Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network

Hitesh Keserwani; Himanshu Rastogi; Ardhariksa Zukhruf Kurniullah; Sushil Kumar Janardan; Ramakrishnan Raman; Vinod Motiram Rathod; Ankur Gupta

Hitesh Keserwani Amity Business School, Amity University, Lucknow, Uttar Pradesh, India

Himanshu Rastogi Amity Business School, Amity University, Lucknow, Uttar Pradesh, India

Ardhariksa Zukhruf Kurniullah Faculty of Communications Science, Universitas Mercu Buana, Jakarta, Indonesia

Sushil Kumar Janardan Department of Computer Science and Engineering, Rungta College of Engineering and Technology Bhilai, Rungta Educational Campus, Kohka-Kurud Road, Bhilai - 490024, Chhattisgarh, India

Ramakrishnan Raman Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra, India

Vinod Motiram Rathod Bharati Vidyapeeth Deemed University, Department of Engineering and Technology, Navi Mumbai, Maharashtra, India

Ankur Gupta Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak- 124001, Haryana, India

Need of security enhancement for 5G network has been increased inlast decade. Data transmitted over network need to be secure fromexternal attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different securitysystem that focus on identification of attacks. In order to identifyattack different machine learning mechanism are considered. But theissue with existing research work is limited security and performanceissue. There remains need to enhance security of 5G network. Toachieve this objective hybrid mechanism are introduced. Differenttreats such as Denial-of-Service, Denial-of-Detection, Unfair use orresources are classified using enhanced machine learning approach.Proposed work has make use of LSTM model to improve accuracyduring decision making and classification of attack of 5G network.