AI-Driven Cybersecurity Threat Detection: Building Resilient ‎Defense Systems Using Predictive Analytics - Das, Biswajit Chandra; Sartaz, M Saif; Reza, Syed Ali; Hossain, Arat; Nasiruddin, Md; Bishnu, Kanchon Kumar; Sultana, Kazi Sharmin; Shatyi, Sadia Sharmeen; Khan, MD Azam; Abed, Joynal (International Journal of Basic and Applied Sciences, 2025)
This study examines how Artificial Intelligence can aid in identifying and mitigating cyber threats in the U.S. across four key areas: intru-‎sion detection, malware classification, phishing detection, and insider threat analysis. Each of these problems has its quirks, meaning there ‎needs to be different approaches to each, so we matched the models to the shape of the problem. For intrusion detection, catching things like ‎unauthorized access, we tested unsupervised anomaly detection methods. Isolation forests and deep autoencoders both gave us useful sig-‎nals by picking up odd patterns in network traffic. When it came to malware detection, we leaned on ensemble models like Random Forest ‎and XGBoost, trained on features pulled from files and traffic logs. Phishing was more straightforward. We fed standard classifiers (logistic ‎regression, Random Forest, XGBoost) a mix of email and web-based features. These models handled the task surprisingly well; phishing ‎turned out to be the easiest problem to crack, at least with the data we had. There was a different story. We utilized an LSTM autoencoder to ‎identify behavioral anomalies in user activity logs. It caught every suspicious behavior but flagged a lot of harmless ones too. That kind of ‎model makes sense when the cost of missing a threat is high and you’re willing to sift through some noise. What we saw across the board is ‎that performance wasn’t about stacking the most complex model. What mattered was how well the model’s structure matched the way the ‎data behaved. When signals were strong and obvious, simple models worked fine. But for messier, more subtle threats, we needed some-‎thing more adaptive, sequence models and anomaly detectors, though they brought their trade-offs. The takeaway here is clear: in cybersecu-‎rity, context drives the solution. There’s no universal model that works for everything. The smart move is to build systems that fit the prob-‎lem, and more importantly, evolve with it. Threats don’t sit still, and neither should our defenses‎.
Keywords: Anomaly Detection; Autoencoder; Cybersecurity; Predictive Modeling; LSTM; Threat Detection; UEBA; XGBoost