Exploiting altruism in social networks for friend-to-friend malware detection

pre-printWe propose a novel malware detection application- SocialScan-which enables friend-to-friend (f2f) malware scanning services among social peers, with scanning resource sharing governed by levels of social altruism. We show that with f2f sharing of resources, SocialScan achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the the detection rates of individual scanners. We also show that SocialScan provides greatly enhanced malware protection to social hubs

Doctor of Philosophy

dissertationWe develop a novel framework for friend-to-friend (f2f) distributed services (F3DS) by which applications can easily offer peer-to-peer (p2p) services among social peers with resource sharing governed by approximated levels of social altruism. Our frame- work differs significantly from typical p2p collaboration in that it provides a founda- tion for distributed applications to cooperate based on pre-existing trust and altruism among social peers. With the goal of facilitating the approximation of relative levels of altruism among social peers within F3DS, we introduce a new metric: SocialDistance. SocialDistance is a synthetic metric that combines direct levels of altruism between peers with an altruism decay for each hop to approximate indirect levels of altruism. The resulting multihop altruism levels are used by F3DS applications to proportion and prioritize the sharing of resources with other social peers. We use SocialDistance to implement a novel flash file/patch distribution method, SocialSwarm. SocialSwarm uses the SocialDistance metric as part of its resource allocation to overcome the neces- sity of (and inefficiency created by) resource bartering among friends participating in a BitTorrent swarm. We find that SocialSwarm achieves an average file download time reduction of 25% to 35% in comparison with standard BitTorrent under a variety of configurations and conditions, including file sizes, maximum SocialDistance, as well as leech and seed counts. The most socially connected peers yield up to a 47% decrease in download completion time in comparison with average nonsocial BitTorrent swarms. We also use the F3DS framework to implement novel malware detection application- F3DS Antivirus (F3AV)-and evaluate it on the Amazon cloud. We show that with f2f sharing of resources, F3AV achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the average of individual scanners. Furthermore, we show that F3AV provides the greatest diversity of mal- ware scanners (and thus malware protection) to social hubs-those nodes that are positioned to provide strategic defense against socially aware malware

Got Phished? Internet Security and Human Vulnerability

A leading cause of security breaches is a basic human vulnerability: our susceptibility to deception. Hackers exploit this vulnerability by sending phishing emails that induce users to click on malicious links that then download malware or trick the victim into revealing personal confidential information to the hacker. Past research has focused on human susceptibility to generic phishing emails or individually targeted spear-phishing emails. This study addresses how contextualization of phishing emails for targeted groups impacts their susceptibility to phishing. We manipulated the framing and content of email messages and tested the effects on users’ susceptibility to phishing. We constructed phishing emails to elicit either the fear of losing something valuable (e.g., course registrations, tuition assistance) or the anticipation of gaining something desirable (e.g., iPad, gift card, social networks). We designed the emails’ context to manipulate human psychological weaknesses such as greed, social needs, and so on. We sent fictitious (benign) emails to 7,225 undergraduate students and recorded their responses. Results revealed that contextualizing messages to appeal to recipients’ psychological weaknesses increased their susceptibility to phishing. The fear of losing or anticipation of gaining something valuable increased susceptibility to deception and vulnerability to phishing. The results of our study provide important contributions to information security research, including a theoretical framework based on the heuristic-systematic processing model to study the susceptibility of users to deception. We demonstrate through our experiment that several situational factors do, in fact, alter the effectiveness of phishing attempts

A Study of Scams and Frauds using Social Engineering in “The Kathmandu Valley” of Nepal

Social Engineering scams are common in Nepal. Coupled with inability of government to enforce policies over technology giants and large swaths of population that are uneducated, social engineering scams and frauds are a real issue. The purpose of the thesis is to find out the extent and impact of social engineering attacks in “The Kathmandu valley” of Nepal. The Kathmandu valley consists of 3 cities including the capital city of Nepal. To conduct the research, the newspaper “The Kathmandu Post” from the year 2019 to 2022 was downloaded and searched for keywords “scam” and “fraud”. After which the results were manually examined to separate news reports of social engineering attacks in Nepal and other countries. Also, a survey was conducted by visiting parks in the Kathmandu valley. A total of 149 people were interviewed to collect data by asking 21 questions regarding social engineering attack faced by the interviewee. Further, literature review of the research papers published related to social engineering and phishing was conducted. The main finding of the thesis was that public awareness program are effective reducing the extent and impact of social engineering attacks in Nepal. The survey suggests large percentage of population have become victims of social engineering attack attempts. More than 70 percent have received messages on WhatsApp regarding fake lottery wins

Unauthorized Access

Going beyond current books on privacy and security, this book proposes specific solutions to public policy issues pertaining to online privacy and security. Requiring no technical or legal expertise, it provides a practical framework to address ethical and legal issues. The authors explore the well-established connection between social norms, privacy, security, and technological structure. They also discuss how rapid technological developments have created novel situations that lack relevant norms and present ways to develop these norms for protecting informational privacy and ensuring sufficient information security

When Others Impinge upon Your Privacy:Interdependent Risks and Protection in a Connected World

Privacy is defined as the right to control, edit, manage, and delete information about oneself and decide when, how, and to what extent this information is communicated to others. Therefore, every person should ideally be empowered to manage and protect his own data, individually and independently of others. This assumption, however, barely holds in practice, because people are by nature biologically and socially interconnected. An individual's identity is essentially determined at the biological and social levels. First, a person is biologically determined by his DNA, his genes, that fully encode his physical characteristics. Second, human beings are social animals, with a strong need to create ties and interact with their peers. Interdependence is present at both levels. At the biological level, interdependence stems from genetic inheritance. At the social level, interdependence emerges from social ties. In this thesis, we investigate whether, in today's highly connected world, individual privacy is in fact achievable, or if it is almost impossible due to the inherent interdependence between people. First, we study interdependent privacy risks at the social level, focusing on online social networks (OSNs), the digital counterpart of our social lives. We show that, even if an OSN user carefully tunes his privacy settings in order to not be present in any search directory, it is possible for an adversary to find him by using publicly visible attributes of other OSN users. We demonstrate that, in OSNs where privacy settings are not aligned between users and where some users reveal a (even limited) set of attributes, it is almost impossible for a specific user to hide in the crowd. Our navigation attack complements existing work on inference attacks in OSNs by showing how we can efficiently find targeted profiles in OSNs, which is a necessary precondition for any targeted attack. Our attack also demonstrates the threat on OSN-membership privacy. Second, we investigate upcoming interdependent privacy risks at the biological level. More precisely, due to the recent drop in costs of genome sequencing, an increasing number of people are having their genomes sequenced and share them online and/or with third parties for various purposes. However, familial genetic dependencies induce indirect genomic privacy risks for the relatives of the individuals who share their genomes. We propose a probabilistic framework that relies upon graphical models and Bayesian inference in order to formally quantify genomic privacy risks. Then, we study the interplay between rational family members with potentially conflicting interests regarding the storage security and disclosure of their genomic data. We consider both purely selfish and altruistic behaviors, and we make use of multi-agent influence diagrams to efficiently derive equilibria in the general case where more than two relatives interact with each other. We also propose an obfuscation mechanism in order to reconcile utility with privacy in genomics, in the context where all family members are cooperative and care about each other's privacy. Third, we study privacy-enhancing systems, such as anonymity networks, where users do not damage other users' privacy but are actually needed in order to protect privacy. In this context, we show how incentives based on virtual currency can be used and their amount optimized in order to foster cooperation between users and eventually improve everyone's privacy.[...

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering