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      KCI등재 SCOPUS

      A Cooperative Smart Jamming Attack in Internet of Things Networks

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      https://www.riss.kr/link?id=A108394600

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.
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      The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect per...

      The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.

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      참고문헌 (Reference)

      1 P. Koloveas, "inTIME: A machine learning-based framework for gathering and leveraging web data to cyber-threat intelligence" 10 (10): 818-, 2021

      2 L. Basyoni, "Traffic analysis attacks on Tor : A survey" 183-188, 2020

      3 A. Rajan, "Sybil attack in IOT : Modelling and defenses" 2323-2327, 2017

      4 R. Baskar, "Sinkhole attack in wireless sensor networksperformance analysis and detection methods" 10 (10): 1-8, 2017

      5 I. Butun, "Security of the internet of things : Vulnerabilities, attacks, and countermeasures" 22 (22): 616-644, 2019

      6 G. Chen, "Secrecy outage analysis for downlink transmissions in the presence of randomly located eavesdroppers" 12 (12): 1195-1206, 2017

      7 G. Chen, "Physical layer network security in the full-duplex relay system" 10 (10): 574-583, 2015

      8 G. Rajendran, "Modern security threats in the Internet of Things (IoT): Attacks and countermeasures" 1-6, 2019

      9 N. Namvar, "Jamming in the internet of things : A game-theoretic perspective" 1-6, 2016

      10 M. N. Aman, "HAtt : Hybrid remote attestation for the internet of things with high availability" 7 (7): 7220-7233, 2020

      1 P. Koloveas, "inTIME: A machine learning-based framework for gathering and leveraging web data to cyber-threat intelligence" 10 (10): 818-, 2021

      2 L. Basyoni, "Traffic analysis attacks on Tor : A survey" 183-188, 2020

      3 A. Rajan, "Sybil attack in IOT : Modelling and defenses" 2323-2327, 2017

      4 R. Baskar, "Sinkhole attack in wireless sensor networksperformance analysis and detection methods" 10 (10): 1-8, 2017

      5 I. Butun, "Security of the internet of things : Vulnerabilities, attacks, and countermeasures" 22 (22): 616-644, 2019

      6 G. Chen, "Secrecy outage analysis for downlink transmissions in the presence of randomly located eavesdroppers" 12 (12): 1195-1206, 2017

      7 G. Chen, "Physical layer network security in the full-duplex relay system" 10 (10): 574-583, 2015

      8 G. Rajendran, "Modern security threats in the Internet of Things (IoT): Attacks and countermeasures" 1-6, 2019

      9 N. Namvar, "Jamming in the internet of things : A game-theoretic perspective" 1-6, 2016

      10 M. N. Aman, "HAtt : Hybrid remote attestation for the internet of things with high availability" 7 (7): 7220-7233, 2020

      11 Y. Tian, "Event-based sliding mode control under denial-of-service attacks" 65 (65): 2022

      12 P. Chen, "Dynamic event-triggered output feedback control for load frequency control in power systems with multiple cyber attacks" 52 (52): 1-13, 2022

      13 X. Jin, "Distributed adaptive security consensus control for a class of multi-agent systems under network decay and intermittent attacks" 547 : 88-102, 2021

      14 R. Ma, "Dissipativity-based sliding-mode control of cyber-physical systems under denial-of-service attacks" 51 (51): 2306-2318, 2020

      15 S. Choudhary, "Detection and prevention of routing attacks in internet of things" 1537-1540, 2018

      16 X. Huang, "Detection and isolation of false data injection attack in intelligent transportation system via robust state observer" 10 (10): 1299-, 2022

      17 M. Abdullahi, "Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review" 11 (11): 198-, 2022

      18 T. Jamal, "Denial of service attack in cooperative networks"

      19 A. Malik, "Blockchain technology-future of IoT : Including Structure, limitations and various possible attacks" 1100-1104, 2019

      20 N. Panda, "Analysis of blackhole attack in AODV and DSR" 8 (8): 3092-3102, 2018

      21 M. Jeevamaheswari, "AODV routing protocol to defence against packet dropping gray hole attack In MANET" 2018

      22 K. Zhao, "A survey on the internet of things security" 663-667, 2013

      23 N. Geethanjali, "A survey on energy depletion attacks in wireless sensor networks" 3 (3): 2070-2074, 2014

      24 M. U. Farooq, "A review on internet of things(IoT)" 113 (113): 1-7, 2015

      25 S. Deshmukh-Bhosale, "A real-time intrusion detection system for wormhole attack in the RPL based internet of things" 32 : 840-847, 2019

      26 H. Fu, "A data clustering algorithm for detecting selective forwarding attack in cluster-based wireless sensor networks" 20 (20): 23-, 2020

      27 M. Labib, "A colonel blotto game for anti-jamming in the internet of things" 1-6, 2015

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