A Belief Approach for Detecting Spammed Links in Social Networks

International audienceNowadays, we are interconnected with people whether professionally or personally using different social networks. However, we sometimes receive messages or advertisements that are not correlated to the nature of the relation established between the persons. Therefore, it became important to be able to sort out our relationships. Thus, based on the type of links that connect us, we can decide if this last is spammed and should be deleted. Thereby, we propose in this paper a belief approach in order to detect the spammed links. Our method consists on modelling the belief that a link is perceived as spammed by taking into account the prior information of the nodes, the links and the messages that pass through them. To evaluate our method, we first add some noise to the messages, then to both links and messages in order to distinguish the spammed links in the network. Second, we select randomly spammed links of the network and observe if our model is able to detect them. The results of the proposed approach are compared with those of the baseline and to the k-nn algorithm. The experiments indicate the efficiency of the proposed model

Evaluation of Email Spam Detection Techniques

Email has become a vital form of communication among individuals and organizations in today’s world. However, simultaneously it became a threat to many users in the form of spam emails which are also referred as junk/unsolicited emails. Most of the spam emails received by the users are in the form of commercial advertising, which usually carry computer viruses without any notifications. Today, 95% of the email messages across the world are believed to be spam, therefore it is essential to develop spam detection techniques. There are different techniques to detect and filter the spam emails, but off recently all the developed techniques are being implemented successfully to minimize the threats. This paper describes how the current spam email detection approaches are determining and evaluating the problems. There are different types of techniques developed based on Reputation, Origin, Words, Multimedia, Textual, Community, Rules, Hybrid, Machine learning, Fingerprint, Social networks, Protocols, Traffic analysis, OCR techniques, Low-level features, and many other techniques. All these filtering techniques are developed to detect and evaluate spam emails. Along with classification of the email messages into spam or ham, this paper also demonstrates the effectiveness and accuracy of the spam detection techniques

AI and extremism in social networks

Studien utforsker hvordan midler som kunstig intelligens, AI- drevne chatbots, kan være kilder man kan regne med som moralske aktører på digitale plattformer og som kan være identifiserbare opprørsmodeller til bekjempelse av ekstremistiske og voldsforherligende ytringer på sosiale medieplattformer. Fremveksten av digital nettverkskommunikasjon har lettet prosessen med sosiale bevegelser, noe fenomenet «Den arabiske våren» tydelig demonstrerer. Sosiale medier har vært et verdifullt verktøy når det gjelder å utvikle kollektive identiteter med en felles ideologi for å fremme et bestemt mål eller en sak og gi alternative plattformer for undertrykte samfunn. Imidlertid forblir virkningen og konsekvensene av sosiale medier i samfunn der maktbalansen forrykkes gjennom fundamentale endringer et bekymringsfullt fenomen. Radikaliserte individer og grupper har også hevdet sin tilstedeværelse på sosiale medieplattformer gjennom å fremme fordommer, hat og vold. Ekstremistiske grupper bruker ulike taktikker for å utøve makten sin på disse plattformene. Bekjempelsen av voldelig ekstremisme på sosiale medieplattformer blir som regel ikke koordinert av aktuelle aktører som regjeringer, sosiale medieselskaper, FN eller andre private organisasjoner. I tillegg har fremdeles ikke forsøk på å konstituere AI til bekjempelse av voldelig ekstremisme blitt gjennomført, men lovende resultater har blitt oppnådd gjennom noen initiativer. Prosjektet som en ‘case study’ ser på den nylige reformen i Etiopia som ble gjennomført av Nobels fredsprisvinner 2019 Abiy Ahmed etter at han tiltrådte som statsminister i Etiopia i april 2018. Etter flere tiår med undertrykkelse har den nye maktovertakelsen der det politiske rommet ble åpnet opp og ytringsfrihet ble tillatt, uventet ført til et skred av etniske gruppers polarisering. Nye etno-ekstremister har dukket frem fra alle kriker og kroker av landet og også fra sin tilværelse i diaspora. Studien ser videre på hvilken rolle sosiale medier til tider spiller ved direkte å presse på for å påvirke til og dermed forårsake voldelige handlinger på grasrota.Ved å bruke en kvalitativ forskningsmetode for ustrukturerte intervjuer med etiopiske brukere av sosiale medier, journalister og aktivister, identifiserer studien kjerneaspektene ved konfliktene og foreslår initiativer som kan brukes til å motvirke voldelig etnisk ekstremisme. Ved å bruke relevant litteratur ser prosjektet videre på innarbeidelsen av kunstig intelligens (AI) i «moralske handlinger» på sosiale medier og hvordan den kan utformes slik at den av seg selv kan ta i bruk moralske beslutningsevner i nettverket. I tillegg ser studien på mulighetene videre for bekjempelse av voldelig ekstremisme og skisserer den spesifikke rollen ikke menneskelige aktører som profesjonelle troll og bots på sosiale medier bør spille for å slåss mot radikalisering som kan føre til voldelige handlinger.Mastergradsoppgave i digital kulturMAHF-DIKULDIKULT35

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

A malware threat avoidance model for online social network users

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe main purpose of this thesis is to develop a malware threat avoidance model for users of online social networks (OSNs). To understand the research domain, a comprehensive and systematic literature review was conducted and then the research scope was established. Two design science iterations were carried out to achieve the research aim reported in this thesis. In the first iteration, the research extended the Technology Threat Avoidance Theory (TTAT) to include a unique characteristic of OSN – Mass Interpersonal Persuasion (MIP). The extended model (TTAT-MIP), focused on investigating the factors that needs to be considered in a security awareness system to motivate OSN users to avoid malware threats. Using a quantitative approach, the results of the first iteration suggests perceived severity, perceived threat, safeguard effectiveness, safeguard cost, self-efficacy and mass interpersonal persuasion should be included in a security awareness system to motivate OSN users to avoid malware threats. The second iteration was conducted to further validate TTAT-MIP through a Facebook video animation security awareness system (referred in this thesis as Social Network Criminal (SNC)). SNC is a Web-based application integrated within Facebook to provide security awareness to OSN users. To evaluate TTAT-MIP through SNC, three research techniques were adopted: lab experiments, usability study and semi-structured interviews. The results suggest that participants perceived SNC as a useful tool for malware threat avoidance. In addition, SNC had a significant effect on the malware threat avoidance capabilities of the study participants. Moreover, the thematic analysis of the semi-structured interviews demonstrated that the study participants‘ found SNC to be highly informative; persuasive; interpersonally persuasive; easy to use; relatable; fun to use; engaging; and easy to understand. These findings were strongly related to the constructs of TTAT-MIP. The research contributes to theory by demonstrating a novel approach to design and deploy security awareness systems in a social context. This was achieved by including users‘ behavioural characteristic on the online platform where malware threats occur within a security awareness system. Besides, this research shows how practitioners keen on developing systems to improve security behaviours could adopt the TTAT-MIP model for other related contexts

A self-healing framework to combat cyber attacks. Analysis and development of a self-healing mitigation framework against controlled malware attacks for enterprise networks.

Cybercrime costs a total loss of about $338 billion annually which makes it one of the most profitable criminal activities in the world. Controlled malware (Botnet) is one of the most prominent tools used by cybercriminals to infect, compromise computer networks and steal important information. Infecting a computer is relatively easy nowadays with malware that propagates through social networking in addition to the traditional methods like SPAM messages and email attachments. In fact, more than 1/4 of all computers in the world are infected by malware which makes them viable for botnet use. This thesis proposes, implements and presents the Self-healing framework that takes inspiration from the human immune system. The designed self-healing framework utilises the key characteristics and attributes of the nature’s immune system to reverse botnet infections. It employs its main components to heal the infected nodes. If the healing process was not successful for any reason, it immediately removes the infected node from the Enterprise’s network to a quarantined network to avoid any further botnet propagation and alert the Administrators for human intervention. The designed self-healing framework was tested and validated using different experiments and the results show that it efficiently heals the infected workstations in an Enterprise network

Sticks and Stones May Break My Bones but Words Will Never Hurt Me...Until I See Them: A Qualitative Content Analysis of Trolls in Relation to the Gricean Maxims and (IM)Polite Virtual Speech Acts

The troll is one of the most obtrusive and disruptive bad actors on the internet. Unlike other bad actors, the troll interacts on a more personal and intimate level with other internet users. Social media platforms, online communities, comment boards, and chatroom forums provide them with this opportunity. What distinguishes these social provocateurs from other bad actors are their virtual speech acts and online behaviors. These acts aim to incite anger, shame, or frustration in others through the weaponization of words, phrases, and other rhetoric. Online trolls come in all forms and use various speech tactics to insult and demean their target audiences. The goal of this research is to investigate trolls\u27 virtual speech acts and the impact of troll-like behaviors on online communities. Using Gricean maxims and politeness theory, this study seeks to identify common vernacular, word usage, and other language behaviors that trolls use to divert the conversation, insult others, and possibly affect fellow internet users’ mental health and well-being