No abstract

Contact tracing has historically been used to retard the spread of infectious diseases, but if it is exercised by hand in large-scale, it is known to be a resource-intensive and quite deficient process. Nowadays, digital contact tracing has promptly emerged as an indispensable asset in the global fight against the coronavirus pandemic. The work at hand offers a meticulous study of all the official Android contact tracing apps deployed hitherto by European countries. Each app is closely scrutinized both statically and dynamically by means of dynamic instrumentation. Depending on the level of examination, static analysis results are grouped in two axes. The first encompasses permissions, API calls, and possible connections to external URLs, while the second concentrates on potential security weaknesses and vulnerabilities, including the use of trackers, in-depth manifest analysis, shared software analysis, and taint analysis. Dynamic analysis on the other hand collects data pertaining to Java classes and network traffic. The results demonstrate that while overall these apps are well-engineered, they are not free of weaknesses, vulnerabilities, and misconfigurations that may ultimately put the user security and privacy at risk.

No abstract

Wi-Fi is arguably the most proliferated wireless technology today. Due to its massive adoption, Wi-Fi deployments always remain in the epicenter of attackers and evildoers. Surprisingly, research regarding machine learning driven intrusion detection systems (IDS) that are specifically optimized to detect Wi-Fi attacks is lagging behind. On top of that, the field is dominated by false or half-true assumptions that potentially can lead to corresponding models being overfilled to certain validation datasets, simply giving the impression or illusion of high efficiency. This work attempts to provide concrete answers to the following key questions regarding IEEE 802.11 machine learning driven IDS. First, from an expert's viewpoint and with reference to the relevant literature, what are the criteria for determining the smallest possible set of classification features, which are also common and potentially transferable to virtually any deployment types/versions of 802.11? And second, based on these features, what is the detection performance across different network versions and diverse machine learning techniques, i.e., shallow versus deep learning ones? To answer these questions, we rely on the renowned 802.11 security-oriented AWID family of datasets. In a nutshell, our experiments demonstrate that with a rather small set of 16 features and without the use of any optimization or ensemble method, shallow and deep learning classification can achieve an average F1 score of up to 99.55% and 97.55%, respectively. We argue that the suggested human expert driven feature selection leads to lightweight, deployment-agnostic detection systems, and therefore can be used as a basis for future work in this interesting and rapidly evolving field.

Built on top of UDP, the recently standardized QUIC protocol primarily aims to gradually replace the TCP plus TLS plus HTTP/2 model. For instance, HTTP/3 is designed to exploit QUIC’s features, including reduced connection establishment time, multiplexing without head of line blocking, always-encrypted end-to-end security, and others. This work serves two key objectives. Initially, it offers the first to our knowledge full-fledged review on QUIC security as seen through the lens of the relevant literature so far. Second and more importantly, through extensive fuzz testing, we conduct a hands-on security evaluation against the six most popular QUIC-enabled production-grade servers. This assessment identified several effective and practical zero-day vulnerabilities, which, if exploited, can quickly overwhelm the server resources. This finding is a clear indication that the fragmented production-level implementations of this contemporary protocol are not yet mature enough. Overall, the work at hand provides the first wholemeal appraisal of QUIC security from both a literature review and empirical standpoint, and it is therefore foreseen to serve as a reference for future research in this timely area.