A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

Hai, Tran Hoang; Van Thieu, Vu; Duong, Tran Thai; Nguyen, Hong Hoa; Huh, Eui-Nam

doi:10.1109/access.2023.3329112

Cited by 5 publications

(1 citation statement)

References 29 publications

(34 reference statements)

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“…Hai et al's work on image-based malware detection systems [15] demonstrates an innovative approach to endpoint detection and response, showcasing the potential of using visual features for malware identification [15] process.…”

Section: Ravi Et Al's Attention-based Multidimensional Deep Learning ...mentioning

confidence: 99%

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

Mahesh T. Dhande

2023

IJRITCC

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In the rapidly evolving landscape of cyber security, the incessant advancement of malicious activities presents a formidable challenge, necessitating a paradigm shift in detection methodologies. Traditional methods, primarily reliant on static rule-based systems, exhibit palpable limitations in grappling with the dynamic and sophisticated nature of modern cyber threats. This inadequacy underscores the urgent need for innovative approaches that can adeptly adapt and respond to the ever-changing threat environment. Addressing this exigency, the present research introduces a novel hybrid machine learning model, ingeniously crafted to transcend the constraints of existing malicious activity detection frameworks. The proposed model synergizes the strengths of diverse machine learning strategies, including anomaly detection techniques including Isolation Forest and One-Class SVM, and validates the results of these classifiers using Random Forest and Gradient Boosting operations. The validated malware instances are classified into malware types using fusion of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) based Recurrent Neural Networks (RNNs) under real-time network configuration sets. This eclectic amalgamation not only leverages the unique capabilities of each algorithm but also harmonizes them to forge a more robust and precise detection mechanisms. The strategic integration of these algorithms facilitates a comprehensive analysis of network traffic and system logs, thereby significantly enhancing the detection accuracy. Furthermore, the model's adaptive learning component ensures its relevance and efficacy in the face of evolving cyber threats, a quintessential feature for contemporary cyber security solutions. Empirical evaluations, conducted using multiple malware datasets and samples, substantiate the model's superiority over existing methods. It exhibited a remarkable 10.4% improvement in precision, an 8.5% increase in accuracy, a 4.9% enhancement in recall, an 8.3% rise in AUC, a 4.5% boost in specificity, and a notable 2.5% reduction in detection delay. These compelling results underscore the model's potential in revolutionizing malicious activity detection, providing organizations with a more effective and resilient defense mechanism against a spectrum of cyber threats. The research culminates in a significant stride forward in cyber security, offering a robust, adaptive, and comprehensive solution that addresses the pressing need for advanced malicious activity detection, thereby bolstering the overall cyber security posture of organizations in the digital age sets.

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Section: Ravi Et Al's Attention-based Multidimensional Deep Learning ...mentioning

confidence: 99%

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

Mahesh T. Dhande

2023

IJRITCC

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A Weighted Discrete Wavelet Transform-Based Capsule Network for Malware Classification

Qiao,

Cao,

Zou

et al. 2024

Lecture Notes in Computer Science

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Enhanced Image-Based Malware Classification Using Snake Optimization Algorithm With Deep Convolutional Neural Network

Duraibi

2024

IEEE Access

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A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System

Abstract: This research was funded by the Vietnam Ministry of Education and Training under project number BKA-04-2022.

Cited by 5 publications

References 29 publications

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

A Weighted Discrete Wavelet Transform-Based Capsule Network for Malware Classification

Enhanced Image-Based Malware Classification Using Snake Optimization Algorithm With Deep Convolutional Neural Network

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