International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
IDENTIFYING OF SECURITY THREAT IN THE NETWORK USING ML TECHNIQUES Nelluri Raja Sekhar2,Bandlamudi Sarath3,Konakanchi Sai teja4,Gokarla Syam Sundhar5, Mr.B.Kalyan Chakravarthy 6 M.Tech UG Students, Department of IT, 6(Associate Professor) Vasireddy Venkatadri Institute of Technology, Nambur, Guntur Dt., Andhra Pradesh. -------------------------------------------------------------------------***------------------------------------------------------------------Abstract 2,3,4,5
Effective methods for identifying malicious activity in computer networks are in greater demand due to the complexity and diversity of cyberattacks becoming more and more complicated. In this paper, a unique machine learning approach to network intrusion detection is presented. We provide a multi-phase system that includes the steps of feature selection, extraction, and classification. The suggested framework analyse network traffic data and looks for patterns of suspicious behaviour using a variety of statistical and machine learning algorithms. Experiments carried out on a real-world dataset show how effective the suggested method is. The findings demonstrate that a variety of network assaults, such as Denial of Service (DoS), Remote to Local (R2L), User to Root (U2R), and probing attacks, may be reliably identified by our method. Additionally, our method performs better than some cutting-edge.
Keywords: XGBoost, LSTM, SMOTE, NSL-KDD, Network Attack, Machine Learning. 1.Introduction
point to possible hostile activity. This research specifically aims to develop a machine learning model capable of reliably classifying network data into two categories: harmful and normal. 1. Assessing the suggested method's efficacy using a dataset made up of different network attacks. 2. Evaluating how well the suggested method performs in comparison to more established methods of network security, like intrusion detection systems and firewalls (IDS). 3. Providing information about how machine learning may be used to identify and stop dangerous activity in computer networks.
The proliferation of cyber dangers and harmful actions can be attributed to the fast expansion of computer networks and the growing dependence on technology. In order to protect their networks and sensitive data, businesses are now very concerned with identifying and stopping these operations. Intrusion detection systems (IDS) and firewalls, two common forms of network security, are not very good at detecting and neutralizing. As a result, more sophisticated and effective methods of identifying and stopping harmful activity are required. A promising method for identifying and stopping harmful activity in computer networks is machine learning. Large amounts of network traffic data can be analysed by machine learning algorithms, which can then be used to spot trends and abnormalities that might point to possible malicious activity.
3.Related Work Numerous investigations have been carried out to identify malevolent actions within computer networks using the utilization of machine learning methods and the NSL-KDD dataset. In this linked article, we review a few recent research that have employed the NSL-KDD dataset and the XGBOOST and LSTM algorithms to detect harmful activity in computer networks. One study suggested utilizing the XGBOOST algorithm in conjunction with machine learning to identify network assaults. The study trained the model using a variety of features taken from network traffic data. The XGBOOST model was then applied to categorize network traffic as harmful or legitimate.
Next, network traffic is divided into types based on the model: malicious and regular. The efficacy of the suggested methodology is assessed using a dataset comprising diverse network assaults, showcasing the capability of machine learning to identify and avert malevolent actions within computer networks.
2.Objective This paper's primary goal is to suggest a machine learning-based method for identifying harmful activity in computer networks. The strategy looks to analyse network traffic data using machine learning techniques in order to spot trends and abnormalities that might
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Another study suggested utilizing the LSTM algorithm in conjunction with deep learning to identify network assaults. In order to train the LSTM model and model the
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