NEW CYBER SECURITY METHODS IN NETWORK USING LOGISTIC REGRESSION TECHNIQUES

Abstract

ABSTRACT: The network traffic should be monitored and analyzed to detect malicious activities and attacks to ensure reliable functionality of the networks and security of users’ information. Machine learning techniques can be applied to detect the network attacks. Network security is one of the major concerns of the modern. With the rapid development and massive usage of internet over the past decade the vulnerabilities of network security have become an important issue. Our approach is to use three learning techniques in parallel gated recurrent unit (GRU), convolution neural network as deep techniques and Random Forest as an ensemble technique. Our main goal is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. The performance of the proposed system is compared with conventional machine learning algorithms namely, Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT) and Random Forest (RF) methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. The much more popular kinds of cyber security risks are evaluated using machine learning algorithms which describe how machine learning is used for computer defense such as the identification and avoidance of attacks, vulnerability scanning and recognition and public internet risk assessment.