Artificial registration of network stress to self-Monitor an autonomic computing system

Idio, Isong; Rufus, Rahmira; Esterline, Albert

doi:10.1109/secon.2017.7925334

Cited by 1 publication

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…I Singh et al [18] I Li and Li [1] I Sejdić et al [5] I Mohammadi et al [24] 6. Resilience and Robustness I Idio et al [31] I Hahsler et al [3] I Breiman [32] 7. Survey and Overview of Cyber-physical Systems I Chen and Bahsoon [17] I Subashini and Kavitha [4] I Mahdavinejad et al [8] I Samuel and Madria [7] I Zhang et al [6] 2.…”

Section: Fault Diagnosis and Detectionmentioning

confidence: 99%

“…Agent architecture is described in [31] as a rule-based architecture that uses an "ifthen" rationalising scheme, in which the consequent result relies on prior experience. Then, appropriate agent architecture influences how well the system's agents handle their operating environment through inference reasoning.…”

Section: Agent Architecturementioning

confidence: 99%

“…Then, appropriate agent architecture influences how well the system's agents handle their operating environment through inference reasoning. The scope for simultaneous internal and external monitoring of the system is limited due to what is referred to as a threading obstacle with JADE/JESS integration, as noted in Cardoso [31]. The alternative and viable option is to structure agent communication from JADE agents to the JESS inference by granting JESS access privileges to the agent communication language (ACL) message or allowing JESS to add ACL message objects to the JESS working memory.…”

Section: Agent Architecturementioning

confidence: 99%

“…This process is like how the biological immune system detects foreign agents such as viruses or bacteria. According to Rufus and Esterline, AIS mimics how biological mechanisms fight unknown threats, like how an organism resists a new virus and mimics the process in a computer system [31]. The effector component of the AIS then takes action to correct the anomaly.…”

Section: Artificial Immune Systemmentioning

confidence: 99%

“…Once an anomaly is detected, the system can mitigate the threat by blocking the offending traffic or alerting security personnel. The functionality is explained in Arrington in [31], where BMIDS is shown to use a computing process to detect intrusion within a smart house environment. A three-stage method for connecting real-world scripted events schemes called behavioural script event scheme (BSES) was presented.…”

Section: Behavioural Modelling Intrusion Detection Systemmentioning

confidence: 99%

See 4 more Smart Citations

Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools

Johnphill,

Sadiq,

Al-Obeidat

et al. 2023

Future Internet

View full text Add to dashboard Cite

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems.

show abstract