A novel approach for detection of APT malware using multi-dimensional hybrid Bayesian belief network

Sharma, Amit Kumar; Gupta, Brij B.; Singh, Awadhesh Kumar; Saraswat, V. K.

doi:10.1007/s10207-022-00631-5

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…The accuracy comparison, as shown in Figure 2 between the proposed UA-DES-DBN model and the related models (VGG16-PSO [48], DBN-IDS [49], and DBN [50]) across various input layer sizes reveals that the UA-DES-DBN model consistently outperforms the other models, at almost all input sizes. The results show that as the input layer size increases from 5 to 20, the UA-DES-DBN model's accuracy improves significantly, reaching a peak accuracy of 0.986 at an input size of 20.…”

Section: Resultsmentioning

confidence: 96%

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

Gazzan,

Sheldon

2024

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Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call “UA-DES-DBN.” The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications.

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Section: Resultsmentioning

confidence: 96%

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

Gazzan,

Sheldon

2024

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“…Deep learning models may struggle to adapt to rapidly changing environments, leading to potential obsolescence [129][130][131][132][133][134][135][136]. Developing dynamic models capable of continuous learning from new data and adapting to evolving threat landscapes is a solution to this problem.…”

Section: Open Challengesmentioning

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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“…Use of malicious codes (malware) to log into a network or system without authorization. (Sharma et al, 2023;Wang et al, 2022) Network exploitation…”

Section: Malware Distributionmentioning

confidence: 99%

A System Dynamics Approach to Evaluate Advanced Persistent Threat Vectors

Nicho¹,

McDermott

Fakhry

et al. 2023

International Journal of Information Security and Privacy

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Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage by mitigating specific socio-technical cues. This study provides valuable insights into the dynamics of cyber threats, making it the first to apply SD to APTs.

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A novel approach for detection of APT malware using multi-dimensional hybrid Bayesian belief network

Cited by 12 publications

References 36 publications

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

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

A System Dynamics Approach to Evaluate Advanced Persistent Threat Vectors

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