Author
Listed:
- Md Mashfiquer Rahman
- Kailash Dhakal
- Najmul Gony MD
- Maria Khatun Shuvra SD
- Mostafizur Rahman MD
Abstract
The rapid digitization of critical infrastructure has significantly increased the complexity and frequency of cybersecurity threats. Traditional threat detection and response mechanisms are often insufficient to address evolving cyberattacks in real time. This meta-analysis aims to evaluate how artificial intelligence (AI) has been integrated into cybersecurity tools, particularly for threat detection and response, and to assess the effectiveness of various AI techniques across application domains. A systematic review was conducted across IEEE, Scopus, ACM, and PubMed databases, covering publications from 2015 to 2024. Out of 400 initially screened studies, 150 high-quality articles met the PRISMA inclusion criteria. The selected studies were categorized based on their use of AI techniques machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL) and their application areas, including malware detection, intrusion detection systems (IDS), anomaly detection, phishing prevention, and automated incident response. Statistical synthesis revealed that ML-based IDS, particularly using Random Forest and Support Vector Machine (SVM) models, improved detection accuracy by 17–35% over traditional systems. DL architectures, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, were effective in analyzing network traffic and behavioral anomalies. NLP techniques enhanced phishing detection and log analysis, while RL approaches enabled adaptive incident response and automated defense mechanisms. Overall, AI integration was found to reduce response times by up to 45% and significantly improve threat detection accuracy. AI-driven cybersecurity solutions demonstrate substantial improvements in detection accuracy and response efficiency. However, challenges such as data imbalance, lack of model explainability, vulnerability to adversarial attacks, and high computational demands persist. The study recommends the development of interpretable AI models, hybrid systems, and standardized datasets and evaluation metrics to advance future research and practical implementation.
Suggested Citation
Md Mashfiquer Rahman & Kailash Dhakal & Najmul Gony MD & Maria Khatun Shuvra SD & Mostafizur Rahman MD, 2025.
"AI integration in cybersecurity software: Threat detection and response,"
International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(3), pages 3907-3921.
Handle:
RePEc:aac:ijirss:v:8:y:2025:i:3:p:3907-3921:id:7403
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aac:ijirss:v:8:y:2025:i:3:p:3907-3921:id:7403. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Natalie Jean (email available below). General contact details of provider: https://ijirss.com/index.php/ijirss/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.