AI in the Trenches: How Machine Learning is Fighting Cybercrime
Sriharsha Daram · International Journal of Computer Trends and Technology · 2024
Cybersecurity threats increased incidents, and the sophistication of the development of superior countermeasures has never been more significant. Although not powerless in detecting or preventing threats, conventional security solutions currently available are insufficient in combating complex attacks like ransomware, phishing, and zero-day attacks. To overcome this, Artificial Intelligence (AI) and Machine Learning (ML) are the key technologies enabling cybersecurity by implementing automated tools for detecting and preventing such attacks. Compared to rule-based systems, AI applications can be updated and modified, which sets them as optimal for anomaly detection, pattern finding, and predictive evaluation. They all take vast volumes of data and process it in near real-time, and are able to pick out patterns or features that may indicate signs of attack, enabling more accurate and quicker threat detection. However, incorporating AI and ML in cybersecurity is not without its hurdles; training and validating such systems involves inputting a large volume of data, especially personally identifiable and organizational data. Further, the ability to scale the model can also be a challenge since AI models have to perform well in various network environments to prevent, detect, and respond to a range of threats without slowing down the system. In addition, issues of adversarial attacks on the machine learning models in which the attackers ensure that they provide data that the AI system will find hard to decipher qualify as a serious threat due to their impact on the reliability of these systems. Thus, despite its effectiveness in the field of cybersecurity, it is relevant to note that the constant enhancement of the applied technologies is the key to further protection against new and more complex cyber threats.