An intrusion detection system using optimized deep neural network architecture

被引:37
|
作者
Ramaiah, Mangayarkarasi [1 ]
Chandrasekaran, Vanmathi [1 ]
Ravi, Vinayakumar [2 ]
Kumar, Neeraj [3 ,4 ,5 ]
机构
[1] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India
[2] Prince Mohammad Bin Fahd Univ, Ctr Artificial Intelligence, Khobar, Saudi Arabia
[3] Thapar Univ, Dept Comp Sci & Engn, Patiala, Punjab, India
[4] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan
[5] Univ Petr & Energy Studies, Sch Comp, Dehra Dun, Uttarakhand, India
关键词
LEARNING APPROACH; MODEL;
D O I
10.1002/ett.4221
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber-attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural-based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural-based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber-attacks.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Deep Neural Network Architecture for Anomaly Based Intrusion Detection System
    Behera, Sidharth
    Pradhan, Ayush
    Dash, Ratnakar
    [J]. 2018 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN), 2018, : 270 - 274
  • [2] An optimized hybrid deep neural network architecture for intrusion detection in real-time IoT networks
    Shobana, M.
    Shanmuganathan, C.
    Challa, Nagendra Panini
    Ramya, S.
    [J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2022, 33 (12)
  • [3] Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security
    Kang, Min-Joo
    Kang, Je-Won
    [J]. PLOS ONE, 2016, 11 (06):
  • [4] Method of Intrusion Detection using Deep Neural Network
    Kim, Jin
    Shin, Nara
    Jo, Seung Yeon
    Kim, Sang Hyun
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2017, : 313 - 316
  • [5] Intrusion Detection System based on Network Traffic using Deep Neural Networks
    Chamou, Dimitra
    Toupas, Petros
    Ketzaki, Eleni
    Papadopoulos, Stavros
    Giannoutakis, Konstantinos M.
    Drosou, Anastasios
    Tzovaras, Dimitrios
    [J]. 2019 IEEE 24TH INTERNATIONAL WORKSHOP ON COMPUTER AIDED MODELING AND DESIGN OF COMMUNICATION LINKS AND NETWORKS (IEEE CAMAD), 2019,
  • [6] Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network
    Sayed, Nasir
    Shoaib, Muhammad
    Ahmed, Waqas
    Qasem, Sultan Noman
    Albarrak, Abdullah M.
    Saeed, Faisal
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (01): : 1351 - 1374
  • [7] An intrusion detection system for wireless sensor networks using deep neural network
    Gowdhaman, V
    Dhanapal, R.
    [J]. SOFT COMPUTING, 2022, 26 (23) : 13059 - 13067
  • [8] An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units
    Xu, Congyuan
    Shen, Jizhong
    Du, Xin
    Zhang, Fan
    [J]. IEEE ACCESS, 2018, 6 : 48697 - 48707
  • [9] An intrusion detection system for wireless sensor networks using deep neural network
    V. Gowdhaman
    R. Dhanapal
    [J]. Soft Computing, 2022, 26 : 13059 - 13067
  • [10] Deep recurrent neural network for IoT intrusion detection system
    Almiani, Muder
    AbuGhazleh, Alia
    Al-Rahayfeh, Amer
    Atiewi, Saleh
    Razaque, Abdul
    [J]. SIMULATION MODELLING PRACTICE AND THEORY, 2020, 101