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Register a new userAgainst All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
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Added by
Erwin Quiring
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Machine learning-based systems for malware detection operate in a hostile environment. Consequently, adversaries will also target the learning system and use evasion attacks to bypass the detection of malware. In this paper, we outline our learning-based system PEberus that got the first place in the defender challenge of the Microsoft Evasion Competition, resisting a variety of attacks from independent attackers. Our system combines multiple, diverse defenses: we address the semantic gap, use various classification models, and apply a stateful defense. This competition gives us the unique opportunity to examine evasion attacks under a realistic scenario. It also highlights that existing machine learning methods can be hardened against attacks by thoroughly analyzing the attack surface and implementing concepts from adversarial learning. Our defense can serve as an additional baseline in the future to strengthen the research on secure learning.
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Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.
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.
The Spectre vulnerability in modern processors has been widely reported. The key insight in this vulnerability is that speculative execution in processors can be misused to access the secrets. Subsequently, even though the speculatively executed instructions are squashed, the secret may linger in micro-architectural states such as cache, and can potentially be accessed by an attacker via side channels. In this paper, we propose oo7, a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them. Our key contribution is to balance the concerns of effectiveness, analysis time and run-time overheads. We employ control flow extraction, taint analysis, and address analysis to detect tainted conditional branches and speculative memory accesses. oo7 can detect all fifteen purpose-built Spectre-vulnerable code patterns, whereas Microsoft compiler with Spectre mitigation option can only detect two of them. We also report the results of a large-scale study on applying oo7 to over 500 program binaries (average binary size 261 KB) from different real-world projects. We protect programs against Spectre attack by selectively inserting fences only at vulnerable conditional branches to prevent speculative execution. Our approach is experimentally observed to incur around 5.9% performance overheads on SPECint benchmarks.
With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge node to misclassify. Due to the property of transferability, the adversary can easily make a black-box attack using a local substitute model. Nevertheless, the limitation of resource of edge nodes cannot afford a complicated defense mechanism as doing on the cloud data center. To overcome the challenge, we propose a dynamic defense mechanism, namely EI-MTD. It first obtains robust member models with small size through differential knowledge distillation from a complicated teacher model on the cloud data center. Then, a dynamic scheduling policy based on a Bayesian Stackelberg game is applied to the choice of a target model for service. This dynamic defense can prohibit the adversary from selecting an optimal substitute model for black-box attacks. Our experimental result shows that this dynamic scheduling can effectively protect edge intelligence against adversarial attacks under the black-box setting.
As collaborative learning allows joint training of a model using multiple sources of data, the security problem has been a central concern. Malicious users can upload poisoned data to prevent the models convergence or inject hidden backdoors. The so-called backdoor attacks are especially difficult to detect since the model behaves normally on standard test data but gives wrong outputs when triggered by certain backdoor keys. Although Byzantine-tolerant training algorithms provide convergence guarantee, provable defense against backdoor attacks remains largely unsolved. Methods based on randomized smoothing can only correct a small number of corrupted pixels or labels; methods based on subset aggregation cause a severe drop in classification accuracy due to low data utilization. We propose a novel framework that generalizes existing subset aggregation methods. The framework shows that the subset selection process, a deciding factor for subset aggregation methods, can be viewed as a code design problem. We derive the theoretical bound of data utilization ratio and provide optimal code construction. Experiments on non-II
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