Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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 classification-based 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

Deauthentication and disassociation detection and mitigation scheme using artificial neural network

Wireless local area networks (WLAN) are increasingly deployed and widespread worldwide due to the convenience and the low cost that characterized it. However, due to the broadcasting and the shared nature of the wireless medium, WLANs are vulnerable to many kinds of attacks. Although there are many efforts to improve the security of a wireless network, some attacks are inevitable. Attackers can send fake de-authentication or disassociation frames to end the session a victim leading to a denial of service, stolen passwords, and leaks of sensitive information among many other cybercrimes. Effectively detecting such attacks is crucial in today’s critical applications. However, the extant security standards are vulnerable to such an attack, and it is still an open research problem. In this paper, a scheme called D3MS is proposed to detect and mitigate de-authentication and disassociation attack effectively. The aim is to construct a model that can distinguish between benign and fake frames by recognizing the normal behavior of the wireless station before sending the authentication and de-authentication frames. The hypothesis is that the emulating the normal behavior of a benign station prior to the authentication and de-authentication attack is useless. The experimentation results showed the effectiveness of the proposed detection technique. The proposed scheme has improved the detection performance by 64.4% comparing to the related work

A multinational cross-sectional study on the awareness and concerns of healthcare providers toward monkeypox and the promotion of the monkeypox vaccination

BackgroundThe aim of this study was to explore potential healthcare workers' (HCWs) concerns about the monkeypox virus in order to create practical solutions to manage this disease.MethodsOnline cross-sectional research was conducted in 11 Arabic countries (Egypt, Saudi Arabia, Yemen, Syria, Libya, Algeria, Tunisia, Iraq, Palestine, Jordan, and Sudan) from 2 August 2022 to 28 December 2022.ResultsApproximately 82% of respondents felt the need to acquire further information. The acceptability of the vaccine against monkeypox has been indicated by more than half of the participants (54.5%). Furthermore, we state that 45% of the participants are knowledgeable about the monkeypox virus, and 53.1% of the participants have never been affected with COVID-19 before are more worried about COVID-19 than about monkeypox. Participants diagnosed with COVID-19 were 0.63 times less likely to worry about monkeypox than those who were not diagnosed with COVID-19. A greater willingness to get the monkeypox vaccination was seen among the age group 21–30 years (42.4%) compared to the other age groups.ConclusionMost healthcare professionals have a moderate knowledge of the monkeypox virus. Furthermore, they demonstrated a low willingness to get the vaccination against the monkeypox virus