Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

Abstract

IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classificationbased methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today's real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection.

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

“…For the frame type, we only took the DATA, CMD, ACK, and BCN frames. It is important to note that the not-included frame type 1_OCT_HEADER stands for 1 Octal Header Low Latency [34], and it has a linear correlation (0.44) to the target class, but it is a trivial result since low latency packets are sent when the conditions of the network force a low response from the devices, and, therefore, packet loss is expected.…”

“…The wireless protocol IEEE 802.15.4 standard suggests [31][32][33] that every packet contains its Frame Control Type inside the packet's payload byte block. This frame type takes eight different values [34] in the captured data for the present study: 1_OCT_HEADER (1 Octet MAC Header for Low Latency) [34], CSL_WAKEUP, CSL_SECURE_ACK, and RFID_BLINK, all of which are coded to fit in 3 bits inside the payload byte block [32,35] as mentioned. The frame type is crucial since it refers to the type of command that sent a packet [34], which in turn specifies if the sender is either the collector/master (Beacon) or one of the sensor nodes.…”