A method for malware propagation in industrial critical infrastructures

Rey, Ángel Martín del; Encinas, Ascensión Hernández; Vaquero, Juan Pablo Aparicio; Dios, Araceli Queiruga; Sánchez, Gerardo Rodríguez

doi:10.3233/ica-160518

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The infection probability is the same for all nodes in the same class. An individual based malware propagation method for industrial critical infrastructure is introduced in [129]. The model also considers characteristics that are critical for IoT malware considerations such as; 1) Operating system coefficient o[i] -which is set to 1 if the interacting agents have the same operating system otherwise set to zero.…”

Section:  Individual Based Modelsmentioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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Section:  Individual Based Modelsmentioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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“…To address the challenge of CPS security, researchers have paid a great attention on the security issues of malwares propagation, such as propagation modeling, prevention, detection, and mitigation [ 20 , 21 , 22 ]. However, the unprotected exposure is usually neglected and few of them have considered the trade-off between safety and security when using light-weight countermeasures.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Defense against Stealth Malware Propagation in Cyber-Physical Systems: A Game-Theoretical Framework

Xiao

Zhu

Xie

et al. 2020

Entropy

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Stealth malware is a representative tool of advanced persistent threat (APT) attacks, which poses an increased threat to cyber-physical systems (CPS) today. Due to the use of stealthy and evasive techniques, stealth malwares usually render conventional heavy-weight countermeasures inapplicable. Light-weight countermeasures, on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures, which normally is a challenging task. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game with both static version (SSPTI) and multi-stage dynamic version (DSPTI), and safety requirements of CPS are introduced as constraints in the defender’s decision model. The attacker aims to stealthily penetrate the CPS at the lowest cost (e.g., time, effort) by selecting optimal network links to spread, while the defender aims to retard the malware epidemic as much as possible. Both games are modeled as bi-level integer programs and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg equilibrium of SSPTI, and design a Model Predictive Control strategy to solve DSPTI approximately by sequentially solving an 1+δ approximation of SSPTI. Extensive experiments have been conducted by comparing proposed algorithms and strategies with existing ones on both static and dynamic performance metrics. The evaluation results demonstrate the efficiency of proposed algorithms and strategies on both simulated and real-case-based CPS networks. Furthermore, the proposed dynamic defense framework shows its advantage of achieving a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

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