Addressing cyber and privacy risks has never been more critical for organisations. While a number of risk assessment methodologies and software tools are available, it is most often the case that one must, at least, integrate them into a holistic approach that combines several appropriate risk sources as input to risk mitigation tools. In addition, cyber risk assessment primarily investigates cyber risks as the consequence of vulnerabilities and threats that threaten assets of the investigated infrastructure. In fact, cyber risk assessment is decoupled from privacy impact assessment, which aims to detect privacy-specific threats and assess the degree of compliance with data protection legislation. Furthermore, a Privacy Impact Assessment (PIA) is conducted in a proactive manner during the design phase of a system, combining processing activities and their inter-dependencies with assets, vulnerabilities, real-time threats and Personally Identifiable Information (PII) that may occur during the dynamic life-cycle of systems. In this paper, we propose a cyber and privacy risk management toolkit, called AMBIENT (Automated Cyber and Privacy Risk Management Toolkit) that addresses the above challenges by implementing and integrating three distinct software tools. AMBIENT not only assesses cyber and privacy risks in a thorough and automated manner but it also offers decision-support capabilities, to recommend optimal safeguards using the well-known repository of the Center for Internet Security (CIS) Controls. To the best of our knowledge, AMBIENT is the first toolkit in the academic literature that brings together the aforementioned capabilities. To demonstrate its use, we have created a case scenario based on information about cyber attacks we have received from a healthcare organisation, as a reference sector that faces critical cyber and privacy threats.

Despite the prevalence and the high impact of command injection attacks, little attention has been given by the research community to this type of code injections. Although there are many software tools to detect and exploit other types of code injections, such as SQL injections or cross-site scripting, there is no dedicated and specialized software that detects and exploits, automatically, command injection vulnerabilities. This paper proposes an open-source tool that automates the process of detecting and exploiting command injection flaws on Web applications, named as COMMand Injection eXploiter (Commix). We present and elaborate on the software architecture and detection engine of Commix as well its extra functionalities that greatly facilitate penetration testers and security researchers in the detection and exploitation of command injection vulnerabilities. Moreover, based on the knowledge and the practical experience gained from the development of Commix, we propose and analyze new identified techniques that perform side-channel exploitation for command injections allowing an attacker to indirectly deduce the output of the executed command (i.e., also known as blind command injections). Furthermore, we evaluate the detection capabilities of Commix, by performing experiments against various applications. The experimental results show that Commix presents high detection accuracy, while at the same time false positives are eliminated. Finally and more importantly, we analyze several 0-day command injection vulnerabilities that Commix detected in real-world applications. Despite its short release time, Commix has been embraced by the security community and comes preinstalled in many security-oriented operating systems including the well-known Kali Linux.

Despite the rapid growth of the cyber insurance market in recent years, insurance companies in this area face several challenges, such as a lack of data, a shortage of automated tasks, increased fraudulent claims from legal policyholders, attackers masquerading as legal policyholders, and insurance companies becoming targets of cybersecurity attacks due to the abundance of data they store. On top of that, there is a lack of Know Your Customer procedures. To address these challenges, in this article, we present , an innovative architecture that utilizes Blockchain technology to provide data transparency and traceability. The backbone of the architecture is complemented by Smart Contracts, which automate cyber insurance processes, and Self-Sovereign Identity for robust identification. The effectiveness of ’s architecture is compared with the literature against the challenges the cyber insurance industry faces. In a nutshell, our approach presents a significant advancement in the field of cyber insurance, as it effectively combats the issue of fraudulent claims and ensures proper customer identification and authentication. Overall, this research demonstrates a novel and effective solution to the complex problem of managing cyber insurance, providing a solid foundation for future developments in the field.

The blackhole attack is one of the simplest yet effective attacks that target the AODV protocol. Blackhole attackers exploit AODV parameters in order to win route requests, and thus, attract traffic, which they subsequently capture and drop. However, the first part of the attack is often neglected in present literature, while the majority of attempts in detection focus only on the second part of the attack (i.e., packet drop). This paper provides a comprehensive analysis of the blackhole attack, focusing not only on the effects of the attack, but also on the exploitation of the route discovery process. As a result, a new critical attack parameter is identified (i.e., blackhole intensity), which quantifies the relation between AODV's sequence number parameter and the performance of blackhole attacks. In addition, a novel blackhole detection mechanism is also proposed. This mechanism utilizes a dynamic threshold cumulative sum (CUSUM) test in order to detect abrupt changes in the normal behavior of AODV's sequence number parameter. A key advantage of the proposed mechanism is its ability to accurately detect blackhole attacks with a minimal rate of false positives, even if the malicious node selectively drops packets. (C) 2016 Elsevier B.V. All rights reserved.