Safeguarding against external intrusions utilizing adaptive bio-inspired multi-population anomaly detection for IoT...

Area of Science:

• Cybersecurity
• Network Security
• Artificial Intelligence

Background:

• The proliferation of Internet of Things (IoT) devices necessitates advanced security measures against evolving cyber threats.
• Existing anomaly-based intrusion detection systems struggle to identify novel and zero-day attacks in dynamic IoT environments.
• There is a critical need for adaptive security solutions that can effectively counter sophisticated cyberattacks targeting IoT ecosystems.

Purpose of the Study:

• To introduce a novel anomaly-based intrusion detection system, Chaotic Multi-Population Grasshopper Optimization with Differential Evolution (CMGODE), for enhanced IoT network security.
• To improve the performance of traditional optimization algorithms by integrating chaotic mapping, multi-population strategies, and differential evolution.
• To address the limitations of conventional detection frameworks in identifying sophisticated and previously unseen cyberattack patterns.

Main Methods:

• Developed Chaotic Multi-Population Grasshopper Optimization with Differential Evolution (CMGODE) for intrusion detection.
• Integrated chaotic mapping to enhance exploitation and prevent premature convergence in the optimization process.
• Employed a multi-population strategy and differential evolution for improved diversity, global search, and solution refinement.

Main Results:

• The CMGODE-based system demonstrated high detection accuracy and computational efficiency on BoT-IoT and UNSW-NB15 datasets.
• The proposed method consistently outperformed several state-of-the-art approaches in identifying diverse cyber threats.
• Achieved a superior balance between detection rates and processing speed for real-time IoT security applications.

Conclusions:

• CMGODE offers a robust and adaptive solution for detecting known and novel cyberattacks in IoT networks.
• The enhanced optimization techniques significantly improve the effectiveness of anomaly-based intrusion detection systems.
• This approach provides a promising direction for securing dynamic and complex IoT ecosystems against advanced cyber threats.