Intrusion Detection System based on Machine Learning and Deep Learning Model

👥 Ankit Gupta, Dr.Harsh Mathur

📙 Abstract : The exponential growth of cloud computing, Internet of Things (IoT), Industrial Internet of Things (IIoT), edge computing, and intelligent networking has significantly increased the frequency and complexity of cyber-attacks. Traditional signature-based Intrusion Detection Systems (IDS) are often ineffective against zero-day attacks, polymorphic malware, and sophisticated network intrusions due to their dependence on predefined attack signatures and limited adaptability. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promising solutions for intelligent intrusion detection by automatically learning network traffic patterns and identifying malicious activities. This literature survey critically reviews recent advances in ML- and DL-based intrusion detection systems, compares existing methodologies using standard performance metrics such as Accuracy, Precision, Recall, F1-score, False Positive Rate (FPR), False Negative Rate (FNR), Area Under the Curve (AUC), and Detection Rate (DR), and identifies current research gaps. This research work presents, comparative study for intrusion detection system using ML-DL based approaches, and also provides valuable insights for designing next-generation intelligent, scalable, explainable, and lightweight intrusion detection systems suitable for modern cloud, IoT, and edge computing environments.

🔖 Keywords :️ Machine learning, Network security, Deep learning, Intrusion detection system, Cyberattacks.