Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things

Ahmed, Usman; Lin, Jerry Chun‐Wei; Srivastava, Gautam; Jolfaei, Alireza

doi:10.1109/jsyst.2022.3223694

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…In the IoT sphere, Lee et al [18] presented a robust malware detection and classification system utilizing opcode category features, emphasizing the importance of feature selection in machine learning-based approaches. Ahmed et al [19] took a different angle, focusing on active learning-based defense strategies against adversary evasion attacks, a crucial aspect considering the adaptive nature of cyber threats.…”

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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TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

Misalkar,

Harshavardhanan

2025

Computer Standards & Interfaces

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Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

Khan¹,

Memon²,

Dev³

et al. 2022

Digital Communications and Networks

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Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things

Cited by 6 publications

References 26 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

TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

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