Deception as a service: Intrusion and Ransomware Detection System for Cloud Computing (IRDS4C)

El-Kosairy, Ahmed; Abdelbaki, Nashwa

doi:10.1007/s43674-023-00056-0

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…These tools are designed to create a deceptive environment within a network with the aim of misleading, confusing, and ultimately trapping malicious actors and malware. They employ various strategies and technologies to achieve this goal [64][65][66]. A common technique used in deception tools is the deployment of honeypots and honeynets, which essentially act as decoy systems created to mimic real assets within a network.…”

Section: Deception Toolsmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

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Section: Deception Toolsmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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RAKSHAM: Responsive approach to Knock‐off scavenging hackers and attack mitigation

Bhardwaj,

Dave

2023

Trans Emerging Tel Tech

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With the increasing interconnectivity of devices and systems in the Internet of Things (IoT) landscape, the importance of cybersecurity has grown significantly. This work presents a comprehensive solution that combines deceptive techniques and AI‐based detection to strengthen the security of IoT networks against sophisticated cyber threats. Deceptive techniques, such as the utilization of honeypots, deceptive IoT devices, and deceptive network traffic, are deliberately employed with the aim of misleading potential attackers. However, the effectiveness of these systems is often challenged by highly skilled attackers and the complexities associated with allocating resources. To address these challenges, “RAKSHAM” is a unique approach to improving the security of IoT networks. It combines deceptive techniques with AI‐based detection mechanisms, minimizing false positives and operational outages in IoT networks. This approach utilizes Eigenvector Centrality (EVC) to identify nodes with a higher risk level. It also evaluates the performance of an AI‐based attack detection model. The AI model, which utilizes a Convolutional Neural Network (CNN), demonstrates exceptional efficacy in the detection of attacks with 99% accuracy and a minimal attack success probability rate of 0.10. RAKSHAM offers a pragmatic and efficient solution for securing IoT networks by accurately detecting attacks and minimizing false positives and operational outages.

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Navigating the Cyber Landscape: A Framework for Transitioning from Business Continuity to Digital Resilience

Zighan

2024

2024 2nd International Conference on Cyber Resilience (ICCR)

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Deception as a service: Intrusion and Ransomware Detection System for Cloud Computing (IRDS4C)

Cited by 3 publications

References 28 publications

Deep learning-powered malware detection in cyberspace: a contemporary review

Deep learning-powered malware detection in cyberspace: a contemporary review

RAKSHAM: Responsive approach to Knock‐off scavenging hackers and attack mitigation

Navigating the Cyber Landscape: A Framework for Transitioning from Business Continuity to Digital Resilience

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