To Detect Malware attacks for an Autonomic Self-Heal Approach of Virtual Machines in Cloud Computing

Joseph, Linda; Mukesh, Rajeswari

doi:10.1109/iconstem.2019.8918909

Cited by 4 publications

(9 citation statements)

References 24 publications

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“…SVM can be trained on historical data to identify patterns and predict future behaviour. SVM was described by [39] as a set of supervised prediction-learning methods that are used for classification and regression. The technique uses machine-learning theory to maximise the predicting accuracy of an anomaly on cyber-physical systems.…”

Section: Support Vector Machinementioning

confidence: 99%

“…The algorithm supports empirical performance and the structure risk minimisation (SRM) principle. SRM is argued by [39] to be superior to the traditional empirical risk minimisation (ERM) principle. SVM uses statistical-learning theories to study the problems of knowledge gain, predictions, and decision-making for a given dataset.…”

Section: Support Vector Machinementioning

confidence: 99%

“…The naïve Bayes machine-learning algorithm is described by [39] as a classification algorithm based on Bayes theorems. It is also described in Rani in [28] as a classifier that applies the Bayes theorem to solve various classification problems such as detecting systems vulnerability, disease diagnosis, spam prevention, document filtering, etcetera.…”

Section: Naïve Bayesmentioning

confidence: 99%

“…The naïve Bayes algorithm applies to self-healing systems functionality because it applies to classifying datasets into models that can aid the detection of system vulnerability, providing the first step towards realising a self-healing functionality. The algorithm utilises conditional, and class probability, and the class probability can be the probability of a random dataset belonging to a particular class, which is calculated as: The conditional probability, on the other hand, as [39] noted, is the probability of a feature value of a class and is calculated as:…”

Section: Naïve Bayesmentioning

confidence: 99%

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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

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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.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

Section: Naïve Bayesmentioning

confidence: 99%

Section: Naïve Bayesmentioning

confidence: 99%

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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

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“…ML-based methods have been proposed to fight against co-resident attacks focusing on different factors, such as minimizing the time of a malicious VM co-location. Joseph et al [36] used traditional ML algorithms, such as support vector machine (SVM), naïve bayes, and random forests to detect malware, following a self-healing methodology to power off the attacked VMs and restore them to healthy conditions. In reality, there is a concern with the amount of time it takes to implement a solution to mitigate VMs in the event of co-resident attacks.…”

Section: Related Work: Secure Cloud Resource Managementmentioning

confidence: 99%

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Cui¹,

Homsi²

2022

Artificial Intelligence Annual Volume 2022

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Cloud computing brings convenience and cost efficiency to users, but multiplexing virtual machines (VMs) on a single physical machine (PM) results in various cybersecurity risks. For example, a co-resident attack could occur when malicious VMs use shared resources on the hosting PM to control or gain unauthorized access to other benign VMs. Most task schedulers do not contribute to both resource management and risk control. This article studies how to minimize the co-resident risks while optimizing the VM completion time through designing efficient VM allocation policies. A zero-trust threat model is defined with a set of co-resident risk mitigation parameters to support this argument and assume that all VMs are malicious. In order to reduce the chances of co-residency, deep reinforcement learning (DRL) is adopted to decide the VM allocation strategy. An effective cost function is developed to guide the reinforcement learning (RL) policy training. Compared with other traditional scheduling paradigms, the proposed system achieves plausible mitigation of co-resident attacks with a relatively small VM slowdown ratio.

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Self-Adaptive Secure Mechanism for Containers

G¹,

Karthik²,

Heremath³

et al. 2022

2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)

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To Detect Malware attacks for an Autonomic Self-Heal Approach of Virtual Machines in Cloud Computing

Cited by 4 publications

References 24 publications

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

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

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Self-Adaptive Secure Mechanism for Containers

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