AI-Based Binary Deep Learning Models for Real-Time Malware Analysis and Classification
Keywords:
real-time malware detection, binary deep learning, AI in cybersecurity, machine learningAbstract
The previous decade has seen malware grow increasingly complicated and harder to identify using signatures. Real-time malware analysis requires advanced detection algorithms. Deep learning can analyze raw binary data and find complex patterns that suggest dangerous behavior, making AI-based binary deep learning models effective. AI-driven binary deep learning models for real-time malware analysis and classification are described in this work, including their architecture, training, and capacity to discover new threats. We address model implementation concerns such data imbalances and processing demands and provide solutions to improve detection accuracy and system efficiency. Successful applications of these models show their cybersecurity potential.
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