MACHINE LEARNING BASED INSTRUCTION DETECTION SYSTEM TOWARDS CYBERSPACE SECURITY

Abstract

The Internet of Things (IoT) is growing rapidly, but its security remains a critical challenge as cyberattacks can increase concurrency and lead to system errors. With cyberspace  increasingly connected, the need for robust cybersecurity mechanisms to protect critical systems from malicious attacks was never significant. Traditional intrusion detection systems (IDS) often rely on static rules and signatures, making them less effective against new or harsh threats. Machine learning-based intrusion detection systems (ML-IDs) offer a promising alternative by enabling dynamic data-driven detection of anomalies and potential intrusions. By using algorithms for MLare classification, gathering, anomaly detection, ML-IDs can learn from network traffic then adjust to new patterns. The article examines integration of machine learning to IDs to improve cybersecurity in cyberspace and highlight the possibility of recognition of complex attacks, reduction of false positives, and improving the general resistance of digital infrastructure. It compares the performance of several models for machine learning  in predicting attacks on IoT systems and addresses issues with small technology imbalance class.