AI in Malware Detection

By: Arya Brijith, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,sia University, Taiwan, arya.brijithk@gmail.com

Abstract

Artificial intelligence (AI) has emerged as a powerful ally in the continuing battle against harmful software, or malware, in the shadowy realm of cybersecurity. Artificial Intelligence (AI) is revolutionizing the field of malware detection with its abilities in pattern recognition, machine learning, and predictive analytics. In this article, we shall discuss Malware detection, the role of AI in it, and its applications.

Keywords AI, cybersecurity, malware, threats

Introduction

In the world of cybersecurity, where cyber threats cast a long shadow, artificial intelligence (AI) has emerged as a new front in the struggle against malicious software, or malware. AI is leading the way in revolutionizing malware detection with its arsenal of predictive analytics, machine learning expertise, and pattern recognition. This article delves into the topic of malware detection, revealing the critical role artificial intelligence (AI) plays and examining its uses in bolstering digital defenses against more sophisticated cyber threats.

Malware Detection

Detecting malware is similar to having a watchful virtual bodyguard who continuously examines and scans your computer or network for any odd or suspicious activity. It involves employing specialized software and tools to examine files, applications, and online activities in search of telltale symptoms or actions that might point to the presence of dangerous software. Consider it as an attentive eye that searches for anything unusual, hoping to detect and neutralize any threats like viruses, ransomware, or spyware before they can compromise your data or do damage to your system.

Role of AI in Malware Detection

Application

• For anomaly detection: AI systems excel in identifying patterns and departures from established standards, which is known as anomaly detection. With this skill, they can identify previously unidentified malicious strains or mutations that could evade conventional signature-based detection techniques.
• Machine Learning for Identification of Threats: Machine learning models are trained on extensive datasets of well-known malicious samples to identify and categorize novel and evolving threats. This adaptive learning aids in the proactive identification and categorization of malicious content based on traits and actions.
• Automation in Response to Incidents: By automating routine operations like threat analysis, prioritization, and response, artificial intelligence (AI) streamlines incident response and frees up cybersecurity teams to concentrate on more complex threats that need human intervention.

Conclusion

The convergence of cybersecurity and artificial intelligence in the field of malware detection represents a turning point in the security of digital landscapes. AI has redefined the paradigm for identifying and thwarting malicious activity with its capacity to decipher patterns, identify anomalies, and predict potential threats. Organizations may proactively strengthen their cybersecurity defenses by leveraging AI’s capabilities, quickly identifying and neutralizing threats before they compromise data or infiltrate systems. AI’s role in bolstering malicious detection capabilities will undoubtedly remain at the forefront as it develops, ensuring a resilient and proactive stance against the always-changing spectrum of cyber threats.

References

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Cite As

Brijith A. (2023) AI in Malware Detection, Insights2Techinfo, pp.1

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