DOMR: Toward Deep Open-World Malware Recognition

Lu, Tingting; Wang, Junfeng

doi:10.1109/tifs.2023.3338469

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…In the realm of Android malware, Zhang et al [12] utilized a deep forest and feature enhancement approach for detection, highlighting the effectiveness of ensemble learning methods in this context [12]. Lu and Wang's exploration of deep openworld malware recognition [13] and Kim et al's automated zero-day malware detection system [14] both contribute to the ongoing effort to detect previously unknown malware types, a critical aspect of cyber security sets.…”

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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“…Initially, signature matching dominated the industry's approach to malware identification [1], [2], [3], boasting high accuracy for known malware but falling short against unknown threats. Subsequently, heuristic-based methods emerged, defining static or dynamic rules to identify malware based on its characteristics [4], [5], [6]. While capable of detecting some unknown malicious codes, these methods suffer from a high false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

Yuanming,

Latih

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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With the continuous development of technology, the types of malware and their variants continue to increase, which has become an enormous challenge to network security. These malware use a variety of technical means to deceive or evade traditional detection methods, making traditional signature-based rule-based malware identification methods no longer applicable. Many machine algorithms have attracted widespread academic attention as powerful malware detection and classification methods in recent years. After an in-depth study of rich literature and a comprehensive survey of the latest scientific research results, feature extraction is used as the basis for classification. By extracting meaningful features from malware samples, such as behavioral patterns, code structures, and file attributes, researchers can discern unique characteristics that distinguish malicious software from benign ones. This process is the foundation for developing effective detection models and understanding the underlying mechanisms of malware behavior. We divide feature engineering and learning-based methods into two categories for investigation. Feature engineering involves selecting and extracting relevant features from raw data, while learning-based methods leverage machine learning algorithms to analyze and classify malware based on these features. Supervised, unsupervised, and deep learning techniques have shown promise in accurately detecting and classifying malware, even in the face of evolving threats. On this basis, we further look into the current problems and challenges malware identification research faces.

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Enhancing Android Malware Detection Through Machine Learning: Insights From Permission and Metadata Analysis

et al. 2024

2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)

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DOMR: Toward Deep Open-World Malware Recognition

Cited by 3 publications

References 55 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 Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

Enhancing Android Malware Detection Through Machine Learning: Insights From Permission and Metadata Analysis

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