Deep IDS : A deep learning approach for Intrusion detection based on IDS 2018

被引:12
|
作者
Dey, Arunavo [1 ]
机构
[1] Bangladesh Univ Business & Technol, Dept Comp Sci & Engn, Dhaka, Bangladesh
关键词
Intrusion Detection; Anomaly Detection; Attention Mechanism; Neural Network; LSTM; CNN;
D O I
10.1109/STI50764.2020.9350411
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Intrusion Detection is one of the fields network security important for industry 4.0. Applying deep learning models opened a new scope in this field. But availability of latest data set and volume makes it often harder to apply latest techniques. Moreover emergence of new machine learning algorithms always hold scope to improve over the existing ones. In this paper, the effectiveness of attention mechanism over the existing deep learning techniques for Intrusion detection is being proposed and a novel attention based CNN-LSTM model has been proposed based on IDS 2018 data set. A detail performance evaluation on IDS 2018 has been elaborated to establish the claim.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] HEDL-IDS: A Hybrid Ensemble Deep Learning Approach for Cyber Intrusion Detection
    Psathas, Anastasios Panagiotis
    Iliadis, Lazaros
    Papaleonidas, Antonios
    Bountas, Dimitris
    [J]. ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART I, 2022, 646 : 116 - 131
  • [2] TDL-IDS: Towards A Transfer Deep Learning based Intrusion Detection System
    Sun, Xingguo
    Meng, Weizhi
    Chiu, Wei-Yang
    Lampe, Brooke
    [J]. 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 2603 - 2608
  • [3] DL-IDS: a deep learning-based intrusion detection framework for securing IoT
    Otoum, Yazan
    Liu, Dandan
    Nayak, Amiya
    [J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2022, 33 (03):
  • [4] HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles
    Ullah, Safi
    Khan, Muazzam A.
    Ahmad, Jawad
    Jamal, Sajjad Shaukat
    Huma, Zil E.
    Hassan, Muhammad Tahir
    Pitropakis, Nikolaos
    Arshad
    Buchanan, William J.
    [J]. SENSORS, 2022, 22 (04)
  • [5] DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection
    Maseer, Ziadoon K.
    Yusof, Robiah
    Mostafa, Salama A.
    Bahaman, Nazrulazhar
    Musa, Omar
    Al-rimy, Bander Ali Saleh
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 69 (03): : 3945 - 3966
  • [6] A new distributed anomaly detection approach for log IDS management based on deep learning
    Koca, Murat
    Aydin, Muhammed Ali
    Sertbas, Ahmet
    Zaim, Abdul Halim
    [J]. TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2021, 29 (05) : 2486 - 2501
  • [7] GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture
    Berguiga, Abdelwahed
    Harchay, Ahlem
    Massaoudi, Ayman
    Ben Ayed, Mossaad
    Belmabrouk, Hafedh
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 77 (01): : 379 - 402
  • [8] L-IDS: A Lifelong Learning Approach for Intrusion Detection
    Doroud, Hossein
    Alkhateeb, Omar
    Jarchlo, Elnaz Alizadeh
    Dressler, Falko
    [J]. 2023 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2023, : 482 - 487
  • [9] AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection
    Kim, Aechan
    Park, Mohyun
    Lee, Dong Hoon
    [J]. IEEE ACCESS, 2020, 8 : 70245 - 70261
  • [10] Metaverse-IDS: Deep learning-based intrusion detection system for Metaverse-IoT networks
    Gaber, Tarek
    Awotunde, Joseph Bamidele
    Torky, Mohamed
    Ajagbe, Sunday A.
    Hammoudeh, Mohammad
    Li, Wei
    [J]. INTERNET OF THINGS, 2023, 24