Biblio
In cyberspace, a digital signature is a mathematical technique that plays a significant role, especially in validating the authenticity of digital messages, emails, or documents. Furthermore, the digital signature mechanism allows the recipient to trust the authenticity of the received message that is coming from the said sender and that the message was not altered in transit. Moreover, a digital signature provides a solution to the problems of tampering and impersonation in digital communications. In a real-life example, it is equivalent to a handwritten signature or stamp seal, but it offers more security. This paper proposes a scheme to enable users to digitally sign their communications by validating their identity through users’ mobile devices. This is done by utilizing the user’s ambient Wi-Fi-enabled devices. Moreover, the proposed scheme depends on something that a user possesses (i.e., Wi-Fi-enabled devices), and something that is in the user’s environment (i.e., ambient Wi-Fi access points) where the validation process is implemented, in a way that requires no effort from users and removes the "weak link" from the validation process. The proposed scheme was experimentally examined.
With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy.
We use model-based testing techniques to detect logical vulnerabilities in implementations of the Wi-Fi handshake. This reveals new fingerprinting techniques, multiple downgrade attacks, and Denial of Service (DoS) vulnerabilities. Stations use the Wi-Fi handshake to securely connect with wireless networks. In this handshake, mutually supported capabilities are determined, and fresh pairwise keys are negotiated. As a result, a proper implementation of the Wi-Fi handshake is essential in protecting all subsequent traffic. To detect the presence of erroneous behaviour, we propose a model-based technique that generates a set of representative test cases. These tests cover all states of the Wi-Fi handshake, and explore various edge cases in each state. We then treat the implementation under test as a black box, and execute all generated tests. Determining whether a failed test introduces a security weakness is done manually. We tested 12 implementations using this approach, and discovered irregularities in all of them. Our findings include fingerprinting mechanisms, DoS attacks, and downgrade attacks where an adversary can force usage of the insecure WPA-TKIP cipher. Finally, we explain how one of our downgrade attacks highlights incorrect claims made in the 802.11 standard.
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.
Opportunistic Networks are delay-tolerant mobile networks with intermittent node contacts in which data is transferred with the store-carry-forward principle. Owners of smartphones and smart objects form such networks due to their social behaviour. Opportunistic Networking can be used in remote areas with no access to the Internet, to establish communication after disasters, in emergency situations or to bypass censorship, but also in parallel to familiar networking. In this work, we create a mobile network application that connects Android devices over Wi-Fi, offers identification and encryption, and gathers information for routing in the network. The network application is constructed in such a way that third party applications can use the network application as network layer to send and receive data packets. We create secure and reliable connections while maintaining a high transmission speed, and with the gathered information about the network we offer knowledge for state of the art routing protocols. We conduct tests on connectivity, transmission range and speed, battery life and encryption speed and show a proof of concept for routing in the network.