Deep Learning for Cyber Deception in Wireless Networks
Deep Learning for Cyber Deception in Wireless Networks
复制标题
无线网络中网络欺骗的深度学习
DOI：
10.1109/msn53354.2021.00086
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Charles A. Kamhoua
中科院分区：
文献类型：
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作者：
Felix O. Olowononi;Ahmed H. Anwar;D. Rawat;Jaime C. Acosta;Charles A. Kamhoua
无线通信网络是智能系统的组成部分，其增强了人类所从事的各种活动和操作的自动化。例如，充满传感器的智能设备的开发利用了机器学习（ML）和人工智能（AI）等新兴技术，这些技术已被证明可以通过通信，控制，情报收集和态势感知来增强军事行动。然而，对网络安全的日益关注意味着攻击者总是试图利用扩大的攻击面来发动对抗性攻击，从而损害合法用户的活动。为了应对这一挑战，我们利用深度学习（DL）和网络欺骗的原理，提出了一种保护无线网络免受干扰的方法。具体来说，我们使用DL来调节分配给用户的功率和他们用来通信的信道，从而诱使干扰机攻击被认为是保证最大的损害时，攻击指定的信道。此外，通过将其能量引导到特定信道上的攻击，其他信道被释放用于实际传输，确保安全通信。通过仿真和实验，我们得出结论，这种方法提高了无线通信系统的安全性。
Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.