Empirical Techniques To Detect Rogue Wireless Devices

Media Access Control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. We proposed a solution to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the Received Signal Strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a Wireless Local Area Network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16%, and 88.38% accuracy when the attacker is 8–13 m, 4–8 m, and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as Random Forests. We also proposed an anomaly detection solution to deal with situations where it is impossible to cover the whole intended area. The solution is totally passive and unsupervised (using unlabeled data points) to build the profile of the legitimate device. It only requires the training of one location which is the location of the legitimate device (unlike the misuse detection solution that train and simulate the existing of the attacker in every possible spot in the network diameter). The solution was tested in the same test-bed and yield about 79% overall accuracy. We build a misuseWireless Local Area Network Intrusion Detection System (WIDS) and discover some important fields in WLAN MAC-layer frame to differentiate the attackers from the legitimate devices. We tested several machine learning algorithms and found some promising ones to improve the accuracy and computation time on a public dataset. The best performing algorithms that we found are Extra Trees, Random Forests, and Bagging. We then used a majority voting technique to vote on these algorithms. Bagging classifier and our customized voting technique have good results (about 96.25 % and 96.32 %respectively) when tested on all the features. We also used a data mining technique based on Extra Trees ensemble method to find the most important features on AWID public dataset. After selecting the most 20 important features, Extra Trees and our voting technique are the best performing classifiers in term of accuracy (96.31 % and 96.32 % respectively)

Time of Flight and Fingerprinting Based Methods for Wireless Rogue Device Detection

Existing network detection techniques rely on SSIDs, network patterns or MAC addresses of genuine wireless devices to identify malicious attacks on the network. However, these device characteristics can be manipulated posing a security threat to information integrity, lowering detection accuracy, and weakening device protection. This research study focuses on empirical analysis to elaborate the relationship between received signal strength (RSSI) and distance; investigates methods to detect rogue devices and access points on Wi-Fi networks using network traffic analysis and fingerprint identification methods. In this paper, we conducted three experiments to evaluate the performance of RSSI and clock skews as features to detect rogue devices for indoor and outdoor locations. Results from the experiments suggest different devices connected to the same access point can be detected (p \u3c 0.05) using RSSI values. However, the magnitude of the difference was not consistent as devices were placed further from the same access point. Therefore, an optimal distance for maximizing the detection rate requires further examination. The random forest classifier provided the best performance with a mean accuracy of 79% across all distances. Our experiment on clock skew shows improved accuracy in using beacon timestamps to detect rogue APs on the network

Darma: Defeating And Reconnaissance Manna-Karma Attacks In 802.11 With Multiple Detections And Prevention

The vast growing usage of mobile phones increases Wi-Fi technology. At present, the pattern of human interaction with the internet is not a desktop or laptop anymore. The assimilation of tools for surfing, working, and communication is now shifting to mobile phones. Thus, this is the motivation to expand Wi-Fi technology so that it will be the primary medium for internet connectivity. Hence, increasing the security risk for it attracts attackers despite its popularity among users. The DOS attack in 802.11 management frames is widely known as an initial process before Man-in-the-middle (MiTM) attacks in 802.11 takes part. Karma and Manna's attacks are an unprecedented attack in the 802.11 management frames. This paper proposed a mechanism called Defeating and Reconnaissance Manna-karma Attack (DARMA), which is client-side multiple detection techniques to defeat and prevent karma-manna attack. The proposed mechanism consisted of 4 layers of processes inclusive of monitors, detection, confirmation, and preventions. The effectiveness of the detection is base of the current real-time behaviour of the packets

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

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Wireless and Mobile Computing of the Nelson Mandela African Institution of Science and TechnologyNetwork 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

A New MAC Address Spoofing Detection Technique Based on Random Forests

Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests.https://doi.org/10.3390/s1603028

Development of a Client-Side Evil Twin Attack Detection System for Public Wi-Fi Hotspots based on Design Science Approach

Users and providers benefit considerably from public Wi-Fi hotspots. Users receive wireless Internet access and providers draw new prospective customers. While users are able to enjoy the ease of Wi-Fi Internet hotspot networks in public more conveniently, they are more susceptible to a particular type of fraud and identify theft, referred to as evil twin attack (ETA). Through setting up an ETA, an attacker can intercept sensitive data such as passwords or credit card information by snooping into the communication links. Since the objective of free open (unencrypted) public Wi-Fi hotspots is to provide ease of accessibility and to entice customers, no security mechanisms are in place. The public’s lack of awareness of the security threat posed by free open public Wi-Fi hotspots makes this problem even more heinous. Client-side systems to help wireless users detect and protect themselves from evil twin attacks in public Wi-Fi hotspots are in great need. In this dissertation report, the author explored the problem of the need for client-side detection systems that will allow wireless users to help protect their data from evil twin attacks while using free open public Wi-Fi. The client-side evil twin attack detection system constructed as part of this dissertation linked the gap between the need for wireless security in free open public Wi-Fi hotspots and limitations in existing client-side evil twin attack detection solutions. Based on design science research (DSR) literature, Hevner’s seven guidelines of DSR, Peffer’s design science research methodology (DSRM), Gregor’s IS design theory, and Hossen & Wenyuan’s (2014) study evaluation methodology, the author developed design principles, procedures and specifications to guide the construction, implementation, and evaluation of a prototype client-side evil twin attack detection artifact. The client-side evil twin attack detection system was evaluated in a hotel public Wi-Fi environment. The goal of this research was to develop a more effective, efficient, and practical client-side detection system for wireless users to independently detect and protect themselves from mobile evil twin attacks while using free open public Wi-Fi hotspots. The experimental results showed that client-side evil twin attack detection system can effectively detect and protect users from mobile evil twin AP attacks in public Wi-Fi hotspots in various real-world scenarios despite time delay caused by many factors

DRET:a system for detecting evil-twin attacks in smart homes

Evil-twin is one of most commonly attacks in the WIFI environments, with which an attacker can steal sensitive information by cloning a fake AP in Smart Homes. The current approaches of detecting Evil-twin AP uses some identities/fingerprints of legitimated APs to identify rouge APs. Prior work in the area uses information like SSIDs, MAC addresses, and network traffics to detect bogus APs. However, such information can be easily intimated by the attacker, leading to low detection rates. This paper introduces a novel Evil-Twin AP detection method based on received signal strength indicator (RSSI). Our approach exploits the fact that the AP location is relatively stable in Smart Homes, which is to great extent to meet the requirement of the detection factor not easy to imitate as previous refer. We employ two detection strategies: a single position detection and a multi-positioned detection methods. Our approach exploits the multipath effect of WIFI signals to translate the problem of attack detection into AP positioning detection. Compared to classical detection methods, our approach can perform detection without relying on professional devices. Experimental results show that the single position detection approach achieves 20 seconds’ reduction of delay time with an accuracy of 98%, whereas the multi-positioned detection approach achieves 90% correct