Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized many aspects of our lives in recent years. However, with these technological advancements come significant challenges, and one of the most pressing is cybercrime. Cybercriminals have capitalized on the pervasive nature of digital technologies, exploiting vulnerabilities in governments, businesses, and civil societies around the world. As a result, there has been a surge in the demand for intelligent threat detection systems that rely on AI and ML to combat this global threat.

This article delves into the topic of AI-based cyber threat detection and explores its importance in protecting our modern digital ecosystems. It specifically focuses on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection. By investigating these models and their integration into network security, mobile security, and IoT security, we can better understand the challenges that arise when deploying AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures.

One of the key takeaways from this discussion is the need for a comprehensive approach to cybersecurity. Traditional methods of threat detection, which rely heavily on human intervention, are no longer sufficient in the face of rapidly evolving cyber threats. Instead, AI and ML offer a more proactive and adaptive solution, capable of analyzing vast amounts of data in real-time to detect anomalies and potentially malicious activity. This shift towards intelligent threat detection systems is crucial for staying one step ahead of cybercriminals.

However, integrating AI-enabled cybersecurity solutions into existing IT infrastructures poses its own set of challenges. Legacy systems may not be compatible with the advanced algorithms and models that power AI-based threat detection systems. Additionally, issues of data privacy, ethics, and explainability arise when relying on AI to make critical security decisions. Overcoming these hurdles requires careful planning, collaboration between different stakeholders, and a commitment to ongoing monitoring and evaluation.

Looking towards the future, this paper suggests several research directions to further enhance the security and resilience of our modern digital industries, infrastructures, and ecosystems. This includes the exploration of advanced AI techniques, such as deep learning and reinforcement learning, to improve threat detection accuracy and response time. Additionally, research is needed to address the challenges of securing mobile devices and IoT devices, which are increasingly interconnected and vulnerable to cyber attacks.

In conclusion, AI-based cyber threat detection is an essential tool in safeguarding our digital ecosystems. The advancements in AI and ML have paved the way for more sophisticated and proactive security measures. However, implementing these solutions requires careful consideration of the challenges and limitations associated with integrating AI into existing IT systems. By addressing these issues and investing in continued research, we can strengthen the security posture of our digital world and mitigate the threats posed by cybercrime.

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