Securing Data in Power-Limited Sensor Networks Using Two-Channel Communications

Wolfe, Clark L.; Graham, Steve; Mills, Robert F.; Nykl, Scott; Simon, Paul

doi:10.1007/978-3-030-04537-1_5

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Algorithm 1 Login automation on the fake captive portal Input: username ← randomString (10) Input: password ← randomString (10) 1: Load captive portal URL 2: if pageLoaded then…”

Section: Network With Captive Portalmentioning

confidence: 99%

“…On the other hand, we looked at how the number of rounds could affect the performance of the detection prototype. We tested the prototype by collecting AP details in two (2), ten (10), and twenty (20) and fifty (50) rounds. The lower number of rounds would reduce the time taken for scanning and detection (better speed), however, it would affect the detection accuracy.…”

Section: Testingmentioning

confidence: 99%

“…The sender or receiver address of a successful spoofing attack is disguised VOLUME 4, 2016 to appear legitimate. As a result, the receiver does not notice the sender's true address, and the sender sends packets to a bogus or spoofed address [10]. An attack occurs when an attacker successfully creates illegitimate Wi-Fi APs in wireless networks, and a user connects to it.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mwinuka

Agghey²,

Kaijage

et al. 2022

IEEE Access

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Network spoofing is becoming a common attack in wireless networks. The trend is going high due to an increase in Internet users. Similarly, there is a rapid growth of numbers in mobile devices in the working environments and on most official occasions. The trends pose a huge threat to users since they become the prime target of attackers. More unfortunately, mobile devices have weak security measures due to their limited computational powers. Current approaches to detect spoofing attacks focus on personal computers and rely on the network hosts' capacity, leaving guest users with mobile devices at risk. Some approaches on Android-based devices demand root privilege, which is highly discouraged. This paper presents an Android-based client-side solution to detect the presence of fake access points in a perimeter using details collected from probe responses. Our approach considers the difference in security information and signal level of an access point (AP). We present the detection in three networks, (i) open networks, (ii) closed networks and (iii) networks with captive portals. As a departure from existing works, our solution does not require root access for detection, and it is developed for portability and better performance. Experimental results show that our approach can detect fake access points with an accuracy of 99% and 99.7% at an average of 24.64 and 7.78 milliseconds in open and closed networks, respectively.

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“…Algorithm 1 Login automation on the fake captive portal Input: username ← randomString (10) Input: password ← randomString (10) 1: Load captive portal URL 2: if pageLoaded then…”

Section: Network With Captive Portalmentioning

confidence: 99%

Section: Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mwinuka

Agghey²,

Kaijage

et al. 2022

IEEE Access

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“…A successful spoofing attack's sender or receiver address is disguised to appear legitimate. As a result, the receiver does not notice the sender's valid address, and the sender sends packets to a bogus or spoofed address (Wolfe et al, 2018). An attack occurs when an attacker successfully creates illegitimate Wi-Fi APs in wireless networks, and a user connects to it.…”

mentioning

confidence: 99%

User-side wi-fi hotspot spoofing detection on android-based devices

Mwinuka

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Network spoofing is becoming a common attack in wireless networks. Similarly, there is a rapid growth of numbers in mobile devices in the working environments. The trends pose a huge threat to users since they become the prime target of attackers. More unfortunately, mobile devices have weak security measures due to their limited computational powers, making them an easy target for attackers. Current approaches to detect spoofing attacks focus on personal computers and rely on the network hosts’ capacity, leaving users with mobile devices at risk. Furthermore, some approaches on Android-based devices demand root privilege, which is highly discouraged. This research aims to study users' susceptibility to network spoofing attacks and propose a detection solution in Android-based devices. The presented approach considers the difference in security information and signal levels of an access point to determine its legitimacy. On the other hand, it tests the legitimacy of the captive portal with fake login credentials since, usually, fake captive portals do not authenticate users. The detection approaches are presented in three networks: (a) open networks, (b) closed networks and (c) networks with captive portals. As a departure from existing works, this solution does not require root access for detection, and it is developed for portability and better performance. Experimental results show that this approach can detect fake access points with an accuracy of 98% and 99% at an average of 24.64 and 7.78 milliseconds in open and closed networks, respectively. On the other hand, it can detect the existence of a fake captive portal at an accuracy of 88%. Despite achieving this performance, the presented detection approach does not cover APs that do not mimic legitimate APs. As an improvement, future work may focus on pcap files which is rich of information to be used in detection.

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