FakeAP Detector: An Android-Based Client-Side Application for Detecting Wi-Fi Hotspot Spoofing

Mwinuka, Lunodzo J.; Agghey, Abel Z.; Kaijage, Shubi; Ndibwile, Jema David

doi:10.1109/access.2022.3146802

Cited by 4 publications

(2 citation statements)

References 24 publications

(34 reference statements)

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“…The unsupervised learning algorithm performs less due to unlabeled data. The increase in over fit risk occurs when the number of observations are insufficient during execution of IDS ( De Araujo-Filho et al, 2021 ; Mwinuka et al, 2022 ). Existing IDS algorithms, needs more computation time when the number of variables is more.…”

Section: Literature Workmentioning

confidence: 99%

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination

S.,

2024

PeerJ Computer Science

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IoT-wireless sensor networks (WSN) have extensive applications in diverse fields such as battlegrounds, commercial sectors, habitat monitoring, buildings, smart homes, and traffic surveillance. WSNs are susceptible to various types of attacks, such as malicious attacks, false data injection attacks, traffic attacks, and HTTP flood attacks. CONNECT attack is a novel attack in WSN. CONNECT attack plays a crucial role through disrupting packet transmission and node connections and significantly impacts CPU performance. Detecting and preventing CONNECT attacks is imperative for enhancing WSN efficiency. During a CONNECT attack, nodes fail to respond to legitimate requests, resulting in connectivity delays, acknowledgment delays, and packet drop attacks in IoT-WSN nodes. This article introduces an Intrusion Detection Algorithm based on the Cyclic Analysis Method (CAM), which incorporates a forward selection approach and backward elimination method. CAM analyzes routing information and behavior within the WSN, facilitating the identification of malicious paths and nodes. The proposed approach aims to pinpoint and mitigate the risks associated with CONNECT attacks, emphasizing the identification of malevolent pathways and nodes while establishing multiple disjoint loop-free routes for seamless data delivery in the IoT-WSN. Furthermore, the performance of CAM is assessed based on metrics such as malicious node detection accuracy, connectivity, packet loss, and network traffic. Simulation results using Matlab software demonstrate superior accuracy in malicious node detection, achieving accuracy in attack detection of approximately 99%, surpassing traditional algorithms accuracy of attack detection.

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Section: Literature Workmentioning

confidence: 99%

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination

S.,

2024

PeerJ Computer Science

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“…Wireless fidelity (Wi-Fi) technology allows electronic devices to connect to a wireless network using radio waves. It is a popular Internet connection method, as it is widely supported on many devices, including smartphones and laptops [130]. On the other hand, Wi-Fi attacks on Android mobile devices refer to a wide range of malicious activities that target wireless network connections on these devices.…”

Section: Wi-fi Attacksmentioning

confidence: 99%

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

et al. 2023

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There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.

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Building IoT Based Wireless Network Pen-Testing Using NodeMCU

Mangal,

Sharma,

Chavar

et al. 2024

2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)

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FakeAP Detector: An Android-Based Client-Side Application for Detecting Wi-Fi Hotspot Spoofing

Cited by 4 publications

References 24 publications

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

Building IoT Based Wireless Network Pen-Testing Using NodeMCU

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