In the face of escalating cyber threats posed by malware, advanced detection techniques are crucial. This study introduces a cutting-edge approach that merges convolutional neural networks (CNNs) and long short-term memory recurrent neural networks (LSTMs) for enhanced malware classification. The effectiveness of this method is rigorously examined using Microsoft's BIG Cup 2015 dataset. By combining CNN's ability to capture local features and LSTM's proficiency in processing sequence data, our approach achieves remarkable accuracy (98.73%) in identifying malicious behaviors. This research contributes an extensive exploration of deep learning models, an innovative CNN-LSTM hybrid architecture, and a comprehensive case study showcasing its superior performance. The presented approach signifies a significant stride in bolstering cybersecurity against the ever-evolving threat of malware.
Recent Advancements in Multimedia Data Processing and Security
Top1. Introduction
In the rapidly evolving digital landscape, the proliferation of malicious software, commonly called malware, has emerged as a pervasive cybersecurity challenge. (Sadqi & Maleh, 2022) With the potential to inflict a broad spectrum of threats, from data breaches to covert surveillance, the need for accurate and robust malware classification methods has grown more critical than ever. Traditional approaches often fall short in the face of the escalating sophistication of malware variants, driving researchers to explore cutting-edge technologies to fortify cybersecurity defenses (Maleh, 2019).
In this context, the convergence of deep learning techniques presents a promising avenue for enhancing the accuracy and resilience of malware classification systems. By harnessing the power of neural networks, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory Recurrent Neural Networks (LSTMs), a novel paradigm of “Deep Learning Fusion” emerges. This amalgamation capitalizes on the strengths of both CNNs in capturing local features and LSTMs in modeling temporal dependencies, thus enabling the creation of a potent classifier capable of discerning intricate patterns within malicious code. Furthermore, the democratization of malware development has diminished the skill barrier, owing to the widespread availability of attack tools on the Internet (Maleh et al., 2021). The proliferation of anti-detection techniques and the accessibility of black-market malware has made it feasible for virtually anyone to become an attacker, regardless of technical expertise. The landscape is witnessing increased attacks initiated by script kiddies or automated agents (Aliyev, 2010).
In light of these developments, safeguarding computer systems against malware has become a paramount cybersecurity imperative for individuals and businesses. A single breach can compromise critical data and trigger substantial losses. The prevalence of attacks and their profound repercussions underscore the urgency of precise and prompt detection strategies. Established static and dynamic approaches often fall short, especially in the realm of zero-day attacks, prompting an exploration of machine-learning techniques (Chumachenko & Technology, 2017).
When classifying families of malicious code, the process hinges on identifying unique attributes while aptly selecting classification algorithms for accurate outcomes. Remarkably, the arena of deep neural networks (DNNs) has emerged as a focal point for classification and recognition methodologies. The extension to deep neural network models, achieved by augmenting the hidden layer depths of neural networks, has yielded exceptional performance gains in areas like image and speech recognition. This trend has found its way into malicious code analysis, although incorporating DNN-based models in this field remains relatively limited, particularly concerning malware and intrusion detection (M. & Sethuraman, 2023).
This chapter explores the intricate domain of malware classification, presenting a pioneering approach that unites Convolutional Neural Networks (CNNs) with Long Short-Term Memory Recurrent Neural Networks (LSTMs). The effectiveness of this novel approach is rigorously evaluated using data sourced from Microsoft's BIG Cup 2015 dataset. The main contributions of this chapter encompass the following:
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An in-depth exploration of diverse deep learning models tailored to address the complex challenge of malware classification.
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The introduction of an innovative deep neural network model fusing CNN and LSTM layers to discern and classify malicious behaviors.
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A detailed case study involving the Microsoft Malware Dataset, showcases the exceptional detection accuracy (98.73%) achieved by our proposed model compared to some related works.