Detection and Threat Prioritization of Pivoting Attacks in Large Networks

Pierazzi, Fabio; Colajanni, Michele; Marchetti, Mirco

doi:10.1109/tetc.2017.2764885

Cited by 22 publications

(35 citation statements)

References 29 publications

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“…We put more emphasis on small increments because not only they are easier to introduce, but they also yield samples that are more similar to their "original" variant (which is a typical characteristic of adversarial perturbations [9]). Furthermore, increasing some of these features by higher amounts may trigger external defensive mechanisms based on anomaly detection [59], or may generate incorrect flows (e.g.,: some flow collectors [70] have flow upper duration limits of 120 s [71]). The complete breakdown of the operations performed to generate our adversarial datasets is provided in Algorithm 1, in which an adversarially manipulated input is denoted through the A(•) operator.…”

Section: Generation Of Adversarial Samplesmentioning

confidence: 99%

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors

et al. 2020

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Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. We address this problem by presenting AppCon, an original approach to harden intrusion detectors against adversarial evasion attacks. Our proposal leverages the integration of ensemble learning to realistic network environments, by combining layers of detectors devoted to monitor the behavior of the applications employed by the organization. Our proposal is validated through extensive experiments performed in heterogeneous network settings simulating botnet detection scenarios, and consider detectors based on distinct machine- and deep-learning algorithms. The results demonstrate the effectiveness of AppCon in mitigating the dangerous threat of adversarial attacks in over 75% of the considered evasion attempts, while not being affected by the limitations of existing countermeasures, such as performance degradation in non-adversarial settings. For these reasons, our proposal represents a valuable contribution to the development of more secure cyber defense platforms.

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Section: Generation Of Adversarial Samplesmentioning

confidence: 99%

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors

et al. 2020

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“…Furthermore, when simulating attacks at the feature-space it is also needed to check that all inter-dependencies between features are maintained, and that the feature values of the perturbed samples do not result in impossible numbers: for example, the size of a TCP packet cannot exceed 64KBytes, while some network flow collectors have fixed thresholds for the maximum flow duration [14,71].…”

Section: Realistic Evaluation Of Existing Attacksmentioning

confidence: 99%

Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

et al. 2022

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The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning methods, we identify and model the real capabilities and circumstances that are necessary for an attacker to carry out a feasible and successful adversarial attack. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.

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“…A common technique used by APT to overcome connectivity restrictions imposed by firewalls or to access different network segments is the Pivot attack. Apruzzese et al [28] described the first flow-based pivoting detection algorithm, which uses temporal graph-analytics techniques to detect the attacks and prioritise detection results based on a scoring system. The same authors defined the pivot attack as a command propagation tunnel created among three or more internal hosts to control a specific target.…”

Section: Pivotingmentioning

confidence: 99%

“…However, it is essential that we compare our detection scheme with prior research effort in this area. Table II To the best of our knowledge, [28] is the first paper that specifically addresses pivoting by introducing an attack detection method. However, authors in [30] stated that the approach is not feasible to detect pivot attacks in enterprise networks when considering internal and external connections.…”

Section: E Related Workmentioning

confidence: 99%