In the twenty-first century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks with automated decision making and sophisticated technology, no doubt we feel much more secure than half a century ago. As the technological advancement takes root, so does the maturity of security threats. It is common that today’s critical infrastructures are operated by non-computer experts, e.g. nurses in health care, soldiers in military or firefighters in emergency services. In such challenging applications, protecting against insider attacks is often neither feasible nor economically possible, but these threats can be managed using suitable risk management strategies. Security technologies, e.g. firewalls, help protect data assets and computer systems against unauthorised entry. However, one area which is often largely ignored is the human factor of system security. Through social engineering techniques, malicious attackers are able to breach organisational security via people interactions. This paper presents a security awareness training framework, which can be used to train operators of critical infrastructure, on various social engineering security threats such as spear phishing, baiting, pretexting, among others.

For years governments, organizations and companies have made great efforts to keep hackers, malware, cyber attacks at bay with different degrees of success. On the other hand, cyber criminals and miscreants produced more advanced techniques to compromise Internet infrastructure. Targeted attack or advanced persistent threat (APT) attack is a new challenge and aims to accomplish a specific goal, most often espionage. APTs are presently the biggest threat to governments and organizations. This paper states research questions and propose a novel approach to intrusion detection system processes network traffic and able to detect potential APT attack. This detection of APT attack is based on the correlation between the events which we get as outputs of our detection methods. Each detection method aims to detect one technique used in one of APT attack steps.

In this work, we compare the pulse-shape discrimination (PSD) properties of newly developed liquid scintillator LSB-200, EJ-299-33A, BC-501A, stilbene, p-terphenyl, and Hidex Aqualight in neutron field generated by the LVR-15 reactor with silicon filter utilization. Pulses from the scintillators are processed by the Neutron-Gamma spectrometer. This spectrometric system with fast digitizer card contains two analog–digital converters with a resolution of 12 bits and sampling frequency 500 × 106 Hz. For photomultiplier linearity improvement, the active divider has been used. Measured data from scintillators have been processed using the integration method and compared. The results are presented.

The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness.