Intrusion Detection Using Machine Learning and Deep Learning Models on Cyber Security Attacks

Abstract

To detect and stop harmful activity in computer networks, network intrusion detection is an essential part of cybersecurity defensive systems. It is becoming more difficult for traditional rule-based techniques to identify new attack vectors in the face of the increasing complexity and diversity of cyber threats. Machine learning (ML) and deep learning (DL) models can analyze vast amounts of network traffic data and automatically identify patterns and anomalies, there has been a surge in interest in using these models for network intrusion detection. This paper examines the approaches, algorithms, and real-world applications of machine learning and deep learning techniques for network intrusion detection in order to present a thorough review of the state-of-the-art in countering cyber threats. We assess ML and DL-based intrusion detection systems' effectiveness, strengths, and weaknesses in a range of attack scenarios and network environments by synthesizing current literature and empirical research. Additionally, we talk about new developments, obstacles, and paths forward in the areas of transfer learning, adversarial robustness, and ensemble learning. The understanding gained from this investigation clarifies the potential of ML and DL models in strengthening defenses against changing cyber threats, reducing risks, and protecting vital assets. In deep learning autoencode accuracy 68\% less than other models. The performance of the CNN and LSTM algorithm is impressive and outperformed with 100\% accuracy on cyber security attacks datasets. Machine learning algorithm accuracy rate of SVM and KNN 100\% while logistic regression accuracy is 99\% GNB accuracy 80\% with training data of the models. The overall models perforamance deep learning increadible accuracy with 100\% on the training and testing data.