IGI EPUB Test Book 85x11 by IGI Global

Author:IGI Global
Language: eng
Format: epub
Published: 2024-09-11T00:00:00+00:00

Figure 6. Importance of integrating threat intelligence into generative AI

Showcase of Techniques for Correlating Threat Intelligence Data With Behavioral Analytics to Enhance Threat Detection Capabilities

The rate of development for cybersecurity concerns is reaching unprecedented speed, which calls for constant innovation and improving detection and prevention measures. Evolving among various techniques with exceptional results is blending behavioural analytics with threat intelligence data to increase prevention capabilities. The combination of both approaches will pave the way for the cyber security strategy of organisations that confront cyber threats proactively and introduce relevant remedial measures.

Institutions can stay aware of emerging threats by employing data on threat intelligence that incorporates indicators of compromise, tactics used in the attack, and malware signatures that show details on possible riskers (Makar et al., 2023). Enhanced insight and understanding are obtained when integrated with Behavior Analytics. This tool studies and examines abnormal behaviour patterns to identify suspicious activities. Including behavioural analytics with threat intelligence data services raises AMR's power to discover threats in several dimensions (Mounce, 2020). Secondly, it gives users enough information to identify anything that looks suspicious and harmful from others that are normal. This basis of analysis focuses on essential alerts and provides the ability to react in a way that is appropriate to a real and present danger. Furthermore, this integration permits a superficial insight into more sophisticated multi-stage attacks and the regular security measures that might be ineffective against them. By integrating attack patterns with known things that could have happened, organisations can discover the incredibly complex ways that an attack could happen, which could prevent severe damage before it is too late (Goyal et al., 2023).

This way, affirmative operations are available, and a prompt reaction to detected risks that might happen and threats turn into developing insights. Although there are various ways to keep people aware of the possible threats and further enhance the prognostic analytics models using the patterns of behaviour possible, establishments might adjust the security scenarios to the changing circumstances and eliminate the emerging risks before they take place. Finally, when threat intelligence data and behavioural analytics are merged, they become a very efficient approach to help cybersecurity detect emerging threats faster and more accurately (Sarker, 2022). By merging these two data sources, organisations can understand imminent risks, distinguish between harmless and harmful behaviours, and actively recognise new dangers. In an ever-evolving landscape of increasingly complex cyberattacks happening often, employing state-of-the-art methods such as this is essential for protecting digital resources while ensuring robustness in cybersecurity defences.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.