Performance comparison between backpropagation algorithms applied to intrusion detection in computer network systems

被引：0
|
作者：
Ahmad, Iftikhar [1 ,2 ]
Ansari, M. A. [1 ,2 ]
Mohsin, Sajjad [1 ,2 ]
机构：
[1] FUUAST, Dept Comp Sci, Islamabad, Pakistan
[2] COMSATS Inst Informat Technol, Islamabad, Pakistan
来源：
RECENT ADVANCES IN SYSTEMS, COMMUNICATIONS AND COMPUTERS | 2008年
关键词：
intrusion detection; backpropagation; neural networks; IDS (Intrusion Detection System); learning algorithm; dataset;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper a topology of neural network intrusion detection system is proposed on which different backpropagation algorithms are benchmarked. The proposed methodology uses sampled data from KddCup99 data set, an intrusion detection attacks database that is a standard for the evaluation of intrusion detection systems. The performance of backpropagation algorithms implemented in batch mode, is addressed. A comparative analysis of algorithms is made and then the most optimum solution is selected with respect to mean square error.
引用
收藏
页码：47 / +
页数：2
相关论文
共 50 条
  • [41] Performance Analysis of an Intrusion Detection Systems Based of Artificial Neural Network
    Saber, Mohammed
    El Farissi, Ilhame
    Chadli, Sara
    Emharraf, Mohamed
    Belkasmi, Mohammed Ghaouth
    EUROPE AND MENA COOPERATION ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGIES, 2017, 520 : 511 - 521
  • [42] On improving performance of Network Intrusion Detection Systems by efficient packet capturing
    Biswas, Amitava
    Sinha, Purnendu
    2006 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, VOLS 1 AND 2, 2006, : 959 - 962
  • [43] Breast Cancer Detection Using Backpropagation Neural Network with Comparison Between Different Neuron
    Pawar, Punam Suresh
    Patil, Dharmaraj
    2012 2ND IEEE INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (PDGC), 2012, : 170 - 173
  • [44] Boruta Feature Selection Applied to Classification Algorithms for Intrusion Detection
    Lifandali, Oumaima
    Chiba, Zouhair
    Abghour, Noreddine
    PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2024, VOL 6, 2024, 1002 : 605 - 614
  • [45] A machine learning approach for improving the performance of network intrusion detection systems
    Azizan A.H.
    Mostafa S.A.
    Mustapha A.
    Mohd Foozy C.F.
    Abd Wahab M.H.
    Mohammed M.A.
    Khalaf B.A.
    Annals of Emerging Technologies in Computing, 2021, 5 (Special issue 5) : 201 - 208
  • [46] Quantitative Comparison of Unsupervised Anomaly Detection Algorithms for Intrusion Detection
    Falcao, Filipe
    Zoppi, Tommaso
    Viera Silva, Caio Barbosa
    Santos, Anderson
    Fonseca, Baldoino
    Ceccarelli, Andrea
    Bondavalli, Andrea
    SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, 2019, : 318 - 327
  • [47] Virtualization in Network Intrusion Detection Systems
    Akhlaq, Monis
    Alserhani, Faeiz
    Awan, Irfan U.
    Cullen, Andrea J.
    Mellor, John
    Mirchandani, Pravin
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2009 WORKSHOPS, 2009, 5872 : 6 - +
  • [48] Correlation between Deep Neural Network Hidden Layer and Intrusion Detection Performance in IoT Intrusion Detection System
    Han, Hyojoon
    Kim, Hyukho
    Kim, Yangwoo
    SYMMETRY-BASEL, 2022, 14 (10):
  • [49] Oblivious network intrusion detection systems
    Sayed, Mahmoud AbdelHafeez
    Taha, Mostafa
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [50] Oblivious network intrusion detection systems
    Mahmoud AbdelHafeez Sayed
    Mostafa Taha
    Scientific Reports, 13
12345