast-n-dirty' version of Wei's work algorithm have been proposed (Kanungo et al., (Wei 2010; Wei et al., 2009). The focus of this paper 2002). One of the earliest works on modern was to illustrate the efficiency which can be reached clustering techniques was proposed by Koontz et al. prior to the process of clustering, leading to a faster identification of the ‘hot zone'. Therefore, the (1975). They proposed a branch and bound clustering algorithm based on global combinatorial algorithm for clustering is separate from the optimization. DBSCAN is a well-known density- sampling process. As a result, any underlying based clustering algorithm. Arlia et al., proposed a algorithm for the sampling models will provide more method of parallelizing DBSCAN, which is suitable efficient results with respect to time and space. for high-dimensional data, and thus can be useful in Journal of Digital Forensics, Security and Law, Vol. 9(1) implementing a suitable clustering algorithm for the However, given the size of the dataset of the UAB huge number of spam emails (Arlia and Coppola, Spam Data Mine (UAB-CIS, 2013), we suggest that 2001). ST-DBSCAN is a different variation of the purpose of identifying the ‘hot zone' by eCrime DBSCAN, proposed by Birant et al. (2007), which investigators and law enforcement authorities is performs the clustering based on identifying core better served by avoiding such fine-grained spam objects, noise objects, and adjacent clusters. Ying et detection algorithms. al., has already presented in (Ying et al., 2010) a 8. CONCLUSION
variation of DBSCAN to successfully identify spam clusters. The proposed research aims for faster Spam campaigns and emails create a lot of hassle in clustering results from spam emails. Henceforth, it today's world. A lot of people fall victims to such can be suitably stated that, given the organization of scams every day. Most spams are sent using the spam data mine, we will be able to preserve the malware bots, which are installed on affected PCs results from these clustering algorithms, when and spread around like a virus. The UAB Spam Data compared to clustering based on sampled data. Mine collects such spam emails, and provides reports on ongoing spam campaigns. Clustering the There has been significant research on sampling spam data to categorize and identify the spammer methodologies so far. The random sampling with has been implemented using the full dataset. In this reservoir, proposed by Vitter (Vitter 1985), uses a paper, we presented different models for sampling non-replacing one pass sampler, requires constant the spam data, to be used as a tool for data reduction. space, and runs in O(n(1 + log(N/n))) time. These Subsequently, the sampled data were utilized to sampling models aim to introduce randomness in the create the clusters. sampled items. However, we are interested in identifying the most prominent clusters. The purpose Our obtained results substantially prove that is fulfilled using the proposed models and are shown sampling the data and creating the clusters allow the to be effective in determining the ‘hot zone' investigators to interpret the same conclusions, as appropriately. Nagwani et al. (2010) proposed a opposed to using the whole data set. As a result, we weighted matching technique of attributes to claim that it is much faster and efficient to perform measure attribute similarity of email content. The the clusters after sampling the data, and thus identify weights of the attributes are custom assigned and are the ‘hot zone' within a significantly shorter period of then used to create the spam clusters. An algorithm time. We have provided extensive experimental for text clustering based on vector space is presented results using actual spam data and investigated the by Sasaki et al., in (Sasaki and Shinnou, 2005). The distribution of spam in the data mine, which proposed algorithm creates disjoint clusters with the reinforced our claims of sampling being more underlying spherical k-means algorithm to obtain effective given its purpose. Furthermore, we also centroid vectors of the spam clusters. presented an optimization strategy which utilizes the computational power of database engines to perform There are other works related to email filtering the sampling operation more efficiently, and thus which can be related to analyzing the content of promises faster results in terms of the time required. spam emails. An interesting approach for filtering spam emails based on behavioral blacklisting has been proposed by Ramachandran et al. (2007). The This research was supported by a Google Faculty proposed method overcomes the problem of varying Research Award, the Office of Naval Research sender IP addresses by classifying sending patterns Grant #N000141210217, the Department of and behaviors of spammers, and subsequently Homeland Security Grant #FA8750-12-2- 0254, and enforcing blacklisting decisions. Thomas et al., by the National Science Foundation under Grant presents an interesting approach for spam detection, #0937060 to the Computing Research Association which includes real-time web crawling of URLs, for the CIFellows Project. We would like to thank based on blacklists and whitelists (Thomas et al., Jason Britt and Gary Warner for providing the 2011). All the approaches for clustering spam emails support for the UAB Spam Data Mine. are suitable and will have varying results. These algorithms are typically applicable for spam filters, usually on web browsers and email clients. Journal of Digital Forensics, Security and Law, Vol. 9(1) REFERENCES
Algorithm. IEEE Transactions on Computers, 24(9), 908-915. 1. Arlia, D. & Coppola, M. (2001). Experiments in 13. Kyriakopoulou, A. & Kalamboukis, T. (2008). parallel clustering with dbscan. Euro-Par 2001 Combining clustering with classification for Parallel Processing. Lecture Notes in Computer spam detection in social bookmarking systems. Science, 2150. Springer Berlin Heidelberg, 326- Proceedings of European Conference on Machine Learning and Principles and Practice of 2. Birant, D. & Kut, A. (2007). ST-DBSCAN: An Knowledge Discovery in Databases Discovery algorithm for clustering spatial-temporal data. Challenge, (ECML/PKDD RSDC 2008), 47-54. Data & Knowledge Engineering, 60(1), 208- 14. Levchenko, K., Pitsillidis, A., Chachra, N., Enright, B., Halvorson, T., Kanich, C…Savage, 3. Caruana, G. & Li, M. (2008). A survey of S. (2011). Click trajectories: End-to-end emerging approaches to spam filtering. ACM analysis of the spam value chain. Proceedings of Computing Surveys, 44(2), 9:1-9:27. The IEEE Symposium on Security & Privacy, 4. Dagon, D., Gu, G., Lee, C., & Lee, W. (2007). A taxonomy of botnet structures. Proceedings of 15. Levenshtein, V. I. (1966). Binary codes capable the 23rd Annual Computer Security Applications of correcting deletions, insertions and reversals. Conference. ACSAC 2007, 325-339. Soviet Physics Doklady. 10(8 Feb), 707-710. 5. Ganti, V., Ramakrishnan, R., Gehrke, J., & 16. Matsumoto, M. & Nishimura, T. (1998). Powell, A. (1999). Clustering large datasets in 623-dimensionally arbitrary metric spaces. Proceedings of the 15th equidistributed uniform pseudo-random number International Conference on Data Engineering generator. ACM Transactions on Modeling and IEEE Computer Computer Simulation (TOMACS) - Special issue Washington, DC, USA. on uniform random number generation, 8(1 6. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. 17. Moore, T., Clayton, R., & Anderson, R. (2009). Journal of Intelligent Information Systems, 17, The economics of online crime. The Journal of December, 2-3, 107-145. Economic Perspectives, 23(3), 3-20. 7. Hammersley, J. M., Handscomb, D. C., & 18. Nagwani, N. K. & Bhansali, A. (2010). An Weiss, G. (1965). Monte Carlo methods. Email Clustering Model Using Weighted Physics Today, 18, 55. 8. Hartigan, J. A. & Wong, M. A. (1979). International Journal of Research and Reviews Algorithm as 136: A k-means clustering in Computer Science (IJRRCS), 1, 2. algorithm. Journal of the Royal Statistical 19. Nhung, N. P. & Phuong, T. M. (2007). An Society. Series C (Applied Statistics) 28(1), 100- efficient method for filtering image-based spam e-mail. Proceedings of The 12th international 9. Jaccard, P. (1901). Distribution de la flore alpine conference on Computer analysis of images and dans le bassin des Dranses et dans quelques patterns, (CAIP'07). Springer-Verlag, Berlin, régions voisines. Bulletin de la Société Vaudoise Heidelberg, 945-953. des Sciences Naturelles, 37, 241-272. 20. Ono, K., Kawaishi, I., & Kamon, T. (2007). 10. Kanungo, T., Mount, D., Netanyahu, N., Piatko, Trend of Botnet Activities. Proceedings of the C., Silverman, R., & Wu, A. (2002). An efficient 41st Annual IEEE International Carnahan k-means clustering algorithm: analysis and Conference on Security Technology, (ICCST) implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881- 21. Ramachandran, A., Feamster, N., & Vempala, S. 11. Knuth, D. E. (2006). The art of computer blacklisting. Proceedings of the 14th ACM programming. 4, fascicle 4, 1. print. Generating Conference on Computer and Communications all trees. Addison-Wesley. Security, (CCS) 2007. ACM, New York, NY, 12. Koontz, W. L. G., Narendra, P. M., & Fukunaga, K. (1975). A Branch and Bound Clustering Journal of Digital Forensics, Security and Law, Vol. 9(1) 22. Sasaki, M. & Shinnou, H. (2005). Spam detection using text clustering. Proceedings of the International Conference on Cyberworlds, 4(4), 319. 23. Thomas, K., Grier, C., Ma , J., Paxson , V., & Song, D. (2011). Design and evaluation of a real-time url spam filtering service. Proceedings of the 2011 IEEE Symposium on Security and Privacy, (S&P 2011), IEEE, 447-462. University of Alabama at Birmingham, UAB Spam 25. Vitter, J. S. (1985). Random sampling with a reservoir. ACM Transactions on Mathematical Software (TOMS), 11(1 Mar), 37-57. 26. Wei, C. (2010). Clustering Spam Domains and Hosts: Anti-Spam Forensics with Data Mining. Doctoral thesis, University of Alabama at Birmingham. 27. Wei, C., Sprague, A., & Warner, G. (2009). Clustering malware-generated spam emails with a novel fuzzy string matching algorithm. Proceedings of the 2009 ACM symposium on Applied Computing, (SAC 2009), ACM, New York, NY, USA, 889-890. 28. Ying, W., Kai, Y., & Zhong, Jian Z. (2010). Using DBSCAN clustering algorithm in spam identifying. Proceedings of the 2nd International Conference on Education Technology and Computer. (ICETC) 2010, 1, 398-402.


Jurnal Natur Indonesia 15(1), Februari 2013: 57–62 Synthesis and antimalarial activity 57 Synthesis and Antimalarial Activity of 2-Phenyl-1,10-Phenanthroline Derivative Compounds Ruslin Hadanu1*), Mustofa2), and Nazudin1) 1)Department of Chemistry, Faculty of Teachership and Educational Science, Pattimura University, Poka, Ambon 97233


A High Throughput Approach for Metabolite Note: 369 Profiling and Characterization Using theLXQ Linear Ion Trap Mass SpectrometerMin He, Alicia Du, Gargi Choudhary, Karen Salomon and Diane Cho; Thermo Electron Corporation, San Jose, CA, USA Key Words Within the drug discovery environment, high sample • LXQ™ throughput that provides comprehensive drug metabolite