New approaches for content-based analysis towards online social network spam detection

Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. Although spam filtering techniques are capable of detecting significant percentage of the spam messages, the problem is far from being solved, specially due to the total amount of spam traffic that flows over the Internet, and new potential attack vectors used by malicious users. The deeply entrenched use of Online Social Networks (OSNs), where millions of users share unconsciously any kind of personal data, offers a very attractive channel to attackers. Those sites provide two main interesting areas for malicious activities: exploitation of the huge amount of information stored in the profiles of the users, and the possibility of targeting user addresses and user spaces through their personal profiles, groups, pages... Consequently, new type of targeted attacks are being detected in those communication means. Being selling products, creating social alarm, creating public awareness campaigns, generating traffic with viral contents, fooling users with suspicious attachments, etc. the main purpose of spam messages, those type of communications have a specific writing style that spam filtering can take advantage of. The main objectives of this thesis are: (i) to demonstrate that it is possible to develop new targeted attacks exploiting personalized spam campaigns using OSN information, and (ii) to design and validate novel spam detection methods that help detecting the intentionality of the messages, using natural language processing techniques, in order to classify them as spam or legitimate. Additionally, those methods must be effective also dealing with the spam that is appearing in OSNs. To achieve the first objective a system to design and send personalized spam campaigns is proposed. We extract automatically users’ public information from a well known social site. We analyze it and design different templates taking into account the preferences of the users. After that, different experiments are carried out sending typical and personalized spam. The results show that the click-through rate is considerably improved with this new strategy. In the second part of the thesis we propose three novel spam filtering methods. Those methods aim to detect non-evident illegitimate intent in order to add valid information that is used by spam classifiers. To detect the intentionality of the texts, we hypothesize that sentiment analysis and personality recognition techniques could provide new means to differentiate spam text from legitimate one. Taking into account this assumption, we present three different methods: the first one uses sentiment analysis to extract the polarity feature of each analyzed text, thus we analyze the optimistic or pessimistic attitude of spam messages compared to legitimate texts. The second one uses personality recognition techniques to add personality dimensions (Extroversion/Introversion, Thinking/Feeling, Judging/ Perceiving and Sensing/iNtuition) to the spam filtering process; and the last one is a combination of the two previously mentioned techniques. Once the methods are described, we experimentally validate the proposed approaches in three different types of spam: email spam, SMS spam and spam from a popular OSN.Hartzailearen baimenik gabe bidalitako mezuak (spam) egunean milioika erabiltzaileri eragiten dien mehatxua dira. Nahiz eta spam detekzio tresnek gero eta emaitza hobeagoak lortu, arazoa konpontzetik oso urruti dago oraindik, batez ere spam kopuruari eta erasotzaileen estrategia berriei esker. Hori gutxi ez eta azken urteetan sare sozialek izan duten erabiltzaile gorakadaren ondorioz, non milioika erabiltzailek beraien datu pribatuak publiko egiten dituzten, gune hauek oso leku erakargarriak bilakatu dira erasotzaileentzat. Batez ere bi arlo interesgarri eskaintzen dituzte webgune hauek: profiletan pilatutako informazio guztiaren ustiapena, eta erabiltzaileekin harreman zuzena izateko erraztasuna (profil bidez, talde bidez, orrialde bidez...). Ondorioz, gero eta ekintza ilegal gehiago atzematen ari dira webgune hauetan. Spam mezuen helburu nagusienak zerbait saldu, alarma soziala sortu, sentsibilizazio kanpainak martxan jarri, etab. izaki, mezu mota hauek eduki ohi duten idazketa mezua berauen detekziorako erabilia izan daiteke. Lan honen helburu nagusiak ondorengoak dira: alde batetik, sare sozialetako informazio publikoa erabiliz egungo detekzio sistemak saihestuko dituen spam pertsonalizatua garatzea posible dela erakustea; eta bestetik hizkuntza naturalaren prozesamendurako teknikak erabiliz, testuen intentzionalitatea atzeman eta spam-a detektatzeko metodologia berriak garatzea. Gainera, sistema horiek sare sozialetako spam mezuekin lan egiteko gaitasuna ere izan beharko dute. Lehen helburu hori lortzekolan honetan spam pertsonalizatua diseinatu eta bidaltzeko sistema bat aurkeztu da. Era automatikoan erabiltzaileen informazio publikoa ateratzen dugu sare sozial ospetsu batetik, ondoren informazio hori aztertu eta txantiloi ezberdinak garatzen ditugu erabiltzaileen iritziak kontuan hartuaz. Behin hori egindakoan, hainbat esperimentu burutzen ditugu spam normala eta pertsonalizatua bidaliz, bien arteko emaitzen ezberdintasuna alderatzeko. Tesiaren bigarren zatian hiru spam atzemate metodologia berri aurkezten ditugu. Berauen helburua tribialak ez den intentzio komertziala atzeman ta hori baliatuz spam mezuak sailkatzean datza. Intentzionalitate hori lortze aldera, analisi sentimentala eta pertsonalitate detekzio teknikak erabiltzen ditugu. Modu honetan, hiru sistema ezberdin aurkezten dira hemen: lehenengoa analisi sentimentala soilik erabiliz, bigarrena lan honetarako pertsonalitate detekzio teknikek eskaintzen dutena aztertzen duena, eta azkenik, bien arteko konbinazioa. Tresna hauek erabiliz, balidazio esperimentala burutzen da proposatutako sistemak eraginkorrak diren edo ez aztertzeko, hiru mota ezberdinetako spam-arekin lan eginez: email spam-a, SMS spam-a eta sare sozial ospetsu bateko spam-a

Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis

Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced

Document-level sentiment analysis of email data

Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further

Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods

SDRS: a new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

A Machine Learning Technique for Detection of Social Media Fake News

publishedVersio

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies

A discrete hidden Markov model for SMS spam detection

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naive Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets