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Waste Energy Opens New Waste-to-Energy Conversion Site and Corporate Headquarters in Midland, Texas
A novel method for detecting malware using machine learning is proposed. The approach leverages feature extraction techniques to identify malicious patterns within software code. Experimental results demonstrate that the method achieves high accuracy and outperforms existing techniques in detecting previously unseen malware variants. The proposed system offers a proactive solution for enhancing cybersecurity defenses against evolving malware threats.
