The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the use of a behavioural biometric based on keystroke dynamics. Although there are several implementations of keystroke dynamics available, their effectiveness is variable and dependent on the data sample and its acquisition methodology. The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed. Our results also provide evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.

The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called dynamic oriented graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary trees classifier

The specific demands of supply chains built upon large and complex IoT systems, make it a must to design a coordinated framework for cyber resilience provisioning, intended to guarantee trusted supply chains of ICT systems, built upon distributed, dynamic, potentially insecure, and heterogeneous ICT infrastructures. As such, the solution proposed in this paper is envisioned to deal with the whole supply chain system components, from the IoT ecosystem to the infrastructure connecting them, addressing security and privacy functionalities related to risks and vulnerabilities management, accountability, and mitigation strategies, as well as security metrics and evidence-based security assurance. In this paper, we present FISHY as a preliminary architecture that is designed to orchestrate existing and beyond state-of-the-art security appliances in composed ICT scenarios. To this end, the FISHY architecture leverages the capabilities of programmable networks and IT infrastructure through seamless orchestration and instantiation of novel security services, both in real-time and proactively. The paper also includes a thorough business analysis to go far beyond the technical benefits of a potential FISHY adoption, as well as three real-world use cases highlighting the envisioned benefits of a potential FISHY adoption.

This paper describes a new approach to perform segmentation of moving objects in real-time from images acquired by a fixed color video camera and is the first tool of a major project that aspires to recognize abnormal human behavior in public areas. The moving objects detection is based on background subtraction and it is unaffected by changes in illumination, i.e., cast shadows and highlights. Furthermore it does not require a special attention during the initialization process, due to its ability to detect and rectify ghosts. The results show that with image resolutions of 380/spl times/280 at 24 bits per pixel, the time spent in the segmentation process is around 80 ms, in a 32 bits 3 GHz processor based computer.