Modified CBDS for defending against collaborative attacks by malicious nodes in MANETs

被引:0
|
作者
Haghighi, Ahmad [1 ]
Mizanian, Kiarash [1 ]
Mirjalily, Ghasem [1 ]
机构
[1] Yazd Univ, Fac Elect & Comp Engn, Yazd, Iran
关键词
Mobile ad-hoc network (MANET); Cooprative bait detection scheme; Modified CBDS; Blackhole; Grayhole attack; PROTOCOL;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Mobile Ad-hoc Networks (MANETs) are widely used nowadays. Because of its characteristics like open medium, dynamic topology, being infrastructure less and lack of centralized monitoring, MANET is vulnerable to a wide range of attacks like blackhole and grayhole. Blackhole and grayhole attacks refer to the attacks that breach the security by performing packet forwarding and routing misbehavior and cause denial of service in MANETs. In this paper, we proposed two improvements to Cooperative Bait Detection Scheme (CBDS), we reduced both the false-positive rate in detection and the routing overhead. The proposed method (called Modified CBDS) employs Network Simulator-2 (NS-2) to validate the effectiveness under different scenarios. Simulation results show modified CBDS has a better performance in terms of throughput, end-to-end delay and energy consumption.
引用
收藏
页码:902 / 907
页数:6
相关论文
共 50 条
  • [21] Power aware malicious nodes detection for securing MANETs against packet forwarding misbehavior attack
    Kukreja, Deepika
    Dhurandher, S. K.
    Reddy, B. V. R.
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2018, 9 (04) : 941 - 956
  • [22] Power aware malicious nodes detection for securing MANETs against packet forwarding misbehavior attack
    Deepika Kukreja
    S. K. Dhurandher
    B. V. R. Reddy
    [J]. Journal of Ambient Intelligence and Humanized Computing, 2018, 9 : 941 - 956
  • [23] Defending ML-Based Feedback Loop System Against Malicious Adversarial Inference Attacks
    Vahakainu, Petri
    Lehto, Martti
    Kariluoto, Antti
    [J]. PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY (ICCWS 2021), 2021, : 382 - 390
  • [24] FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
    Zhang, Zaixi
    Cao, Xiaoyu
    Jia, Jinyuan
    Gong, Neil Zhenqiang
    [J]. PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 2545 - 2555
  • [25] Analysis and Feasibility of Reactive Routing Protocols with Malicious Nodes in MANETs
    Jain, Shilpi
    Shastri, Alankar
    Chaurasia, Brijesh Kumar
    [J]. 2013 INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORK TECHNOLOGIES (CSNT 2013), 2013, : 356 - 360
  • [26] An Analysis on the Effect of Malicious Nodes on the Performance of LAR Protocol in MANETs
    Suma, R.
    Premasudha, B. G.
    Ram, V. Ravi
    [J]. INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS, VOL 3, INDIA 2016, 2016, 435 : 623 - 633
  • [27] Prevention of Malicious Nodes Communication in MANETs by Using Authorized Tokens
    Chandrakant, N.
    Shenoy, P. Deepa
    Venugopal, K. R.
    Patnaik, L. M.
    [J]. COMMUNICATION AND NETWORKING, PT II, 2010, 120 : 441 - +
  • [28] Grey Model and Polynomial Regression for Identifying Malicious Nodes in MANETs
    Silva, Anderson A. A.
    Pontes, Elvis
    Zhou, Fen
    Kofuji, Sergio Takeo
    [J]. 2014 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2014), 2014, : 162 - 168
  • [29] A Reliable Solution against Packet Dropping Attack due to Malicious Nodes Using Fuzzy Logic in MANETs
    Chaudhary, Alka
    Tiwari, V. N.
    Kumar, Anil
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON RELIABILTY, OPTIMIZATION, & INFORMATION TECHNOLOGY (ICROIT 2014), 2014, : 178 - 181
  • [30] EMAODV: TECHNIQUE TO PREVENT COLLABORATIVE ATTACKS IN MANETs
    Rana, Anuj
    Rana, Vinay
    Gupta, Sandeep
    [J]. PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON ECO-FRIENDLY COMPUTING AND COMMUNICATION SYSTEMS, 2015, 70 : 137 - 145