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.
Das et al. (Sat,) studied this question.