ELM-based spammer detection in social networks

Transcription

1 J Supercomput DOI.7/s ELM-based spammer detection in social networks Xianghan Zheng,2 Xueying Zhang,2 Yuanlong Yu,2 Tahar Kechadi 3 Chunming Rong 4 Springer Science+Business Media New York 25 Abstract Online social networks, such as Facebook, Twitter, and Weibo have played an important role in people s common life. Most existing social network platforms, however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content, malware, viruses, etc. to the legitimate users of the service. The spreading of spam degrades user experience and also negatively impacts server-side functions such as data mining, user behavior analysis, and resource recommendation. In this paper, an extreme learning machine (ELM)-based supervised machine is proposed for effective spammer detection. The work first constructs the labeled dataset through crawling Sina Weibo data and manually classifying corresponding users into spammer and non-spammer categories. A set of features is then extracted from message content and user behavior and applies them to the ELM-based spammer classification algorithm. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99 and %, respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM-based approaches. B Yuanlong Yu yu.yuanlong@fzu.edu.cn College of Mathematics and Computer Science, Fuzhou University, Fuzhou 358, China 2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 358, China 3 School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland 4 Department of Electrical Engineering and Computer Science, University of Stavanger, 436 Stavanger, Norway

2 X. Zheng et al. Keywords machine Social network Spammer Machine learning Extreme learning Introduction With the development of science and technology, social networking sites, such as Facebook, Twitter, and Weibo (previously Sina Weibo), have become important platforms for users to interact with their friends, post messages, discuss hot topics and share views, etc. According to a Statista report [], the number of social network users has reached 2.75 billion until June 24, and is estimated to remain around 2.33 billion users globally until the end of 27. However, online social platforms also attract huge interest from spammers to spread advertisements, disseminate pornography and viruses, and expose phishing and so on. The spreading of spam degrades the user experience and also negatively impacts server-side functions such as data mining, user behavior analysis, and resource recommendation [2,3]. According to Nexgate s report [4], during the first half of 23, the growth of social spam has been 355 %, much faster than the growth rate of authentic accounts and messages on most branded social networks. Since spammers typically behave like legitimate users, detecting and discriminating spam is difficult. Therefore, it becomes highly desirable to develop techniques and methods for identifying spammers and their behavior in online social networks. Currently, there have been a few proposals from industry and academia, discussing possible solutions for spammer detection and analysis. These solutions, however, are either ineffective or based on too many considered conditions (lots of content and behavior features, etc.). This paper investigates social spammer content and behavior issues and proposes an effective extreme learning machine (ELM)-based machine learning model for spammer detection. In conclusion, the paper contains the following main contributions:. The paper adopts the spammer feature to detect spammers and test the results over Sina Weibo, the biggest social network site in China. Under the Weibo API, a specific dataset crawler is developed to extract any unauthorized users public messages inside the Weibo platform. This is the first step of the data analysis. 2. The major novelty of the paper is to study a set of the most important features related to message content and user behaviors and then apply them to the ELM-based classification algorithm for spammer detection. The experiment and comparison work shows that the proposed solution provides higher spammer detection accuracy. 3. The paper compares several aspects of the ELM-based approach to the SVM-based approach, including training and testing time and the sensitivity of parameters. The performance comparison further validates the better feasibility, stability and strong generalization ability of ELM algorithm. It is worth mentioning that although the proposed approach is currently tested specifically in the Sina Weibo social network, it is applicable to all other existing social sites with minor revisions. The rest of the paper is organized as follows: Sect. 2 introduces background information related to social networks, social networking platforms, and surveys existing work on social spammer detection. Section 3 illustrates

3 ELM-based spammer detection in social networks the dataset collection and feature extraction related to content and behavior. Section 4 describes the ELM-based spammer detection model, the experiments, and corresponding evaluation. Finally, the conclusion is given in Sect Background and existing works 2. The social network Sina Weibo is one of the largest social networks in China and attracts millions of users online every day. Weibo is a platform based on user relationships and instantly sharing information through short posts not more than 4 characters via computer or mobile phone [5]. Specifically, Weibo contains the following functions: Follower and Following : each user can choose to start following another user to receive the latest messages and statuses of his/her friends. The user who is followed could either accept or reject the request to following back. Post and Repost: short messages with not more than 4 characters, including punctuation. These posted messages are delivered to followers immediately and the message is made public for anyone to read. Mention: represented meaning that the message sender is willing to share something with the user mentioned. Using a mention, a notification will automatically inform the mentioned user that a message has been sent and is available on his/her homepage. Label: users can post messages containing labels (# #) to identify a specific topic. If enough users pick up this topic, it appears in the list of trending topics. 2.2 Machine learning techniques In the field of machine learning, a series of traditional machine learning algorithms were improved to satisfy the higher data processing needs. For instance, the model of Recently SVM, Least Squares SVM, Limited Newton LSVM [6,7], and so on which reduced the difficulty of solving a certain extent, improved the solution speed. However, they still exist two problems: () the solution speed could not satisfy the processing needs for large data; (2) the model related to SVM needs to manual adjustment parameters (C,γ)frequently and repeat training to obtain the optimal solution with tedious time-consuming process and poor generalization ability. Under the circumstances, extreme learning machine provides a new way to solve these problems. Extreme learning machine (ELM) is a novel machine learning model proposed by Huang [8] as a least square-based learning algorithm for single hidden layer neural networks (SLFNNs). In comparison with traditional neural networks which usually employ back propagation (BP) algorithm [9] to train the connection weights, the tedious process of iterative parameter tuning is eliminated and the slow convergence and local minimum problems are avoided. Currently, ELM has been an important research topic due to its high efficiency, easy-implementation, unification of classification and regression, and therefore might be capable to be implemented in social spammer detection field [].

4 X. Zheng et al. 2.3 Existing works In the past ten years, spam detection and filtering mechanisms have been widely implemented. The main work could be summarized into two categories: a contentbased model and an identity-based model. In the content-based model, a series of machine learning approaches [,2] are implemented that parse content according to keywords and patterns that are potentially spam. In the identity-based model, the most commonly used approach is that each user maintains a whitelist and a blacklist of addresses of people whose s should and should not be blocked by antispam mechanism [3,4]. More recent work is to leverage social network into spam identification according to the Bayesian probability [5]. The concept is to use the social relationship between a sender and a receiver to decide the closeness and trust level in a given relationship, and then increase or decrease the Bayesian probability according to these values. With the rapid development of social networks, social spam has attracted a lot of attention from both industry and academia. In industry, Facebook proposes an EdgeRank algorithm [6] that assigns each post with a score generated from a few features (e.g., number of likes, number of comments, number of reposts, etc.). Therefore, the higher EdgeRank scores, the less possibility to be a spammer. The disadvantage of this solution is that spammers could join their networks and continuously like and comment each other to achieve a high EdgeRank score. In academia, Wang [7] proposes a naïve Bayesian-based spammer classification algorithm to distinguish suspicious behaviors from normal ones on Twitter, with the precision result (F-measure) of 89 %. Yard et al. [8] study the behavior feature of a small sample of spammers on Twitter and find that the behavior of spammers is different than legitimate users in regard to posting tweets, followers, friends, and so on. Stringhini et al. [9] further investigate the spammer features by creating a number of honey-profiles in three large social network sites (Facebook, Titter, and Myspace) and identify five common features (followee-to-follower, URL ratio, message similarity, message sent, and friend number) that may help detect potential spammer activity. Gao et al. [2] adopt a set of novel features for effectively reconstructing spam messages into campaigns rather than examining them individually (with precision value over 8 %). Benevenuto et al. [2] collect a large dataset from Twitter and identify 62 features related to tweet content and user social behaviors. These characteristics are regarded as attributes of machine learning process for classifying users as either spammers or non-spammers. Zheng et al. [22] apply a set of features on SVM classifier to detect spammer and obtain a better classification result; however, this approach leads to higher training time and requires manual adjustment in optimized parameter selection. Besides, many of the researchers had suggested a mechanism via setting active Honeypots running without human inspection and logging information of its fans [23,24], and proposed a feature analysis Spammers mechanism and made a comparison on these features. Furthermore, Zachary et al. [25] proposed two stream clustering algorithms, StreamKM++ and DenStream, which were modified to facilitate spam identification. As a summary, the concept of existing social spam detection work is to extract a set of features that distinguish normal users from spammers and apply that information

5 ELM-based spammer detection in social networks into different classifier models to detect suspicious behavior. Due to the differences in the considered data sources and features, different classifiers might achieve different performance. Generally, this paper follows these similar concepts, however, with two distinct points:. Our proposed ELM-based classification model considers only 8 feature items and achieves the best performance result, with the F-measure value reaching over 99 %. This is the best result ever achieved (although different approaches might not be comparable due to difference of collected dataset). 2. As verified by the experiment results, ELM-based classification tends to achieve better generalization performance than SVM-based solutions. The proposed solution is also less sensitive to user-specified parameters and could be easily implemented. 3 Dataset and feature analysis 3. Dataset collection While Sina Weibo provides a relatively complete API for developers, there are still a lot of constraints in the data collection process. Accordingly, a specific data crawler and feature collection mechanism are developed to solve this problem. Figure describes the basic framework of the data collection and feature extraction. Firstly, we randomly selected normal users from Weibo social network. Because most of the normal users are unlikely to follow spammers in reality, we can crawl the list of users who are following other legitimate users. Similarly, those who follow spam accounts are probably also spammers, which improve the degree of mutual concern. Therefore, the sample set of spammers could be obtained from 5 original spammers. For each user, we crawl corresponding information inside 5 recent messages (although the returned real number of microblogs is less than 5). The Weibo API converts each Weibo ID to details message. 3.2 Feature analysis Spammers usually aim at the commercial intent such as advertisement spreading. In the paper, we randomly select 5 spam messages and 5 normal messages respectively from collected dataset, and assign each message with a random integer value ranged from to 5. We also set the maximum number of reposts, comments and likes to. Figure 2a shows the difference in proportion between the original messages posted by normal user and spammer. Most legitimate users post messages to share personal knowledge and feelings with their friends. On the other hand, most spammers repost messages from others, and therefore cause the proportion of original messages less than %. Figure 2b indicates the proportion of messages containing URLs (the proportion of message contains the URL to the total number of messages). This figure shows that most spammers have at least one URL in each message.

6 X. Zheng et al. Data Source Non-Spammers Spammers Data Crawle Followee Crawler Message Crawler User Data Non-Spammers Spammers Username Feature Extraction Number of Followees Weibo API Number of Followers Created Days Message Crawler Messages IDs Converter Reposts Comments Likes ELM based Feature Learning Non-Spammers Spammers Fig. Dataset craw and feature extract Figure 2c displays the difference in average number of friends mentioned. Considering that most spammers focus on advertising and spend little time interacting with friends, the message content is mostly advertising words and pictures. Legitimate users, however, frequently mention their friends and share funny things. To offer a more specific description, this paper also introduces the cumulative distribution function (CDF) to illustrate the distribution of users behavioral characteristics. The cumulative distribution function (Eq. ) describes the probability that a sample of a random variable X will be less than or equal to a value x, where x is a real value. If X is a continuous random variable then F is a continuous function, and conversely. F (x) = P (X x) ()

7 ELM-based spammer detection in social networks The proportion of the original Weibo NonSpammer Spammer (a) Fraction of message containing URL NonSpammer Spammer (b) Average number of friends mentioned NonSpammer Spammer (c) CDF Spammer Non-Spammer 2 3 (d).8.8 CDF (e) Spammer Non-Spammer CDF Spammer Non-Spammer Fig. 2 Distribution and cumulative distribution function of feature, a the proportion of original messages, b the fraction of messages containing URL, c the average number of friends mentioned, d the number of followees, e the fraction of followees per followers, f the number of created days (f) Figure 2d analyzes the number of people following each user. Normally, spammers try to follow a multitude of legitimate users so as to be followed back. However, it does not work most of the time. This behavior, then, makes the fraction of followees per followers very large in comparison to non-spammers, as illustrated in Fig. 2e.

8 X. Zheng et al. Figure 2f reveals the feature difference in the number of created days. Compared with normal users, most spammers usually own less created day because of anti-spam mechanism that would eventually detect and automatically clean spammer accounts. 4 Spammer detection Based on the dataset and feature collection described in the previous section, a supervised machine learning model is introduced for spammer identification. Supervised learning is the machine learning task of inferring a function from labeled training data that consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called supervisory signal). Through analysis of the training data, supervised learning produces a classification model for predicting new examples. 4. Extreme learning machine Extreme learning machine (ELM) [26] is based on the empirical risk minimization theory and makes use of a single-layer feedforward network for the training of single hidden layer feedforward neural networks (SLFNs) (as illustrated in Fig. 3). The learning process needs only one single iteration and avoids multiple iterations and local minimization. Compared with conventional neural network algorithms, ELMs are capable of achieving faster training speeds and can overcome the problem of overfitting. LetbeasetofP different samples D = (x i, o i ), i =,...,P, where {x i } R m, and {o i } R n. Thus the goal is to find a relationship between {x i } and {o i }. Standard single hidden layer feedforward networks (SLFNs) with N nodes could be mathematically modeled by: N y j = h k f ( ) w k, x j k= (2) where j P,w k stands for the parameters of the k element of the hidden layer, h k refers to the weight that connects k hidden element with the output layer, and f Fig. 3 Feedforward neural network with single hidden layer f ( w, x ).. f ( w, x 2 ) f ( w, x 3 ).. f ( w, x N ) h h 2 h 3 h N y

9 ELM-based spammer detection in social networks represents the function that gives the output of the hidden layer. Equation (2) can be expressed in matrix notation as y = G h, where h is the vector of weights of the output layer and G is given by: f (w, x )... f (w N, x ) G = (3) f (w, x P )... f (w N, x P ) where N is the number of hidden nodes. As mentioned above, ELM proposes a random initialization of the parameters in the hidden layer w k, being the weights of the output layer obtained by the Moore Penrose s generalized inverse [27] according to the expression h = G + o, where G + = ( G T G ) G T is the pseudo-inverse matrix (superscript T means matrix transposition). 4.2 ELM-based spammer detection model Figure 4 illustrates the basic concept of the proposed spammer detection model. In this solution, training data are converted into a series of feature vectors that consists of a set of formulated attribute values. These vectors construct the input value of a supervised machine learning algorithm. After training, a classification model is applied to distinguish whether the specific user belongs to either a normal user or spammer. Because spammers and non-spammers have different social behaviors, it is capable to distinguish abnormal behaviors from legitimate ones. In this paper, we used a model based on 8 features, which were the following: the number of followees, the number of followers, the number of messages, the number of friends following each other, the number of favorites, the number of created days, fraction of followees per followers, fraction of original messages, number of messages per day, the average number of reposts, the average number of comments, average number of likes, the average number of URLs, the average number of pictures, the average number of hashtags, the average Social Network Feature Extraction Web Crawler Feature Vectors Classifier Model Detection Results Data Standardization Extreme Learning Machine Fig. 4 Spammer detection model

10 X. Zheng et al. Table Example of confusion matrix Predicted Spammer Non-spammer True Spammer TP FN Non-spammer FP TN number of user mentions, fraction of messages containing URLs, and fraction of messages containing pictures. To evaluate the effectiveness of the experiment results, we consider a confusion matrix illustrated in Table, where true positive (TP) represents the number of spammers correctly classified, false negative (FN) refers to the number of spammers misclassified as non-spammers, false positive (FP ) expresses the number of non-spammers misclassified as spammers, and true negative (TN) is the number of non-spammers classified correctly. According to the confusion matrix, a set of metrics commonly evaluated in machine learning field are introduced, including: precision (P), recall (R) and F-measure (F). P is the ratio of number of instances correctly classified to the total number of instances and is expressed by the formula: P = TP TP + FP (4) R is the ratio of the number of instances correctly classified to the total number of predicted instances and is expressed with the formula: R = TP TP + FN (5) F-measure is the harmonic mean between precision and recall, and is defined as: F = 2RP R + P (6) For an evaluation of classifiers performance, F measure value is more precise because it is a combination value to summarize both the precision and recall value. 4.3 Classification result and comparison The simulation for ELM algorithms is carried out in MATLAB environment running in a Core i5-347, 3.2 GHZ CPU. Table 2 shows a confusion matrix obtained by ELM classifiers. It shows that our proposed solution is quite efficient, with 99. % spammers and 99.9 % non-spammers classified correctly, leaving only a small fraction of spammers and non-spammers misclassified. Table 3 shows the value of evaluation metrics, in which precision, recall, and F measure are calculated for spammer and non-spammer, respectively.

11 ELM-based spammer detection in social networks Table 2 Confusion matrix Predicted Spammer (%) Non-spammer (%) True Spammer Non-spammer Table 3 Classification evaluation Precision Recall F-measure Spammer Non-spammer Table 4 Comparison between ELM and other classifiers Classifier Precision Recall F-measure Spammer Non-spammer Spammer Non-spammer Spammer Non-spammer ELM SVM Decision tree Naïve Bayes Bayes network Table 5 Comparison between ELM and SVM Classifier Training time (s) Testing time (s) ELM SVM We also compare the proposed solution with other classifiers, including: Decision Tree, Naïve Bayes and Bayes Network, with implementation provided by Weka, a Java data mining software. For each classifier, the same evaluation metrics (precision, recall, and F-measure) are calculated for both spammers and non-spammers. With the results illustrated in Table 4, it is clear that both ELM and SVM classifiers are capable of achieving high accuracy. This observation indicates that ELM- and SVM-based approaches could clearly separate training data into two parts with maximum margin. Besides, it is shown that the three other classifiers also achieve good accuracy. This is because suitable features (including content and user behavior) are selected and capable of effectively distinguishing spammers from non-spammers. Furthermore, we compare training and testing time between SVM-based and ELMbased solutions and the experiment results are illustrated in Table 5. The results indicate that the ELM-based solution is much faster than SVM-based solution, and is therefore more efficient.

12 X. Zheng et al. Finally, to further prove the effectiveness of the proposed spammer detection model, we consider two use scenarios, data standardized and data non-standardized. The paper compares the training time together with testing accuracy under different activation functions (Sin, Sig and Hardlim) and different number of hidden nodes (L). The evaluation is illustrated in Figures 5, 6, 7. Train Time (a) sig-zscore sig-non-zscore Number of Hidden Neurons Test Accuracy (b) sig-zscore sig-non-zscore Number of Hidden Neurons Fig. 5 Comparison of training time and testing accuracy on Sig function, a training time on Sig function with different number of hidden nodes, b testing accuracy on Sig function with different number of hidden 6 Train Time (a) sin-zscore sin-non-zscore Number of Hidden Neurons Test Accuracy (b) sin-zscore sin-non-zscore Number of Hidden Neurons Fig. 6 Comparison of training time and testing accuracy under Sin function, a training time on Sin function with different number of hidden nodes, b testing accuracy on Sin function with different number of hidden Train Time (a) hardlim-zscore hardlim-non-zscore Number of Hidden Neurons Test Accuracy (b) hardlim-zscore hardlim-non-zscore Number of Hidden Neurons Fig. 7 Comparison of training time and testing accuracy under hardlim function, a training time on Hardlim function with different number of hidden nodes, b testing accuracy on Hardlim function with different number of hidden

13 ELM-based spammer detection in social networks Figures 5a, 6a, and 7a show that training time is not significantly influenced under different activation functions whether the data are standardized or non-standardized. Testing accuracy (of standardized data), however, is greatly improved in the case of sin activation function (as shown in Fig. 6b). Therefore, we suggest the formulated dataset be standardized before classification. 4.4 Stability enhancement To achieve good generalization performance, the cost parameter C and kernel parameter of SVM[28,29] need to be chosen appropriately. Furthermore, the ELM should also contain the parameter L that could be adjusted. Accordingly, Fig. 8 compares classification performances between the ELM and SVM solution under different parameters for further stability evaluation. We have used 9 different values of C and 9 different values of γ resulting in a total of 8 pairs of result. The result in Fig. 8a shows that the generalization performance of SVM depends greatly on the combination of (C, γ). Therefore, the SVM-based approach might require tedious and time-consuming parameter tuning in real implementation. On the other hand, the generalization performance of ELM tends to monotonically increase with the increasing number of hidden nodes L, and remains stable when L is larger than 5 (see Fig. 8b). Therefore, from the implementation point of view, another advantage of the ELM-based approach is the stability enhancement. 5 Conclusions and future works The paper presents an ELM-based spammer detection method for social network platforms. Using data crawled from Sina Weibo, a set of content and behavior features are extracted and applied into an ELM-based classification algorithm. Through a set Testing Accuracy(%) (a) C Testing Accuracy(%) Fig. 8 The Stability performance under different parameters, a the performance of SVM is sensitive to the parameters (C, γ ), b the performance of ELM is not sensitive to the parameters (L) (b) L

14 X. Zheng et al. of experiments and evaluation work, our proposed solution is proved to be feasible, efficient, and significantly more stable than existing SVM-based models. However, any amount of labeled data might not be enough in a social network environment with a huge quantity of highly diverse characteristics. Therefore, further work on the subject might include the investigation of a collaborative training-based semi-supervised learning model that is capable to train itself automatically based on a small amount of labeled data. On the other hand, features extracted in our proposed solution (and other existing approaches) are based on statistical analysis and manual selection. In the era of big data with huge data volumes and convenient access, feature extraction mechanisms in our solution might be low in adaptability and somewhat costive. Therefore, considering how to import the concept of Machine Learning technology (e.g., deep learning algorithms [3 33]) into automatic feature learning and extraction has become an important question. Acknowledgments This paper is supported by the National Natural Science Foundation of China under Grant No and No.272, the Key Project of Chinese Ministry of Education under Grant No.2286; the Technology Innovation Platform Project of Fujian Province under Grant No. 29J7, No. 23H6 and 23J228; the Key Project Development Foundation of Education Committee of Fujian province under Grand No. JA and JA26. References. Nexgate (23) State of social media spam. Nexgate-23-State-of-Social-Media-Spam-Research-Report.pdf 2. Bhat SY, Abulaish M (23) Community-based features for identifying spammers in online social networks. In: Proceedings of the 23 IEEE/ACM international conference on advances in social networks analysis and mining. ACM, pp 7 3. Grier C, Thomas K, Paxson V et al (2) At spam: the underground on 4 characters or less[c]. In: Proceedings of the 7th ACM conference on computer and communications security. ACM, pp Liu Y, Wu B, Wang B et al (24) SDHM: a hybrid model for spammer detection in Weibo. Advances in Social networks analysis and mining (ASONAM), 24 IEEE/ACM international conference on. IEEE, pp Rong HJ, Ong YS, Tan AH et al (28) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(): Hsu C-W, Lin C-J (22) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 3(2): Huang GB, Zhu QY, Siew CK (24) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Networks 24. In: Proceedings 24 IEEE international joint conference on. IEEE, vol 2, pp Hirose Y, Yamashita K, Hijiya S (99) Back-propagation algorithm which varies the number of hidden units. Neural Netw 4():6 66. Shen H, Li Z (24) Leveraging social networks for effective spam filtering. IEEE Trans Comput : Uemura M, Tabata T (28) Design and evaluation of a Bayesian-filter-based image spam filtering method, international conference on information security and assurance (ISA), IEEE, pp Zhou B, Yao Y, Luo J (23) Cost-sensitive three-way spam filtering. J Intell Inf Syst 42(): Jung J, Sit E (24) An empirical study of spam traffic and the use of DNS black Lists. In: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, pp

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