Towards Self-Adaptive Cyber Deception for Defense

Landsborough, Jason; Carpenter, Luke; Coronado, Braulio; Fugate, Sunny; Ferguson-Walter, Kimberly; Bruggen, Dirk Van

doi:10.24251/hicss.2021.244

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…We down selected articles to a final sample of 23 (Table 2) in total or 16 unique articles. Notably, four articles [46,53,34,47] appear in multiple categories. Overall, the inclusion criteria consisted of a publication date in the range of 2018 to 2022, available PDF or text of the paper downloadable, and a demonstrated implementation of associated keywords.…”

Section: Methodsmentioning

confidence: 99%

“…In fact, adaptive honeypots and honeynets are the forward edge of state-of-the-art. Such adaptive systems leverage key aspects of dynamic, intelligent, and learning to instantiate responses to environment and behavioral stimuli [32,33,34]. These honeypots and honeynets have an expanded repertoire of behavioral and architectural changes as a result.…”

Section: Adaptivementioning

confidence: 99%

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A green scheduling algorithm for cloud-based honeynets

Pittman¹,

Alaee²

2023

Front. Sustain.

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Modern businesses leverage cloud architecture to achieve agile and cost-effective technology services. Doing so comes at the expense of the environment though cloud technologies consume large quantities of energy. Cloud energy consumption is concerning in light of global climate trends and dwindling fossil fuel reserves. Consequently, increasing attention is given to sustainable and green cloud computing, which seeks to optimize compute-resource allocation and usage of virtualized systems and services. At the same time, progress toward sustainable and green cloud technology is impeded because as more enterprises deploy services into cloud architecture, cybersecurity threats follow. Unfortunately, cybersecurity technologies are optimized for maximum service overwatch without regard for compute resources and energy. This negates the energy reduction achieved in recent sustainable technology advancements. In this work, a generalized cybersecurity honeynet scheduling algorithm is proposed, in which power, CPU, and network overhead are operationalized to increase sustainability while balancing defensive mechanisms. The work describes both the mathematical foundation for the algorithm and a pseudocode proof of concept.

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Section: Methodsmentioning

confidence: 99%

Section: Adaptivementioning

confidence: 99%

A green scheduling algorithm for cloud-based honeynets

Pittman¹,

Alaee²

2023

Front. Sustain.

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“…By merging autonomic computing and cyber Deception, we can obtain an early defender advantage and counter attacker behaviors through automatic adaptation. Article [ 8 ] proposes implementing the adaptive deception framework, which involves a tiny network consisting of two Windows 7 client computers and a database server. One hundred runs were performed for four different scenarios where the attacker tried to access this network.…”

Section: Ci-enabled Techniques Used In Defensive Deceptionmentioning

confidence: 99%

“…The average successful run time was approx. 256.64 s [ 8 ]. This article showed how the autonomous deception framework increased the attacker runtime by 175% and reduced the successful runs by 89%, resulting in an optimal defense strategy.…”

Section: Ci-enabled Techniques Used In Defensive Deceptionmentioning

confidence: 99%

“…Both insider and outsider attacks can be prevented using Deception. These days machine learning has emerged as an effective technology that provides us with a wide range of applications ranging from recognition of patterns, image identification, image, and video processing, making predictions, virus or malware detection, autonomous driving, and other application scenarios [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The advantages of machine learning algorithms can be extended for deploying Defensive Deception frameworks [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , …”

Section: Introductionmentioning

confidence: 99%

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Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Mohan

Dixit

Gyaneshwar

et al. 2022

Sensors

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With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.

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Deception Technology Based Intrusion Protection and Detection Mechanism for Digital Substations: A Game Theoretical Approach

Jay¹

2023

IEEE Access

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Securing substations from cyber attacks is essential to safeguard critical power infrastructure. However, digital substations that are based on the IEC-61850 standard have Generic Object Oriented Substation Events (GOOSE) messages and Sampled Value (SV) messages that are time-critical and thus cannot be protected using encryption techniques. This work presents a study on deception technology (decoys) for mitigating cyber attacks on GOOSE message virtual LAN (VLAN) which is a non-observable strongly connected biography. In this paper, the deployment of defender decoys is proposed by defining observable subgraphs in the VLAN. The defender-attacker interaction is modeled as a single-leader singlefollower game with the defender as the leader. The optimal allocation of decoys for asset protection and attack detection is then formulated as a bi-level optimisation problem. Simultaneous allocation and sequential allocation of protection and detection decoys are considered for defender resource allocation. The existence of equilibrium of the defender-attacker game is proven. The model is illustrated in a 3-IED VLAN and performance is evaluated in a 12-IED VLAN system in the PSRC-I5 protection relay report. The results are compared with the zero-sum game model and it is found that the proposed model is capable of mitigating attacks in the GOOSE VLAN INDEX TERMS Deception technology, IEC-61850 standard, intrusion protection mechanism, intrusion detection mechanism, single-leader single-follower game. NOMENCLATURE VARIABLESDEVIKA JAY (Member, IEEE) received the B.Tech. degree in electrical and electronics engineering from the TKM College of Engineering, Kerala, India, in 2009, and the M.S. degree in power systems and the Ph.D. degree from the Indian Institute of Technology Madras, in 2013 and 2021, respectively. She was the Deputy Manager with Power Grid Corporation of India Ltd. She is currently the Founder and the CTO with Gridsentry Private Ltd. Her research interests include powergrid cybersecurity, power system economics, electricity markets, the application of game theory, and artificial intelligence in power system operation.

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Towards Self-Adaptive Cyber Deception for Defense

Cited by 7 publications

References 21 publications

A green scheduling algorithm for cloud-based honeynets

A green scheduling algorithm for cloud-based honeynets

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions

Deception Technology Based Intrusion Protection and Detection Mechanism for Digital Substations: A Game Theoretical Approach

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