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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Revolutionizing Network Security with AI and Machine Learning Solutions

by Tahir Bashir, Najeeb Abbas Al-Sammarraie
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 53
Year of Publication: 2024
Authors: Tahir Bashir, Najeeb Abbas Al-Sammarraie
10.5120/ijca2024924217

Tahir Bashir, Najeeb Abbas Al-Sammarraie . Revolutionizing Network Security with AI and Machine Learning Solutions. International Journal of Computer Applications. 186, 53 ( Dec 2024), 35-42. DOI=10.5120/ijca2024924217

@article{ 10.5120/ijca2024924217,
author = { Tahir Bashir, Najeeb Abbas Al-Sammarraie },
title = { Revolutionizing Network Security with AI and Machine Learning Solutions },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 53 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number53/revolutionizing-network-security-with-ai-and-machine-learning-solutions/ },
doi = { 10.5120/ijca2024924217 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-07T02:20:23.464426+05:30
%A Tahir Bashir
%A Najeeb Abbas Al-Sammarraie
%T Revolutionizing Network Security with AI and Machine Learning Solutions
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 53
%P 35-42
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As cyber threats evolve and grow more sophisticated, traditional network security approaches are struggling to keep pace with the increasing complexity of modern attacks. This paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing network security by automating critical processes such as threat detection and response. Traditional security models rely heavily on manual monitoring and predefined rules, which can result in delayed responses and missed threats. AI and ML technologies offer an alternative by enabling real-time analysis of network traffic, the identification of anomalies, and proactive threat mitigation. These systems are capable of learning from historical data, improving their detection capabilities over time, and adapting to new and unknown threats, including zero-day vulnerabilities. This research is based on a comprehensive review of existing literature and case studies from industries where AI and ML have been successfully integrated into security frameworks. The findings illustrate the effectiveness of AI and ML in improving security performance, reducing human error, and enhancing operational efficiency. Organizations that have adopted these technologies report faster response times, more accurate threat detection, and fewer false positives. However, the adoption of AI and ML also presents challenges, including the need for substantial initial investments, technical integration with legacy systems, and the requirement for skilled personnel to manage and optimize these technologies. Despite these challenges, AI and ML are becoming indispensable tools for organizations seeking to bolster their cybersecurity capabilities. As cyberattacks grow increasingly complex, the ability to automate critical security tasks and respond to threats in real time positions AI and ML as essential components of modern network defense strategies. This research underscores their potential to transform network security and highlights their role in protecting against the ever-increasing threat of cyberattacks.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Machine Learning Network Security Cybersecurity Zero Trust Architecture Threat Detection Automation Cloud Security Enterprise Networks

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