A Proposed MalwareNexus Framework for Cybersecurity Using File Metadata Analysis and Machine Learning for Malware Detection

A Proposed MalwareNexus Framework for Cybersecurity Using File Metadata Analysis and Machine Learning for Malware Detection

연구 분야: Safety

학회: 2024 3rd International Conference for Advancement in Technology (ICONAT)

In today’s world, technology is ever-advancing and always around us, and with that, our digital lives are also expanding. But with this increase has come a worrying surge in digital crime—specifically, cybersecurity attacks. For the last decade, we’ve seen a disturbing escalation in these attacks, starting from the first-ever malware created in the ‘70s (called, ominously, the Creeper) to recent, high-profile phishing and ransomware attacks that have made headlines and affected many people around the world, like the May 2017 WannaCry attacks. We have Proposed a Framework that can allow user to submit resources. Then scanning file for malware detection we integrated machine learning techniques. Framework is use for detection of malware. Malware is short for “malicious software,” and while some forms of it have existed for decades, the most modern malware is highly sophisticated and very difficult to detect.