An eye for deception: A case study in utilizing the human-as-a-security-sensor paradigm to detect zero-day semantic social engineering attacks

In a number of information security scenarios, human beings can be better than technical security measures at detecting threats. This is particularly the case when a threat is based on deception of the user rather than exploitation of a specific technical flaw, as is the case of spear-phishing, application spoofing, multimedia masquerading and other semantic social engineering attacks. Here, we put the concept of the humanas-a-security-sensor to the test with a first case study on a small number of participants subjected to different attacks in a controlled laboratory environment and provided with a mechanism to report these attacks if they spot them. A key challenge is to estimate the reliability of each report, which we address with a machine learning approach. For comparison, we evaluate the ability of known technical security countermeasures in detecting the same threats. This initial proof of concept study shows that the concept is viable

Predicting the performance of users as human sensors of security threats in social media

While the human as a sensor concept has been utilised extensively for the detection of threats to safety and security in physical space, especially in emergency response and crime reporting, the concept is largely unexplored in the area of cyber security. Here, we evaluate the potential of utilising users as human sensors for the detection of cyber threats, specifically on social media. For this, we have conducted an online test and accompanying questionnaire-based survey, which was taken by 4,457 users. The test included eight realistic social media scenarios (four attack and four non-attack) in the form of screenshots, which the participants were asked to categorise as “likely attack” or “likely not attack”. We present the overall performance of human sensors in our experiment for each exhibit, and also apply logistic regression and Random Forest classifiers to evaluate the feasibility of predicting that performance based on different characteristics of the participants. Such prediction would be useful where accuracy of human sensors in detecting and reporting social media security threats is important. We identify features that are good predictors of a human sensor’s performance and evaluate them in both a theoretical ideal case and two more realistic cases, the latter corresponding to limited access to a user’s characteristics

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

TAS: Risk Analysis & Clustered Sensors

This paper briefly introduces a general view on tomorrow’s border control system and EU inter-BCP real time information sharing, exploring and proposing new operational methods and solutions for border control procedures to increase the efficacy and efficiency of the whole security screening system at the same time reducing the efforts (costs/resources). The general description of the system logic and architecture introduces the core of the solution, the Trust Assessment System. A “black box” based on risk analysis and advanced machine learning algorithms aimed to assign a Traveller Trust Score to each single individual intentioned to cross the border. Main benefits are: improved checkpoint throughput, improved situational awareness and level of security, better traveller experience, optimisation of resources. The concept is that the traveller risk evaluation starts as soon as she/he applies for a visa, a passport or books a trip by whatever means of transport

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article

The threat nets approach to information system security risk analysis

The growing demand for healthcare services is motivating hospitals to strengthen outpatient case management using information systems in order to serve more patients using the available resources. Though the use of information systems in outpatient case management raises patient data security concerns, it was established that the current approaches to information systems risk analysis do not provide logical recipes for quantifying threat impact and determining the cost-effectiveness of risk mitigation controls. Quantifying the likelihood of the threat and determining its potential impact is key in deciding whether to adopt a given information system or not. Therefore, this thesis proposes the Threat Nets Approach organized into 4 service recipes, namely: threat likelihood assessment service, threat impact evaluation service, return on investment assessment service and coordination management. The threat likelihood assessment service offers recipes for determining the likelihood of a threat. The threat impact evaluation service offers techniques of computing the impact of the threat on the organization. The return on investment assessment service offers recipes of determining the cost-effectiveness of threat mitigation controls. To support the application of the approach, a ThreNet tool was developed. The approach was evaluated by experts to ascertain its usability and usefulness. Evaluation of the Threat Nets Approach by the experts shows that it provides complete, usable and useful recipes for the assessment of; threat likelihood, threat impact and cost-effectiveness of threat mitigation controls. The results suggest that the application of Threat Nets approach is effective in quantifying risks to information system