The machine learning models employed in Kaspersky’s solutions use techniques such as Random Forest and term frequency–inverse document frequency (TF-IDF) to process vast amounts of data, enabling faster and more accurate detection of subtle threats. This combination of ML methods allows for the identification of indicators of compromise (IoCs) that traditional detection systems might overlook, leading to more precise anomaly detection and a significant improvement in overall threat detection capabilities. Kaspersky’s ongoing use of machine learning has allowed its systems to process millions of data points daily, providing real-time insights into emerging threats. This has resulted in a 25% increase in threat detections for the first half of 2024, significantly enhancing the ability to reduce response times and mitigate cyber risks. The research results will be discussed at GITEX 2024, where Kaspersky will participate in a panel on the impact of AI on cybersecurity.
