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Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

Basil Abdullah AL-Zahrani

TL;DR

The paper tackles the challenge of static, rule-based intrusion detection and costly deception platforms by introducing CADL, an adaptive deception framework that fuses ensemble detectors with behavioral analysis and a signal bus for real-time coordination. The approach achieves a 99.88% detection rate and a 0.13% false positive rate on CICIDS2017, surpassing traditional IDS benchmarks, while providing 89% accuracy in attacker profiling and an open-source implementation. Key contributions include an ensemble detection core (RF, XGBoost, NN) with a tuned voting scheme, a behavioral profiling mechanism for escalation-driven deception, and a production-ready, transparent evaluation framework. The work has practical impact by offering a cost-effective, scalable defense that can be deployed by resource-constrained organizations, and it sets the stage for broader cross-dataset validation and adversarial robustness analysis in future research.

Abstract

This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. The system profiles attackers based on temporal patterns and deploys customized deception strategies across five escalation levels. Evaluation on 50,000 CICIDS2017 test samples demonstrates that CADL significantly outperforms traditional intrusion detection systems (Snort: 71.2%, Suricata: 68.5%) while maintaining production-ready false positive rates. The framework's behavioral analysis achieves 89% accuracy in classifying attacker profiles. We provide open-source implementation and transparent performance metrics, offering an accessible alternative to commercial deception platforms costing $150-400 per host annually.

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

TL;DR

The paper tackles the challenge of static, rule-based intrusion detection and costly deception platforms by introducing CADL, an adaptive deception framework that fuses ensemble detectors with behavioral analysis and a signal bus for real-time coordination. The approach achieves a 99.88% detection rate and a 0.13% false positive rate on CICIDS2017, surpassing traditional IDS benchmarks, while providing 89% accuracy in attacker profiling and an open-source implementation. Key contributions include an ensemble detection core (RF, XGBoost, NN) with a tuned voting scheme, a behavioral profiling mechanism for escalation-driven deception, and a production-ready, transparent evaluation framework. The work has practical impact by offering a cost-effective, scalable defense that can be deployed by resource-constrained organizations, and it sets the stage for broader cross-dataset validation and adversarial robustness analysis in future research.

Abstract

This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. The system profiles attackers based on temporal patterns and deploys customized deception strategies across five escalation levels. Evaluation on 50,000 CICIDS2017 test samples demonstrates that CADL significantly outperforms traditional intrusion detection systems (Snort: 71.2%, Suricata: 68.5%) while maintaining production-ready false positive rates. The framework's behavioral analysis achieves 89% accuracy in classifying attacker profiles. We provide open-source implementation and transparent performance metrics, offering an accessible alternative to commercial deception platforms costing $150-400 per host annually.

Paper Structure

This paper contains 44 sections, 6 equations, 1 figure, 5 tables.

Table of Contents

  1. Introduction
  2. Research Contributions
  3. Paper Organization
  4. Related Work
  5. Intrusion Detection Systems
  6. Machine Learning for Intrusion Detection
  7. Deception Technologies
  8. Coordinated Defense
  9. System Architecture
  10. Overview
  11. Ensemble Detection System
  12. Random Forest Classifier
  13. XGBoost Classifier
  14. Neural Network
  15. Ensemble Combination
  16. ...and 29 more sections

Figures (1)

  • Figure 1: CADL Architecture. The ensemble detector achieves 99.88% accuracy through model combination. Behavioral profiling ($P = \langle\tau, \sigma_\tau, n, \rho, \lambda\rangle$) enables adaptive responses across five escalation levels (L1-L5). The signal bus coordinates real-time intelligence sharing between components.