Access Point Attacks Research Articles

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Eliminating Rogue Access Point Attacks in IoT: A Deep Learning Approach With Physical-Layer Feature Purification and Device Identification

Automated Fake Access Point Attack Detection and Prevention System with IoT Devices

Wireless access points (APs), which allow many devices to be easily connected to the Internet, are widely used today because they offer the easiest way to connect to the Internet. With the development of the concept of Internet of Things (IoT), WiFi networks are widely used in our homes, workplaces, social areas, campus areas. With the increase of WiFi networks, attacks on these networks are constantly increasing. In this study, an IoT-based approach to detect and prevent Fake Access point attacks frequently seen in WiFi networks is proposed. A Single Board Computer (SBC) and a wireless antenna in the "Soft AP" feature are used for operation. Fake APs were detected by air scanning. In the first phase of the study, fake Access point broadcasts have been created which can create security weakness. In order to determine the fake Access points created in the second stage, SBC and wifi module were used to scan air. In the final stage, the mac address of the fake AP has been assigned to an unauthorized Virtual Local Area Network (vLAN) on the network to prevent detected fake AP broadcasts. The possible attack methods for the study were implemented and it was revealed that the proposed approach prevented the attack successfully in all scenarios. The study is seen as an effective, developed and economically useful IoT application for network administrators to prevent the attack using fake Access point.

A Position Self-Adaptive Method to Detect Fake Access Points

In recent years, with the maturity and popularity of Wi-Fi technology, wireless hotspots have been deployed on a large scale in public places. But at the same time, it brings many security issues that cannot be ignored. Among them, the fake access point attack is a very serious threat in wireless local area network. In this paper, we propose a method to detect fake access points in wireless local area network. First, our detection method is passive, which means there is almost no additional traffic will be generated during the program’s operation. Second, different from many existing methods, our method allows the detection device to change position, the move will be perceived and the fingerprint will be updated automatically. Third, we use a variety of features as fingerprints to describe an access point better and improve efficiency. At last, the method we propose is more in line with the actual scene and has been proved effective by experiments.

Overcoming Security Vulnerabilities in Deep Learning--based Indoor Localization Frameworks on Mobile Devices

Indoor localization is an emerging application domain for the navigation and tracking of people and assets. Ubiquitously available Wi-Fi signals have enabled low-cost fingerprinting-based localization solutions. Further, the rapid growth in mobile hardware capability now allows high-accuracy deep learning--based frameworks to be executed locally on mobile devices in an energy-efficient manner. However, existing deep learning--based indoor localization solutions are vulnerable to access point (AP) attacks. This article presents an analysis into the vulnerability of a convolutional neural network--based indoor localization solution to AP security compromises. Based on this analysis, we propose a novel methodology to maintain indoor localization accuracy, even in the presence of AP attacks. The proposed secured neural network framework (S-CNNLOC) is validated across a benchmark suite of paths and is found to deliver up to 10× more resiliency to malicious AP attacks compared to its unsecured counterpart.

PRAPD: A novel received signal strength–based approach for practical rogue access point detection

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.

A Basic Probability Assignment Methodology for Unsupervised Wireless Intrusion Detection

The broadcast nature of wireless local area networks has made them prone to several types of wireless injection attacks, such as Man-in-the-Middle (MitM) at the physical layer, deauthentication, and rogue access point attacks. The implementation of novel intrusion detection systems (IDSs) is fundamental to provide stronger protection against these wireless injection attacks. Since most attacks manifest themselves through different metrics, current IDSs should leverage a cross-layer approach to help toward improving the detection accuracy. The data fusion technique based on the Dempster–Shafer (D-S) theory has been proven to be an efficient technique to implement the cross-layer metric approach. However, the dynamic generation of the basic probability assignment (BPA) values used by D-S is still an open research problem. In this paper, we propose a novel unsupervised methodology to dynamically generate the BPA values, based on both the Gaussian and exponential probability density functions, the categorical probability mass function, and the local reachability density. Then, D-S is used to fuse the BPA values to classify whether the Wi-Fi frame is normal (i.e., non-malicious) or malicious. The proposed methodology provides 100% true positive rate (TPR) and 4.23% false positive rate (FPR) for the MitM attack and 100% TPR and 2.44% FPR for the deauthentication attack, which confirm the efficiency of the dynamic BPA generation methodology.

Identity Based Replicated Node Detection and Localization in Wireless Network

Wireless networks are more prone towards identity-based attacks, especially the spoofing attacks. These spoofing attacks can further cause many other forms of attacks such as rouge access point attack and denial of service attacks (DOS). Hence the performance of the network is significantly impacted by them. Identity of the node can be verified through cryptographic authentication; however the conventional security approaches are not always possible because it requires infrastructural overhead and key management. In this paper we propose a method for detecting the spoofing attacks. This method utilizes MD5 (Message Digest 5) algorithm, which generates unique identification keys for all the nodes in the network. These unique keys are used as a basis for detecting the attacks and determining the actual number of attackers in the network and to localize these adversaries. Results of the project show that, the proposed method effectively detects the attack and locates the position of the adversaries performing the attack. There by it provides strong evidence to the effectiveness of the proposed method. Keywords: Wireless network security, spoofing attack, Message Digest 5(MD 5), attack detection, localization.

Wi-Fi Networks Security and Accessing Control

As wireless networks access gains popularity in corporate, private and personal networks, the nature of wireless networks opens up new possibilities for network attacks. This paper negotiating Wi-Fi security against scanning of rogue Wi-Fi networks and other related activities and considers the monitoring of Wi-Fi traffic effects. The unauthorized access point (AP) problem has raised more attention and resulted in obtaining wireless access without subscriber permission. This work assumes Wi-Fi AP under attack specially rogue AP and/or ad-hoc client. It provides a solution for detecting and preventing this attack. In addition, it provides the required user permissions to allow/block access of the files on the user of ad-hoc client. The experiments include the rogue AP attack are maintained and the effectiveness of the proposed solution are tested.