Samuel Ndichu

Samuel Ndichu, Tao Ban, Seiichi Ozawa et al.

System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog (Natural-Language Log), a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools them with term-frequency-inverse-document-frequency weighting, classifies sessions with tree ensembles, and back-projects evidence with TreeSHAP for analyst review. On Hadoop Distributed File System (HDFS) and Blue Gene/L (BGL) corpora, NLLog exceeds two reproduced matched-protocol baselines; across HDFS, BGL, and the AIT Alert Data Set, it sustains low false-positive rates with commodity-hardware latency suitable for security operations center triage. Coverage, sparse-versus-dense, faithfulness, and adversarial ablations show that fallback sufficiency is corpus-dependent, that an enrollment-time coverage check can surface refinement requirements before deployment, and that an auditable deterministic rewrite combined with lightweight dense encoding provides a measurable representation layer for log-anomaly detection and triage.

Samuel Ndichu, Tao Ban, Seiichi Ozawa et al.

Security operations centers face persistent alert fatigue: in low-prevalence streams, even low false-positive rates generate substantial investigation load, while aggregate F1 scores obscure analyst burden. We introduce PACT, a Pareto-aware controller for triggered active learning, which wraps an already-deployed frozen XGBoost-Focal screener with an adaptive windowing score-shift trigger and a hybrid acquisition rule combining threshold-relative uncertainty with high-score sampling. On two public low-prevalence benchmarks, AIT-ADS (AIT Alert Data Set), and BOTSv1 (Boss of the SOC version 1), PACT attains the lowest benign-normalized false-positive (FP) burden among the adaptive methods tested. It reduces burden by 43% and 21%, respectively, relative to a frozen baseline, while using 3.8x and 5.2x fewer analyst queries than periodic uniform-random updating. A matched-trigger ablation controls trigger timing and shows that acquisition contributes beyond timing alone, at the cost of approximately ten percentage points of positive-window recall under free-running triggers. A frozen threshold-only baseline pushes FP lower still but collapses BOTSv1 recall by 55 percentage points. Under the evaluated workload assumptions, pure FP minimization trades unacceptable recall for that lower burden.

Samuel Ndichu, Akira Yamada, Tao Ban et al.

Security alert screening is the downstream task of filtering, prioritizing, correlating, and contextualizing alerts for analyst attention in Security Operations Centers. This survey reviews artificial-intelligence-driven alert screening and alert-fatigue mitigation from 2015 to 2026. We synthesize 119 records, including 87 core studies, into a four-stage workflow taxonomy covering filtering, triage, correlation, and generative augmentation. We find persistent gaps in deployment realism, adversarial robustness, cross-environment validation, and evaluation practice. The survey concludes with a research agenda toward trustworthy Cognitive Security Operations Centers.