Man-in-the-Middle (MitM), one of the best known attacks in the world of computer security, is among the greatest concerns for professionals in the field. Main goal of MitM is to compromise confidentiality, integrity and availability of data flowing between source and destination. However, most of its many variants involve difficulties that make it not always possible. The present paper aims at modelling and describing a new method of attack, named Browser-in-the-Middle (BitM) which, despite the similarities with MitM in the way it controls the data flow between a client and the service it accesses, bypasses some of MitM’s typical shortcomings. It could be started by phishing techniques and in some cases coupled to the well-known Man-in-the-Browser (MitB) attack. It will be seen how BitM expands the range of the possible attacker’s actions, at the same time making them easier to implement. Among its features, the absence of the need to install malware of any kind on the victim’s machine and the total control it allows the attacker are to be emphasized.

The introduction of new memory-based crypto-mining techniques and the rise of new web technologies like WebAssembly, made the use of browsers for crypto-currencies mining more and more convenient and popular. That, in turn, originated a new form of computer piracy, called cryptojacking, which is rapidly gaining ground on the web. A cryptojacking site exploits its visitors’ hardware resources to secretly mine crypto-currencies. This paper analyzes current web-based cryptojacking detection methods in order to propose a novel hybrid strategy. Current detection methods are found to require either considerable computer administration skills or execution privileges usually not available to common users. In this view, a method, named MinerAlert, has been designed and proposed, aiming at detecting in real-time sites performing cryptojacking. To address the limitations of current methods, the method implementation has been achieved through a browser extension. The present paper describes the method’s details and its implementation. It also reports the experimental results of its utilization, showing its positive performances in terms of ease of use, successful detections and speed.

Due to the increasing amount of software and hardware in connected and autonomous cars, the attack surface is growing, which increases the risk of security attacks. Researchers proposed machine learning or deep learning techniques to identify threats in in-vehicle networks. However, using these techniques is not enough to support the automotive industry since new processes or techniques must be conceptualized to make automotive systems more secure. Therefore, this research work presents a methodology, Quantum-based Automotive Threat Intelligence and Countermeasures (QUANTICAR), that integrates quantum optimization for CAN bus Intrusion Detection and the National Vulnerability Database (NVD) to understand the automotive attacks. In the first phase, QUANTICAR identifies the different types of attacks and then, based on the specific attack class, extracts new knowledge using the NVD. This contributes not only to improving attack detection but also to developing an Automotive Knowledge Base that can support developers and security experts in the secure development of automotive components in compliance with ISO/SAE 21434.

The recent publication of the “Browser in the Middle” attack has demonstrated an effective way to compromise a good number of variants of Multifactor Authentication and to control the information flow between the victim an the accessed service. That attack was mainly aimed at the victim use of a desktop browser to access a service. The present paper shows how that attack may be extended to involve the mobile environment and how, thanks to that enhancement, the attack may also gain the persistence attribute. The new attack is named MobileApp-in-the-Middle (MAitM). Again, as in BitM, no installation of malware on the victim’s platform is needed with MAitM.