Security modelling and formal verification of survivability properties: Application to cyber–physical systems

Bernardi, Simona; Gentile, Ugo; Marrone, S.; Merseguer, José; Nardone, Roberto

doi:10.1016/j.jss.2020.110746

Cited by 22 publications

(18 citation statements)

References 57 publications

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“…Formal methods are mostly used to verify whether a software/hardware system meets certain requirements, 35 and have been widely adopted in various domains 36–41 . Wang et al 41 applied a formal method to verify the output bound of DNNs.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic incentive mechanism design for regulation‐aware systems

Dang

Cao

et al. 2021

Int J of Intelligent Sys

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As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride‐hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance behaviors, such as detecting fatigue driving by computer vision or pattern recognition methods. However, few of them indicate how to efficiently exploit the detection results to regulate workers' behaviors. In this paper, we point out that workers' noncompliance behaviors and their incomes should be correlated, and propose a quantifiable computation framework that includes a price‐based incentive mechanism and a method to verify the effectiveness of the mechanism. Historical behaviors of workers are summarized as credits and stored in nonfungible token called CreditToken to ensure that it cannot be tampered with. CreditToken will further affect workers' incomes. We abstract the decision‐making behavior of workers as a Markov decision process and demonstrate the effectiveness of the incentive mechanism with model checking and formal methods. The analysis shows that our framework is able to provide a rational price strategy formation for gig service platforms, and can be flexibly integrated into existing pricing schemes to maximize the value of the detection results. Extensive experiments illustrate the advanced nature and practicality of our framework.

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Section: Related Workmentioning

confidence: 99%

“…Koley et al 38 proposed a formal approach for attack detectors in CPS. Formal methods are also used to verify the survivability properties in CPS 36 . We first use the formal method to verify the effectiveness of incentive mechanism in the proposed framework.…”

Section: Related Workmentioning

confidence: 99%

Dynamic incentive mechanism design for regulation‐aware systems

Dang

Cao

et al. 2021

Int J of Intelligent Sys

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show abstract

“…In further research, Semerci et al [18] introduce an intelligentbased system for detecting an attack; however, it only focusses on specific types of attacks: Distributed Denial of Services (DDOS). Different points of view in defending a system are provided in [19][20]. This research explains how the attack occurs, which illustrates a defending model.…”

Section: Related Workmentioning

confidence: 99%

Data Reduction for Optimizing Feature Selection in Modeling Intrusion Detection System

Iman¹,

Ahmad²

2020

IJIES

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With the development and ease of access to internet networks, the potential for attacks and intrusions have increased. The intrusion detection system (IDS), an approach to overcome this problem, is grouped into two models: signature-based and anomaly-based. An anomaly-based IDS can be implemented by machine learning; one of the schemes in machine learning is data reduction. IDS datasets are usually obtained through a real-time process that has undefined proportional data. The purpose of data reduction is to speed up and optimize the process, improving accuracy, precision, and specifications. There are several methods to perform data reduction, one of which uses outlier detection techniques. Proper outlier detection has a positive impact on improving the classification results of machine learning. In this research, the outlier detection is done by a circle generated from the k -means clustering of all selected features. Two scenarios are designed for the evaluation: a circle generated from two points of the minimum and maximum cluster and median of all clusters. The formation of clusters conducted by k -means clustering determines the size and direction of the outlier circle so that it dynamically adjusts the distribution of data from the feature selection results. By employing the previous feature selection algorithms, the comparison is performed to evaluate the proposed method's performance. Our empirical results show that the second scenario can significantly improve the classification results in terms of accuracy, detection rate, and precision. The first and second experiments can increase the accuracy by 0.02%, and the third experiment is by 0.1%. The detection rate in the first, second, and third experiments increases by 0.01%, 0.02%, and 0.07. At the same time, precision increases by 0.04%, 0.02%, and 0.01%, correspondingly.

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“…Besides guaranteeing the correctness of the system behavior, there exist important challenges, such as performance, reliability, and security, which must be tackled, especially in an industrial context. For instance, trustworthiness, intended as the capacity of CPS to deal with disruption while guaranteeing a temporary impact on provided services, has been recently outlined in the literature as one of the major challenges [8], [9], mainly due to these difficulties: (i) failures can occur in various unexpected ways due to uncertainties in the operational environment; (ii) adversarial threats (e.g., the opportunistic behavior of agents [10]) are likely to take place in practice [11], and it is not trivial to plan which countermeasures need to be applied for system recovery.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Fault-Tolerant Multiagent Coordination Mechanisms

Pinciroli

Trubiani

2023

IEEE Trans. Ind. Inf.

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Performance evaluation of multi-agent systems (MAS) embraces several challenges due to uncertain operational environments, such as software/hardware failures and unfaithful communications that facilitate the spread of deceptive messages. One way to smooth the impact of reliability and security potential issues in MAS is to enforce different coordination mechanisms among agents (i.e., coordinating multiple agents that need to perform a sequence of actions to maximize a system-level reward), and evaluate their efficiency. In this paper, we propose a tool-based approach, namely COORDINATE, that simulates and compares the performance characteristics of different coordination mechanisms for MAS while considering faulttolerant and corrupted agents. A Smart Hospital is adopted as illustrative example to show the need of performancebased analysis results pointing out which coordination mechanism is more efficient. Experimental results indicate that the inter-arrival time of tasks to be accomplished, the failure probability of agents, and the ratio of faithful to malicious agents contribute to determining the efficiency of different coordination mechanisms. When varying these parameters in the considered scenarios, the system latency can be reduced up to 4.2× by selecting the optimal coordination mechanism. This way, software developers are informed on the system peculiarities that trigger the switching among such coordination mechanisms for a performance-based optimal solution.

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Security modelling and formal verification of survivability properties: Application to cyber–physical systems

Cited by 22 publications

References 57 publications

Dynamic incentive mechanism design for regulation‐aware systems

Dynamic incentive mechanism design for regulation‐aware systems

Data Reduction for Optimizing Feature Selection in Modeling Intrusion Detection System

Performance Analysis of Fault-Tolerant Multiagent Coordination Mechanisms

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