Biblio

Password-based authentication system has been praised for its user-friendly, cost-effective, and easily deployable features. It is arguably the most commonly used security mechanism for various resources, services, and applications. On the other hand, it has well-known security flaws, including vulnerability to guessing attacks. Present state-of-the-art approaches have high overheads, as well as difficulties and unreliability during training, resulting in a poor user experience and a high false positive rate. As a result, a lightweight authentication compromise detection model that can make accurate detection with a low false positive rate is required.In this paper we propose – LOG-OFF – a behavior-based authentication compromise detection model. LOG-OFF is a lightweight model that can be deployed efficiently in practice because it does not include a labeled dataset. Based on the assumption that the behavioral pattern of a specific user does not suddenly change, we study the real-world authentication traffic data. The dataset contains more than 4 million records. We use two features to model the user behaviors, i.e., consecutive failures and login time, and develop a novel approach. LOG-OFF learns from the historical user behaviors to construct user profiles and makes probabilistic predictions of future login attempts for authentication compromise detection. LOG-OFF has a low false positive rate and latency, making it suitable for real-world deployment. In addition, it can also evolve with time and make more accurate detection as more data is being collected.

One of the major issues which are hindering widespread and seamless adoption of Internet of Thing (IoT) is security. The IoT devices are vulnerable and susceptible to attacks which became evident from a series of recent large-scale distributed denial-of-service (DDoS) attacks, leading to substantial business and financial losses. Furthermore, in order to find vulnerabilities in IoT, there is a lack of comprehensive security analysis framework. In this paper, we present a modular, adaptable and tunable framework, called PIT, to probe IoT systems at different layers of design and implementation. PIT consists of several security analysis engines, viz., penetration testing, fuzzing, static analysis, and dynamic analysis and an exploitation engine to discover multiple IoT vulnerabilities, respectively. We also develop a novel grey-box fuzzer, called Applica, as a part of the fuzzing engine to overcome the limitations of the present day fuzzers. The proposed framework has been evaluated on a real-world IoT testbed comprising of the state-of-the-art devices. We discovered several network and system-level vulnerabilities such as Buffer Overflow, Denial-of-Service, SQL Injection, etc., and successfully exploited them to demonstrate the presence of security loopholes in the IoT devices.

The exponential increase of Internet of Things (IoT) devices have resulted in a range of new and unanticipated vulnerabilities associated with their use. IoT devices from smart homes to smart enterprises can easily be compromised. One of the major problems associated with the IoT is maintaining security; the vulnerable nature of IoT devices poses a challenge to many aspects of security, including security testing and analysis. It is trivial to perform the security analysis for IoT devices to understand the loop holes and very nature of the devices itself. Given these issues, there has been less emphasis on security testing and analysis of the IoT. In this paper, we show our preliminary efforts in the area of security analysis for IoT devices and introduce a security IoT testbed for performing security analysis. We also discuss the necessary design, requirements and the architecture to support our security analysis conducted via the proposed testbed.