1,982 research outputs found

    Detection Of Insider Attacks In Block Chain Network Using The Trusted Two Way Intrusion Detection System

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    For data privacy, system reliability, and security, Blockchain technologies have become more popular in recent years. Despite its usefulness, the blockchain is vulnerable to cyber assaults; for example, in January 2019 a 51% attack on Ethereum Classic successfully exposed flaws in the platform's security. From a statistical point of view, attacks represent a highly unusual occurrence that deviates significantly from the norm. Blockchain attack detection may benefit from Deep Learning, a field of study whose aim is to discover insights, patterns, and anomalies within massive data repositories. In this work, we define an trusted two way intrusion detection system based on a Hierarchical weighed fuzzy algorithm and self-organized stacked network (SOSN) deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Here initially the smart contract handles the node authentication. The purpose of authenticating the node is to ensure that only specific nodes can submit and retrieve the information. We implement Hierarchical weighed fuzzy algorithm to evaluate the trust ability of the transaction nodes. Then the transaction verification step ensures that all malicious transactions or activities on the submitted transaction by self-organized stacked network deep learning model. The whole experimentation was carried out under matlab environment. Extensive experimental results confirm that our suggested detection method has better performance over important indicators such as Precision, Recall, F-Score, overhead

    A multi-dimension taxonomy of insider threats in cloud computing

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    Security is considered a significant deficiency in cloud computing, and insider threats problem exacerbate security concerns in the cloud. In addition to that, cloud computing is very complex by itself, because it encompasses numerous technologies and concepts. Apparently, overcoming these challenges requires substantial efforts from information security researchers to develop powerful mitigation solutions for this emerging problem. This entails developing a taxonomy of insider threats in cloud environments encompassing all potential abnormal activities in the cloud, and can be useful for conducting security assessment. This paper describes the first phase of an ongoing research to develop a framework for mitigating insider threats in cloud computing environments. Primarily, it presents a multidimensional taxonomy of insider threats in cloud computing, and demonstrates its viability. The taxonomy provides a fundamental understanding for this complicated problem by identifying five dimensions, it also supports security engineers in identifying hidden paths, thus determining proper countermeasures, and presents a guidance covers all bounders of insiders threats issue in clouds, hence it facilitates researchers’ endeavours in tackling this problem. For instance, according to the hierarchical taxonomy, clearly many significant issues exist in public cloud, while conventional insider mitigation solutions can be used in private clouds. Finally, the taxonomy assists in identifying future research directions in this emerging area

    Autonomic computing meets SCADA security

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    © 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

    Security Challenges from Abuse of Cloud Service Threat

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    Cloud computing is an ever-growing technology that leverages dynamic and versatile provision of computational resources and services. In spite of countless benefits that cloud service has to offer, there is always a security concern for new threats and risks. The paper provides a useful introduction to the rising security issues of Abuse of cloud service threat, which has no standard security measures to mitigate its risks and vulnerabilities. The threat can result an unbearable system gridlock and can make cloud services unavailable or even complete shutdown. The study has identified the potential challenges, as BotNet, BotCloud, Shared Technology Vulnerability and Malicious Insiders, from Abuse of cloud service threat. It has further described the attacking methods, impacts and the reasons due to the identified challenges. The study has evaluated the current available solutions and proposed mitigating security controls for the security risks and challenges from Abuse of cloud services threat

    Autonomic computing architecture for SCADA cyber security

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    Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator
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