Enhancing IoT security: A competitive coevolutionary strategy for detecting RPL attacks in challenging attack environments

被引:0
|
作者
Yilmaz, Selim [1 ]
机构
[1] Mugla Sitki Kocman Univ, Dept Software Engn, TR-48000 Mugla, Turkiye
关键词
RPL; RPL attacks; Intrusion detection; Competitive coevolution; Genetic programming; Genetic algorithm; INTRUSION DETECTION SYSTEM; VERSION NUMBER ATTACKS; LEARNING APPROACH; ROUTING PROTOCOL; LOW-POWER; INTERNET; THINGS; NETWORKS;
D O I
10.1016/j.comnet.2025.111185
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Internet of Things (IoT) is a recent technology that allows heterogeneous devices to communicate with each other and the Internet. Designed specifically for IoT-enabled networks, the IPv6 Routing Protocol for Low Power Lossy Network (RPL) is adopted as standard routing protocol today. While RPL facilitates efficient routing between IoT devices, it is very susceptible to attacks, leading to numerous threats targeting different aspects of the nodes and network. Consequently, several efforts have been made to develop intrusion detection systems to secure RPL-operated networks. However, many existing solutions are tailored to specific attacks, making them unsuitable for other RPL attacks. Additionally, they depend on fixed simulations with specific scenarios, neglecting the influence of attack environments on detection system performance. The impact of RPL attacks varies with factors such as attacker density and position in the network. Consequently, it is crucial to design IDS that can effectively handle these dynamic conditions. This study addresses these challenges by proposing a competitive coevolution-based intrusion detection system that focuses on the most challenging attack environments. To achieve this, the intrusion detection algorithm and challenging attack environments are competitively evolved. Targeting the network's topology, traffic, and resources through the exploitation of control packets, this study investigates 11 RPL attacks: blackhole, DIS flooding, DAG inconsistency, DAO inconsistency, decreased rank, energy depletion, forwarding misbehavior, increased version, spam DIS, selective forwarding, and worst parent. To assess detection performance, a wide range of evaluation metrics such as accuracy, precision, recall, false alarm rate, and F1-score are used. The findings demonstrate that the proposed system ensures strong detection performance with very low memory and power consumption, suggesting its effectiveness against the attacks threatening the multiple aspects of the network and its applicability on resource-constrained nodes.
引用
收藏
页数:23
相关论文
共 50 条
  • [31] IoTPredictor: A security framework for predicting IoT device behaviours and detecting malicious devices against cyber attacks
    Kalaria, Rudri
    Kayes, A. S. M.
    Rahayu, Wenny
    Pardede, Eric
    Salehi, S. Ahmad
    COMPUTERS & SECURITY, 2024, 146
  • [32] Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks
    Almaraz-Rivera, Josue Genaro
    Cantoral-Ceballos, Jose Antonio
    Botero, Juan Felipe
    SENSORS, 2023, 23 (21)
  • [33] Enhancing network security in industrial IoT environments: a DeepCLG hybrid learning model for cyberattack detection
    Gulzar, Qawsar
    Mustafa, Khuram
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2025,
  • [34] Design and analysis of an adaptive, global strategy for detecting and it gating distributed DoS attacks in GRID environments
    Znati, T
    Amadei, J
    Pazehoski, DR
    SweenY, S
    39TH ANNUAL SIMULATION SYMPOSIUM, PROCEEDINGS, 2006, : 2 - 9
  • [35] Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms
    Koirala, Ashish
    Bista, Rabindra
    Ferreira, Joao C.
    FUTURE INTERNET, 2023, 15 (06)
  • [36] A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest-RFTRUST
    Prathapchandran, K.
    Janani, T.
    COMPUTER NETWORKS, 2021, 198
  • [37] A novel decentralized security architecture against sybil attack in RPL-based IoT networks: a focus on smart home use case
    A. O. Bang
    Udai Pratap Rao
    The Journal of Supercomputing, 2021, 77 : 13703 - 13738
  • [38] A novel decentralized security architecture against sybil attack in RPL-based IoT networks: a focus on smart home use case
    Bang, A. O.
    Rao, Udai Pratap
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (12): : 13703 - 13738
  • [39] Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments
    Brun, Olivier
    Yin, Yonghua
    Gelenbe, Erol
    15TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC 2018) / THE 13TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC-2018) / AFFILIATED WORKSHOPS, 2018, 134 : 458 - 463
  • [40] Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments
    Brun, Olivier
    Yin, Yonghua
    Gelenbe, Erol
    Kadioglu, Y. Murat
    Augusto-Gonzalez, Javier
    Ramos, Manuel
    SECURITY IN COMPUTER AND INFORMATION SCIENCES, EURO-CYBERSEC 2018, 2018, 821 : 79 - 89