Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense

Mani, Ganapathy; Haliem, Marina; Bhargava, Bharat; Manickam, Indu; Kochpatcharin, Kevin; Kim, Myeongsu; Vugrin, Eric D.; Wang, Weichao; Jenkins, Chris; Angin, Pelin; Meng, Yonggang

doi:10.1109/tdsc.2023.3234561

Cited by 3 publications

(1 citation statement)

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“…These two approaches suffer the limitations mentioned above, namely the reliance on non-standard network technologies and increased network troubleshooting complexity. In [47] Mani et al analyze the resilience of address randomization-based MTD techniques. They show that machine learning-based can be employed to detect address randomization techniques and, in some cases, even to predict future assigned addresses.…”

Section: B Network-level Mtd Techniquesmentioning

confidence: 99%

DOLOS: A Novel Architecture for Moving Target Defense

Pagnotta,

De Gaspari,

Hitaj

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asymmetric disadvantage for the attacker by using deception and randomization techniques to create a dynamic attack surface. Moving Target Defense (MTD) typically relies on system randomization and diversification, while Cyber Deception is based on decoy nodes and fake systems to deceive attackers. However, current Moving Target Defense techniques are complex to manage and can introduce high overheads, while Cyber Deception nodes are easily recognized and avoided by adversaries.This paper presents DOLOS, a novel architecture that unifies Cyber Deception and Moving Target Defense approaches. DOLOS is motivated by the insight that deceptive techniques are much more powerful when integrated into production systems rather than deployed alongside them. DOLOS combines typical Moving Target Defense techniques, such as randomization, diversity, and redundancy, with cyber deception and seamlessly integrates them into production systems through multiple layers of isolation. We extensively evaluate DOLOS against a wide range of attackers, ranging from automated malware to professional penetration testers, and show that DOLOS is effective in slowing down attacks and protecting the integrity of production systems. We also provide valuable insights and considerations for the future development of MTD techniques based on our findings.

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Section: B Network-level Mtd Techniquesmentioning

confidence: 99%