CBSE APPROACH USING CLEAN ROOM SOFTWARE ENGINEERING FOR INTRUSION DETECTION SYSTEM

被引:0
|
作者
Angurala, Mohit [1 ]
Sharma, Geetanjali [2 ]
机构
[1] PTU, Golden Coll Engn & Technol, Gurdaspur, India
[2] PTU, Beant Coll Engn & Technol, Gurdaspur, India
关键词
Network Security; CBSE; Clean Room Software Engineering;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In today's era security in network is becoming weak due to increase in number of viruses and threats. These viruses and threats are becoming stronger and stronger due to automatic tools that are coming to the market. These tools without any knowledge of coding can create harmful viruses and threats which are really hard to control. Though we have latest tools to overcome against these threats but still we cannot think that we are safe completely. So, in this paper we have proposed our own Intrusion detection system with component based software Engineering approach along with the clean room software engineering approach in which we test our component at every stage of development of a component that is during its build so that we get minimum chances of bug in our Intrusion Detection System. We have proposed an algorithm for it and as well as we have implemented its design in UML language in software.
引用
收藏
页码:3279 / 3285
页数:7
相关论文
共 50 条
  • [41] Software-based packet classification in network intrusion detection system using network processor
    Salim, Robin
    Rao, G. S. V. Radha Krishna
    TENCON 2006 - 2006 IEEE REGION 10 CONFERENCE, VOLS 1-4, 2006, : 1612 - +
  • [42] DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking
    Tuan Anh Tang
    Mhamdi, Lotfi
    McLernon, Des
    Zaidi, Syed Ali Raza
    Ghogho, Mounir
    El Moussa, Fadi
    ELECTRONICS, 2020, 9 (09) : 1 - 18
  • [43] Deep Learning Approach for Network Intrusion Detection in Software Defined Networking
    Tang, Tuan A.
    Mhamdi, Lotfi
    McLernon, Des
    Zaidi, Syed Ali Raza
    Ghogho, Mounir
    2016 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS (WINCOM), 2016, : P258 - P263
  • [44] A software implementation of a genetic algorithm based approach to network intrusion detection
    Gong, RH
    Zulkernine, M
    Abolmaesumi, P
    SIXTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERNG, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING AND FIRST AICS INTERNATIONAL WORKSHOP ON SELF-ASSEMBLING WIRELESS NETWORKS, PROCEEDINGS, 2005, : 246 - 253
  • [45] An autonomic intrusion detection system based on behavioral network engineering
    Mazzariello, Claudio
    Oliviero, Francesco
    25TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS, VOLS 1-7, PROCEEDINGS IEEE INFOCOM 2006, 2006, : 3266 - 3267
  • [46] Feature Engineering Methods in Intrusion Detection System: A Performance Evaluation
    Zare, F.
    Mahmoudi-Nasr, P.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2023, 36 (07): : 1343 - 1353
  • [47] Intrusion detection system in the Smart Distribution Network: A feature engineering based AE-LightGBM approach
    Yao, Ruizhe
    Wang, Ning
    Liu, Zhihui
    Chen, Peng
    Ma, Di
    Sheng, Xianjun
    ENERGY REPORTS, 2021, 7 : 353 - 361
  • [48] A Simple Deep Learning Approach for Intrusion Detection System
    Takeda, Atsushi
    Nagasawa, Daichi
    13TH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU2021), 2021,
  • [49] An approach to reduce false alarms in an Intrusion Detection System
    Mohajerani, M
    Moeini, A
    Haydari, H
    SAM '05: Proceedings of the 2005 International Conference on Security and Management, 2005, : 127 - 132
  • [50] Network intrusion detection system: A machine learning approach
    Panda, Mrutyunjaya
    Abraham, Ajith
    Das, Swagatam
    Patra, Manas Ranjan
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2011, 5 (04): : 347 - 356