273 research outputs found
Scalable Techniques for Anomaly Detection
Computer networks are constantly being attacked by malicious entities for various reasons. Network based attacks include but are not limited to, Distributed Denial of Service (DDoS), DNS based attacks, Cross-site Scripting (XSS) etc. Such attacks have exploited either the network protocol or the end-host software vulnerabilities for perpetration. Current network traffic analysis techniques employed for detection and/or prevention of these anomalies suffer from significant delay or have only limited scalability because of their huge resource requirements. This dissertation proposes more scalable techniques for network anomaly detection.
We propose using DNS analysis for detecting a wide variety of network anomalies. The use of DNS is motivated by the fact that DNS traffic comprises only 2-3% of total network traffic reducing the burden on anomaly detection resources. Our motivation additionally follows from the observation that almost any Internet activity (legitimate or otherwise) is marked by the use of DNS. We propose several techniques for DNS traffic analysis to distinguish anomalous DNS traffic patterns which in turn identify different categories of network attacks.
First, we present MiND, a system to detect misdirected DNS packets arising due to poisoned name server records or due to local infections such as caused by worms like DNSChanger. MiND validates misdirected DNS packets using an externally collected database of authoritative name servers for second or third-level domains. We deploy this tool at the edge of a university campus network for evaluation.
Secondly, we focus on domain-fluxing botnet detection by exploiting the high entropy inherent in the set of domains used for locating the Command and Control (C&C) server. We apply three metrics namely the Kullback-Leibler divergence, the Jaccard Index, and the Edit distance, to different groups of domain names present in Tier-1 ISP DNS traces obtained from South Asia and South America. Our evaluation successfully detects existing domain-fluxing botnets such as Conficker and also recognizes new botnets. We extend this approach by utilizing DNS failures to improve the latency of detection. Alternatively, we propose a system which uses temporal and entropy-based correlation between successful and failed DNS queries, for fluxing botnet detection.
We also present an approach which computes the reputation of domains in a bipartite graph of hosts within a network, and the domains accessed by them. The inference technique utilizes belief propagation, an approximation algorithm for marginal probability estimation. The computation of reputation scores is seeded through a small fraction of domains found in black and white lists. An application of this technique, on an HTTP-proxy dataset from a large enterprise, shows a high detection rate with low false positive rates
An Evasion Attack against ML-based Phishing URL Detectors
Background: Over the year, Machine Learning Phishing URL classification
(MLPU) systems have gained tremendous popularity to detect phishing URLs
proactively. Despite this vogue, the security vulnerabilities of MLPUs remain
mostly unknown. Aim: To address this concern, we conduct a study to understand
the test time security vulnerabilities of the state-of-the-art MLPU systems,
aiming at providing guidelines for the future development of these systems.
Method: In this paper, we propose an evasion attack framework against MLPU
systems. To achieve this, we first develop an algorithm to generate adversarial
phishing URLs. We then reproduce 41 MLPU systems and record their baseline
performance. Finally, we simulate an evasion attack to evaluate these MLPU
systems against our generated adversarial URLs. Results: In comparison to
previous works, our attack is: (i) effective as it evades all the models with
an average success rate of 66% and 85% for famous (such as Netflix, Google) and
less popular phishing targets (e.g., Wish, JBHIFI, Officeworks) respectively;
(ii) realistic as it requires only 23ms to produce a new adversarial URL
variant that is available for registration with a median cost of only
$11.99/year. We also found that popular online services such as Google
SafeBrowsing and VirusTotal are unable to detect these URLs. (iii) We find that
Adversarial training (successful defence against evasion attack) does not
significantly improve the robustness of these systems as it decreases the
success rate of our attack by only 6% on average for all the models. (iv)
Further, we identify the security vulnerabilities of the considered MLPU
systems. Our findings lead to promising directions for future research.
Conclusion: Our study not only illustrate vulnerabilities in MLPU systems but
also highlights implications for future study towards assessing and improving
these systems.Comment: Draft for ACM TOP
Governance by Other Means: Rankings as Regulatory Systems
This article takes the challenges of global governance and legitimacy seriously and looks at new ways in which international organizations (IOs) have attempted to ‘govern’ without explicit legal or regulatory directives. Specifically, we explore the growth of global performance indicators as a form of social control that appears to have certain advantages even as states and civil society actors push back against international regulatory authority. This article discusses the ways in which Michael Zürn\u27s diagnosis of governance dilemmas helps to explain the rise of such ranking systems. These play into favored paradigms that give information and market performance greater social acceptance than rules, laws, and directives designed by international organizations. We discuss how and why these schemes can constitute governance systems, and some of the evidence regarding their effects on actors’ behaviors. Zürn\u27s book provides a useful context for understanding the rise and effectiveness of Governance by Other Means: systems that ‘inform’ and provoke competition among states, shaping outcomes without directly legislating performance
Scalable Techniques for Anomaly Detection
Computer networks are constantly being attacked by malicious entities for various reasons. Network based attacks include but are not limited to, Distributed Denial of Service (DDoS), DNS based attacks, Cross-site Scripting (XSS) etc. Such attacks have exploited either the network protocol or the end-host software vulnerabilities for perpetration. Current network traffic analysis techniques employed for detection and/or prevention of these anomalies suffer from significant delay or have only limited scalability because of their huge resource requirements. This dissertation proposes more scalable techniques for network anomaly detection.
We propose using DNS analysis for detecting a wide variety of network anomalies. The use of DNS is motivated by the fact that DNS traffic comprises only 2-3% of total network traffic reducing the burden on anomaly detection resources. Our motivation additionally follows from the observation that almost any Internet activity (legitimate or otherwise) is marked by the use of DNS. We propose several techniques for DNS traffic analysis to distinguish anomalous DNS traffic patterns which in turn identify different categories of network attacks.
First, we present MiND, a system to detect misdirected DNS packets arising due to poisoned name server records or due to local infections such as caused by worms like DNSChanger. MiND validates misdirected DNS packets using an externally collected database of authoritative name servers for second or third-level domains. We deploy this tool at the edge of a university campus network for evaluation.
Secondly, we focus on domain-fluxing botnet detection by exploiting the high entropy inherent in the set of domains used for locating the Command and Control (C&C) server. We apply three metrics namely the Kullback-Leibler divergence, the Jaccard Index, and the Edit distance, to different groups of domain names present in Tier-1 ISP DNS traces obtained from South Asia and South America. Our evaluation successfully detects existing domain-fluxing botnets such as Conficker and also recognizes new botnets. We extend this approach by utilizing DNS failures to improve the latency of detection. Alternatively, we propose a system which uses temporal and entropy-based correlation between successful and failed DNS queries, for fluxing botnet detection.
We also present an approach which computes the reputation of domains in a bipartite graph of hosts within a network, and the domains accessed by them. The inference technique utilizes belief propagation, an approximation algorithm for marginal probability estimation. The computation of reputation scores is seeded through a small fraction of domains found in black and white lists. An application of this technique, on an HTTP-proxy dataset from a large enterprise, shows a high detection rate with low false positive rates
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