返回
Machine learning-based jamming attack classification and effective defense technique
DOI:10.1016/j.cose.2023.103169.png)
摘要
En 中文
The fourth industrial revolution has resulted in the intelligent Internet of Things being widely used for home networking applications and smart infrastructure. Consequently, wireless connectivity has become essential in industrial and daily-life applications. Wireless communication is a continuously evolving tech-nology that satisfies high-speed and ultra-low latency requirements. However, as multiple users utilize a single channel by sharing frequency and time, the service quality cannot be ensured owing to the in-terference from a congested network. Additionally, malicious attackers can compromise communication availability or destroy data integrity through jamming attacks, threatening human life and safety. Con-ventional jamming attack detection and response technology respond to attacks without detecting the type of jammer, exhibiting certain limitations in detecting and defending against an intelligent attack. This study proposes a novel jammer classification and effective defense (JCED) algorithm that can classify jamming attack types using machine learning (ML) and provide differential responses based on the jam-ming types. Depending on the jammer type, the JCED algorithm can adaptively select various response methods, ranging from simple retransmission to active battery-draining attacks. The experimental results verify that JCED exhibits 24.9% higher effective throughput and 23.4% lower energy consumption than the countermeasure detection and consistency algorithm (CDCA). Moreover, JCED improves the effective throughput by an average of approximately three times compared to CDCA in an environment with in-tegrity violation attacks. Thus, the JCED is an effective def ense mechanism against jamming attacks, en-suring digital information safety and high throughput.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Wi-Fi
Jamming attack
Jammer classification
Jammer defense
Basic service set coloring
Battery draining
期刊
C
IF:
5.4
论文数:
4.6K
被引数:
1.4W
机构
暂无机构信息
引用论文
Battery Draining Attack and Defense against Power Saving Wireless LAN Devices针对节能无线局域网设备的电池耗尽攻击和防御
SENSORS
IF3.5
A novel countermeasure technique for reactive jamming attack in internet of things一种新型的物联网反应式干扰攻击对抗技术
Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks
SUSTAINABILITY
IF3.3
An Intelligent Anti-Jamming Scheme for Cognitive Radio Based on Deep Reinforcement Learning基于深度强化学习的认知无线电智能抗干扰方案
IEEE ACCESS
IF3.6
Secure Visible Light Communication System via Cooperative Attack Detecting Techniques
IEEE ACCESS
IF3.6
Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach
IEEE ACCESS
IF3.6

