Abstract :

The Exponential Growth Of Cyberattacks Against Critical Infrastructure, Enterprises, And Individuals Has Exposed Fundamental Limitations In Conventional Signature-based Intrusion Detection Systems And Manual Incident Response Workflows. Existing Approaches Fragment Detection, Investigation, And Mitigation Across Siloed Tools, Creating Dangerous Dwell-time Windows During Which Adversaries Can Exfiltrate Data, Deploy Ransomware, And Escalate Privileges. This Paper Presents An Integrated Cyber Crime Detection System Built On Kali Linux That Unifies Machine Learning-based Network Traffic Analysis, Superintelligent Agentic AI-driven Forensic Investigation, And SoftwareDefined Networking (SDN)-based Automated Mitigation In A Single Closed-loop Architecture. An Ensemble Of Random Forest, XGBoost, And Long Short-Term Memory (LSTM) Classifiers Processes 9-dimensional Flow Feature Vectors Extracted From Tcpdump/tshark Packet Captures, Achieving 96.8% Binary Classification Accuracy And 94.2% Multi-class Accuracy Across Six Attack Categories—DDoS, Port Scanning, Brute Force, Malware Propagation, Web Attacks, And Lateral Movement—with A False Positive Rate Of 2.8% On CIC-IDS2017/NSL-KDD Benchmark Datasets. Upon Detection, An Agentic AI Planner Dynamically Orchestrates Kali Linux Tools (Nmap, Wireshark/tshark, Metasploit, Autopsy, Volatility) In Threat-adaptive Investigative Sequences, Reducing Mean Investigation Time From Hours To 45 Seconds—an 87% Reduction Over Manual Workflows. The Ryu SDN Controller Enforces Severity-graded Mitigation Including Host Isolation, Iptables IP Blocking, And Fail2ban Rate Limiting With Mean Time To Mitigate Of 2.3 Seconds. SHA-256-based Cryptographic Evidence Chain-of-custody Preserves Forensic Artifacts For Legal Proceedings. End-to-end Mean Time To Respond Is 48 Seconds. Comprehensive 30-day Lab Evaluation Across Five Simulated Attack Scenarios Confirms 99.9% System Uptime And Consistent Detection Performance, Establishing The Proposed Framework As A Viable Autonomous Cyber Defense Platform For Organizations Facing The Global 3.5 Million Cybersecurity Skills Gap.

Published:

02-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

161-167

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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