The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time, and in any place, to each other and to the Internet. One of the major problems associated with the IoT is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly, in the areas of security and privacy. Specifically, security testing and analysis of IoT devices is considered a very complex task, as different security testing methodologies, including software and hardware security testing approaches, are needed. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed is aimed at testing all types of IoT devices, with different software/hardware configurations, by performing standard and advanced security testing. Advanced analysis processes based on machine learning algorithms are employed in the testbed in order to monitor the overall operation of the IoT device under test. The architectural design of the proposed security testbed along with a detailed description of the testbed implementation is discussed. The testbed operation is demonstrated on different IoT devices using several specific IoT testing scenarios. The results obtained demonstrate that the testbed is effective at detecting vulnerabilities and compromised IoT devices.

Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources.

Considering the exponential increase of Internet of Things (IoT) devices there is also unforeseen vulnerabilities associated with these IoT devices. One of the major problems in the IoT is the security testing and analysis due to the heterogeneous nature of deployments. Currently, there is no mechanism that performs security testing for IoT devices in different contexts. In addition, there is a missing framework to be able to adapt and tune accordingly with various security testing perspectives. In this paper, we propose an innovative security testbed targeted at IoT devices and also briefly introduce Adaptable and Tunable Framework (ATF) for testing IoT devices.

The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time and in any place, to each other and to the Internet. One of the major problems associated with the IoT is maintaining security; the heterogeneous nature of such deployments poses a challenge to many aspects of security, including security testing and analysis. In addition, there is no existing mechanism that performs security testing for IoT devices in different contexts. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed supports both standard and context-based security testing, with a set of security tests conducted under the different environmental conditions in which IoT devices operate. The requirements and architectural design of the proposed testbed are discussed, and the testbed operation is demonstrated in several testing scenarios.