A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks.

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Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/iccsc62048.2024.10830401
Citations
1
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.35
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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm

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1 citations
Network Security and Intrusion Detection
0.35
article

A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm

Madhumita, Abhishek Kumar Gupta, Sujeet Kumar, Priyanka Rawat, Neetu Dixit
article en
1 citations

Abstract

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks.

Jagannath University (IN), Lingaya's Vidyapeeth (IN)
Openalex Percentile: Top 34%
Network Security and Intrusion Detection
0.35
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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm — Madhumita, Abhishek Kumar Gupta, et al. · (2024) | TGRS Research Map | TGRS