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1. Distributed Security Network Functions against Botnet Attacks in Software-defined Networks

   Park, Younghee Kengalahalli (November 2018, IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))

   For the past decade, botnets have dominated network attacks in spite of significant research advances in defending against them. The distributed attack sources, the network size, and the diverse botnet attack techniques challenge the effectiveness of a single-point centralized security solution. This paper proposes a distributed security system against largescale disruptive botnet attacks by using SDN/NFV and machinelearning. In our system, a set of distributed network functions detect network attacks for each protocol and to collect real-time traffic information, which also gets relayed to the SDN controller for more sophisticated analyses. The SDN controller then analyzes the real-time traffic with the only forwarded information using machine learning and updates the flow rule or take routing/bandwidth-control measures, which get executed on the nodes implementing the security network functions. Our evaluations show the proposed system to be an efficient and effective defense method against botnet attacks. The evaluation results demonstrated that the proposed system detects large-scale distributed network attacks from botnets at the SDN controller while the network functions locally detect known attacks across different networking protocols. 

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2. Distributed Security Network Functions against Botnet Attacks in Software-defined Networks

   https://doi.org/10.1109/NFV-SDN.2018.8725657  

   Park, Younghee; Kengalahalli, Nikhil Vijayakumar; Chang, Sang-Yoon (November 2018, IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))

   For the past decade, botnets have dominated network attacks in spite of significant research advances in defending against them. The distributed attack sources, the network size, and the diverse botnet attack techniques challenge the effectiveness of a single-point centralized security solution. This paper proposes a distributed security system against large-scale disruptive botnet attacks by using SDN/NFV and machine-learning. In our system, a set of distributed network functions detect network attacks for each protocol and to collect real-time traffic information, which also gets relayed to the SDN controller for more sophisticated analyses. The SDN controller then analyzes the real-time traffic with the only forwarded information using machine learning and updates the flow rule or take routing/bandwidth-control measures, which get executed on the nodes implementing the security network functions. Our evaluations show the proposed system to be an efficient and effective defense method against botnet attacks. The evaluation results demonstrated that the proposed system detects large-scale distributed network attacks from botnets at the SDN controller while the network functions locally detect known attacks across different networking protocols. 

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3. Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

   https://doi.org/10.3390/cryptography8020013  

   Gao, Minyan; Biswas, Liton Kumar; Asadi, Navid; Forte, Domenic (June 2024, Cryptography)

   Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack. 

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4. Breaking On-Chip Communication Anonymity Using Flow Correlation Attacks

   https://doi.org/10.1145/3677034  

   Weerasena, Hansika; Mishra, Prabhat (October 2024, ACM Journal on Emerging Technologies in Computing Systems)

   Network-on-chip (NoC) is widely used to facilitate communication between components in sophisticated system-on-chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker that puts the entire computing infrastructure at risk. We investigate the security strength of existing anonymous routing protocols in NoC architectures, making two pivotal contributions. Firstly, we develop and perform a machine learning (ML)-based flow correlation attack on existing anonymous routing techniques in NoC systems, revealing that they provide only packet-level anonymity. Secondly, we propose a novel, lightweight anonymous routing protocol featuring outbound traffic tunneling and traffic obfuscation. This protocol is designed to provide robust defense against ML-based flow correlation attacks, ensuring both packet-level and flow-level anonymity. Experimental evaluation using both real and synthetic traffic demonstrates that our proposed attack successfully deanonymizes state-of-the-art anonymous routing in NoC architectures with high accuracy (up to 99%) for diverse traffic patterns. It also reveals that our lightweight anonymous routing protocol can defend against ML-based attacks with minor hardware and performance overhead. 

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5. Demystifying the Adversarial Robustness of Random Transformation Defenses

   Sitawarin, Chawin; Golan-Strieb, Zachary; Wagner, David (February 2022, AAAI-22 Workshop: Adversarial Machine Learning and Beyond)

   Dong, Yinpeng; Pang, Tianyu; Yang, Xiao; Wong, Eric; Kolter, Zico; He, Yuan (Ed.)

   Current machine learning models suffer from evasion attacks (i.e., adversarial examples) raising concerns in security-sensitive settings such as autonomous vehicles. While many countermeasures may look promising, only a few withstand rigorous evaluation. Recently, defenses using random transformations (RT) have shown impressive results, particularly BaRT (Raff et al. 2019) on ImageNet. However, this type of defense has not been rigorously evaluated, leaving its robustness properties poorly understood. The stochasticity of these models also makes evaluation more challenging and many proposed attacks on deterministic models inapplicable. First, we show that the BPDA attack (Athalye, Carlini, and Wagner 2018) used in BaRT’s evaluation is ineffective and likely overestimates its robustness. We then attempt to construct the strongest possible RT defense through the informed selection of transformations and Bayesian optimization for tuning their parameters. Furthermore, we create the strongest possible attack to evaluate our RT defense. Our new attack vastly outperforms the baseline, reducing the accuracy by 83% compared to the 19% reduction by the commonly used EoT attack (4.3× improvement). Our result indicates that the RT defense on Imagenette dataset (ten-class subset of ImageNet) is not robust against adversarial examples. Extending the study further, we use our new attack to adversarially train RT defense (called AdvRT). However, the attack is still not sufficiently strong, and thus, the AdvRT model is no more robust than its RT counterpart. In the process of formulating our defense and attack, we perform several ablation studies and uncover insights that we hope will broadly benefit scientific communities studying stochastic neural networks and their robustness properties. 

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