Artificial Intelligence in Network Security with Autonomous Threat Response Systems

Maktoof, Mohammed Abdul Jaleel; Fadhil Mahdi, Mohammed; Zhainagul Abdirasulovna, Abdirasulova; Falih Mahdi, Ammar; T.Y. Alfalahi, Saad

doi:10.22034/jipm.2025.728441

Artificial Intelligence in Network Security with Autonomous Threat Response Systems

Document Type : Original Article

Authors

Mohammed Abdul Jaleel Maktoof ¹

Mohammed Fadhil Mahdi ²

Abdirasulova Zhainagul Abdirasulovna ³

Ammar Falih Mahdi ⁴

Saad T.Y. Alfalahi ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ 3Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728441

Abstract

ABSTRACT
Background: With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time.
Objective: This article seeks to discuss the role played by AI self-defending mechanisms in autonomous network security given their effectiveness in threat detection, response time, and the overall harm that can be caused to networks by cyber criminals.
Methods: Three separate studies were made, including conventional security systems, and analytically compared them with the AI-driven system across 100 different network environments. Machine learning (ML), deep learning (DL), and other forms of AI were applied to identify and counteract distinct threats like viruses, phishing, and even DDoS attacks. Detecting accuracy, response time and ability to mitigate attacks where among some of the other factors that were examined.
Results: Automated threat intelligence systems have a 92% accuracy while legacy systems only have 78%. Mean response time was also decreasing by 65% from 45 seconds to 15 seconds. A significant increase to attack mitigation rates was noted with fifty percent effectiveness of the AI programs averting 85 percent of the threats in the first 30 seconds of identification.
Conclusion: Autonomous threat response systems substantiate AI, which function as a radically superior replacement to conventional network security structures, minimizing threat response time and boosting the overall threat neutralization outcome. Incorporation of these types of secure mechanisms into contemporary security landscapes is important as a means of counteraction against new forms of cyber threats.

Keywords

KEYWORDS: Artificial intelligence

network security

autonomous systems

machine learning (ML)

deep learning (DL)

threat detection

cyberattacks

threat mitigation

response time

DDoS

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