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Harvesting the Low-Hanging Fruits: Defending Against Automated Large-Scale Cyber-Intrusions by Focusing on the Vulnerable Population

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Harvesting the Low-Hanging Fruits: Defending Against Automated Large-Scale Cyber-Intrusions by Focusing on the Vulnerable Population Harvesting the Low-hanging Fruits: Defending Against Automated Large-Scale Cyber-Intrusions by Focusing on the Vulnerable Population Hassan Halawa Konstantin Beznosov Yazan Boshmaf University of British Columbia University of British Columbia Qatar Computing Research Institute Vancouver, Canada Vancouver, Canada Doha, Qatar Baris Coskun Matei Ripeanu Elizeu Santos-Neto Yahoo! Research University of British Columbia Google, Inc. New York, US Vancouver, Canada Zurich,¨ Switzerland ABSTRACT and Google [13, 36, 37]. To carry out further attacks, The orthodox paradigm to defend against automated cyber criminals use social engineering to exploit unsafe social-engineering attacks in large-scale socio-technical decisions by individual users by tricking them into providing systems is reactive and victim-agnostic. Defenses credentials to phishing websites [37], accepting friendship generally focus on identifying the attacks/attackers (e.g., requests from socialbots [12], or downloading malware [91]. phishing emails, social-bot infiltrations, malware offered Such, largely automated, attacks are increasing in frequency, for download). To change the status quo, we propose to scale, and sophistication [56, 71]. As a case in point, one identify, even if imperfectly, the vulnerable user population, of such attacks, phishing (i.e., a social engineering attack that is, the users that are likely to fall victim to such using fraudulent emails/websites to trick users into leaking attacks. Once identified, information about the vulnerable account credentials), causes sizeable financial losses: $1.6 population can be used in two ways. First, the vulnerable billion only in 2013 [66]. A recent study [67] highlights population can be influenced by the defender through several the magnitude of the problem: about half of the 1,000 means including: education, specialized user experience, participants actively clicked on a phishing link, and half extra protection layers and watchdogs. In the same vein, of those actually leaked information to phishing websites, information about the vulnerable population can ultimately becoming victims of the attack. be used to fine-tune and reprioritize defense mechanisms State of the art defenses against cyber intrusion to offer differentiated protection, possibly at the cost of in socio-technical systems are mostly reactive and additional friction generated by the defense mechanism. victim-agnostic. In general, these defenses do not attempt to Secondly, information about the user population can be harness user characteristics, and instead focus on identifying used to identify an attack (or compromised users) based attack entry points, such as phishing emails or friend requests from socialbots, based on structural, contextual, or on differences between the general and the vulnerable 1 population. This paper considers the implications of the behavioral attributes of the attack(er) [22] . The commonly proposed paradigm on existing defenses in three areas used defense mechanisms, presented in Figure 1 from (phishing of user credentials, malware distribution and a high-level design standpoint, typically employ operator socialbot infiltration) and discusses how using knowledge of filters to automatically detect known attacks or anomalies. the vulnerable population can enable more robust defenses. However, as attacks do evolve and due to the need to minimize false positives, a considerable number of attacks pass through this filter and reach users (e.g., a phishing Keywords email ending up in the user's inbox). Vulnerable population; cyber intrusions; defense system Once the threat is exposed at the user level, it is then design up to the user to decide how to respond (e.g., by flagging a phishing email as malicious, ignoring it, or following the 1. INTRODUCTION phishing link). This manual vetting process can be thought of as a second filtering level, marked as a user filter in Social engineering is one of the key attack vectors Figure 1. Attacks that bypass this filter turn the user into a faced by large socio-technical systems such as Facebook victim: A user whose assets, including account credentials, private data, or personal devices, are compromised. Permission to make digital or hard copies of part or all of this work for personal or Usually, the system operator eventually detects some of classroom use is granted without fee provided that copies are not made or distributed the compromised assets, either prompted by the victim, by for profit or commercial advantage and that copies bear this notice and the full citation other users, or based on the abnormal behavior detected by on the first page. Copyrights for third-party components of this work must be honored. an automated incident-response system. Shortly after, the For all other uses, contact the owner/author(s). operator launches a remediation process, which is illustrated NSPW ’16 September 26-29, 2016, Granby, CO, USA as the remediation filter in Figure 1. After the compromise is © 2016 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-4813-3/16/09. 1 DOI: http://dx.doi.org/10.1145/3011883.3011885 Defenses based on anomaly detection also fit here [41]. Attack Launched (2) aid vulnerable users in making better decisions (e.g., e.g., phishing emails, malicious socialbots through user education, personalized interfaces, enforced hardware/software configuration/restrictions, and/or better machine learning, graph analytics (1) Operator Filters understanding of the decision making process of different categories of users), and (3) achieve faster and more accurate compromise detection and remediation for attacks that were System Infiltrated not previously observed (the intuition is that, a user's e.g., email in inbox, request vulnerability score can be used by the defender to prioritize received monitoring and to make more accurate decisions on whether best practices, an asset is compromised when abnormal behaviour is (2) User Filters privacy controls observed). Our contribution. Our proposal draws insights from multiple areas (including usable security, user-aware security User Victimized mechanisms, public health), and is informed by the current e.g., credentials stolen, data breached mass-scale attacks in large socio-technical systems and by existing defenses, some of which do explicitly focus on the incident response, user reports (3) Remediation Filters vulnerable population. However, in contrast with existing ad-hoc, system-specific solutions we approach harnessing the vulnerable population under a single, unified framework that Current Proposed Paradigm Paradigm encompasses diverse application areas. To the best of our Feedback based on Compromise Detected Feedback based on knowledge, we are the first to propose and discuss such a identifying attack patterns Identifying vulnerable users framework as a complementary security paradigm. Figure 1: High-level schematic presenting the Note that we do not aim to present in detail concrete structure of existing defenses against mass-scale defense mechanisms and algorithms that leverage the attacks (left-side feedback loop) and the proposed vulnerable population. Our goal is to put forward a new paradigm (right-side): augmenting the feedback security paradigm which opens new research opportunities loop with information about vulnerable users to in thwarting cyber intrusions in socio-technical systems. improve the operator filter (1), users' own decision For a specific instantiation of this paradigm and a detailed making process (2), and the remediation filter (3). description of defense mechanisms and algorithms we point the reader to our recent work on social-bot infiltration detection [10]. Furthermore, we note that targeted attacks (e.g., spear phishing attacks) are beyond the scope of the cleared, the resulting attack analysis concerning the involved proposed paradigm. assets and attack pattern are generally fed back into the The structure of this paper. To illustrate the potential operator and remediation filters to detect future, similar of this paradigm, we develop our ideas in more detail in attacks and other compromised users. the context of three specific application areas: mitigating The reactive and victim-agnostic nature of this defense phishing-based credential theft (x2.1), malware distribution paradigm [9, 19, 21, 26, 52, 53, 62, 63, 85, 89, 94] gives (x2.2), and socialbot infiltration (x2.3). For each of attackers the opportunity to evade detection (by adjusting these application areas, we discuss where current defense their tactics), circumvent the employed defenses, and still mechanisms fail, how identifying the vulnerable population reach the end-users. could proceed, and how information about vulnerable We advocate a complementary approach: a population can help to build more resilient defense systems. victim-centric defense. We propose to detect the We then discuss (x3) topics related to the feasibility of vulnerable population (that is, the users that are likely to identifying the vulnerable population, factors influencing its become victims, e.g., users likely to fall prey to a phishing accuracy, and possible issues related to adopting this new attack) and to thwart attacks by: focusing defense efforts on paradigm. the vulnerable population and, at the same time, using the information about the vulnerable population to design more robust defenses. We postulate that the information about 2. COMPARING THE CURRENT AND the vulnerable population can be used to build proactive THE PROPOSED PARADIGM defenses (the defender
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