IMPROVING METHODS FOR DETECTING AND PREVENTING CYBERATTACKS IN COMPUTER NETWORKS

Babakulov , Bekzod

Innovation science and technologiy · 2026-yil

Annotatsiya

This study develops an adaptive hybrid framework for detecting and preventing cyberattacks in computernetworks. The framework combines signature matching, supervised classification, unsupervised anomaly detection, behavioralcorrelation, asset context, and a safeguarded response policy. Its purpose is to preserve reliable detection when legitimate trafficchanges and when previously unseen attacks do not match existing rules. A design-science methodology was used together witha controlled streaming emulation containing 120,000 training flows and 120,000 test flows distributed across baseline, benigndrift,and mixed zero-day-like windows. In the emulation, the proposed method achieved 98.94% accuracy, 98.31% precision,97.39% recall, a 97.85% F1-score, and a 0.55% false-positive rate. The strongest advantage appeared in the mixed/zero-daywindow, where its F1-score reached 94.83%, compared with 73.90% for a static Random Forest and 57.26% for signature-onlydetection. The results support a practical conclusion: prevention should be separated from detection and activated graduallythrough logging, alerting, rate limiting, session interruption, and isolation, with higher-impact actions requiring corroborationand rollback.

Maqola ma’lumotlari
MualliflarBabakulov , Bekzod
JurnalInnovation science and technologiy
Nashr sanasi2026-07-01
Jild2
Son7
Betlar246-255
TilIngliz
DOI10.5281/zenodo.21801202

Kalit so‘zlar

network intrusion detection; intrusion prevention; concept drift; Random Forest; Isolation Forest; behavioral analytics; zero-day attack; adaptive threshold; cyber resilience

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