Analysis of the Impact of AuthRF and AssRF Attacks on IEEE 802.11e-based Access Point

Bogdanoski, Mitko; Latkoski, Pero; Risteski, Aleksandar

doi:10.1007/s11036-016-0753-1

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The obtained simulation results for average aggregated system throughput, as well as average multimedia secure access probability values for different 5G and heterogeneous mobile and wireless broadband network conditions (with different security threats per different RATs), are presented furthermore. The jamming and DDoS attacks and the effects of these attacks are simulated in OPNET Modeler environment [40] [47][48][49][50][51][52]. The performance of the proposed ASECQUA framework is evaluated through simulations done in both MATLAB and OPNET.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Future 5G Mobile Broadband Networks Using Cloud-based Services with Advanced Security and QoS Framework

Bogdanoski¹,

Shuminoski²,

Hadji-Janev³

et al. 2020

ACTA POLYTECH HUNG

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Section: Simulation Results and Analysismentioning

confidence: 99%

Future 5G Mobile Broadband Networks Using Cloud-based Services with Advanced Security and QoS Framework

Bogdanoski¹,

Shuminoski²,

Hadji-Janev³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

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“…Moreover, it is known that wireless networks are far more vulnerable to cyber-attacks than wired networks. [1]. Due to the susceptible nature of wireless networks, we believe that an automated video surveillance framework would be adversely affected if its associated wireless network was attacked, especially in a stealthy manner.…”

Section: Methodsmentioning

confidence: 99%

“…1. We targeted our system via a Wi-Fi deauthentication attack and an ARP spoofing attack using the Evil Limiter software 1 . By sending deauthentication packets, an attacker can potentially target the communication between clients and routers, which can result in clients experiencing connection outages.…”

Section: Methodsmentioning

confidence: 99%

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Mumcu¹,

Doshi²,

Yılmaz³

2022

Preprint

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Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on adversarial machine learning targeting video understanding models and no previous work which focuses on video anomaly detection. To this end, we investigate an adversarial machine learning attack against video anomaly detection systems, that can be implemented via an easy-to-perform cyber-attack. Since surveillance cameras are usually connected to the server running the anomaly detection model through a wireless network, they are prone to cyber-attacks targeting the wireless connection. We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems. Specifically, we apply several effects caused by the Wi-Fi deauthentication attack on video quality (e.g., slow down, freeze, fast forward, low resolution) to the popular benchmark datasets for video anomaly detection. Our experiments with several state-of-the-art anomaly detection models show that the attackers can significantly undermine the reliability of video anomaly detection systems by causing frequent false alarms and hiding physical anomalies from the surveillance system.

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“…It is possible mainly because of wireless transmission media. It is proven to be far more vulnerable than traditional wired networks [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deauthentication Attack on IEEE 802.11 Connectivity Based on IoT Technology Using External Penetration Test

Kristiyanto

Ernastuti

2020

CommIT (Communication and Information Technology) Journal

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The research aims to know the level of security of WiFi connectivity against deauthentication attacks on Internet of Things (IoT)-based devices. It is done through testing using an external penetration test method. The external penetration test simulates a real external attack without information about the target system and network given. The process starts from accessing the device through Internet or WiFi by the test target. At the same time, the attacker performs Denial-of-Service (DoS) attacks onWiFi. The attacker uses Arduino ESP8266 NodeMCU WiFi with Lua programming. To record WiFi activities, the researchers use CommView for WiFi V. 7.0, and the target is Internet Protocol (IP) camera device. The result shows that the communication of the test target with the gateway is lost, but the Media Access Control (MAC) of the test target is still registered at the gateway. Deauthentication attacks cause communication paralysis, and several changes occur, such as an increase in data rate, and change in frequency channel, Distribution System (DS) status, retry bits in frame management, and the sequence number.

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Analysis of the Impact of AuthRF and AssRF Attacks on IEEE 802.11e-based Access Point

Cited by 4 publications

References 9 publications

Future 5G Mobile Broadband Networks Using Cloud-based Services with Advanced Security and QoS Framework

Future 5G Mobile Broadband Networks Using Cloud-based Services with Advanced Security and QoS Framework

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

Analysis of Deauthentication Attack on IEEE 802.11 Connectivity Based on IoT Technology Using External Penetration Test

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