Edgar Weippl

André García Gómez, Ines Rieger, Wolfgang Hotwagner et al.

Collaborative Intrusion Detection Systems (CIDS) are increasingly adopted to counter cyberattacks, as their collaborative nature enables them to adapt to diverse scenarios across heterogeneous environments. As distributed critical infrastructure operates in rapidly evolving environments, such as drones in both civil and military domains, there is a growing need for CIDS architectures that can flexibly accommodate these dynamic changes. In this study, we propose a novel CIDS framework designed for easy deployment across diverse distributed environments. The framework dynamically optimizes detector allocation per node based on available resources and data types, enabling rapid adaptation to new operational scenarios with minimal computational overhead. We first conducted a comprehensive literature review to identify key characteristics of existing CIDS architectures. Based on these insights and real-world use cases, we developed our CIDS framework, which we evaluated using several distributed datasets that feature different attack chains and network topologies. Notably, we introduce a public dataset based on a realistic cyberattack targeting a ground drone aimed at sabotaging critical infrastructure. Experimental results demonstrate that the proposed CIDS framework can achieve adaptive, efficient intrusion detection in distributed settings, automatically reconfiguring detectors to maintain an optimal configuration, without requiring heavy computation, since all experiments were conducted on edge devices.

David Rupprecht, Adrian Dabrowski, Thorsten Holz et al.

Over the last decades, numerous security and privacy issues in all three active mobile network generations have been revealed that threaten users as well as network providers. In view of the newest generation (5G) currently under development, we now have the unique opportunity to identify research directions for the next generation based on existing security and privacy issues as well as already proposed defenses. This paper aims to unify security knowledge on mobile phone networks into a comprehensive overview and to derive pressing open research questions. To achieve this systematically, we develop a methodology that categorizes known attacks by their aim, proposed defenses, underlying causes, and root causes. Further, we assess the impact and the efficacy of each attack and defense. We then apply this methodology to existing literature on attacks and defenses in all three network generations. By doing so, we identify ten causes and four root causes of attacks. Mapping the attacks to proposed defenses and suggestions for the 5G specification enables us to uncover open research questions and challenges for the development of next-generation mobile networks. The problems of unsecured pre-authentication traffic and jamming attacks exist across all three mobile generations. They should be addressed in the future, in particular, to wipe out the class of downgrade attacks and, thereby, strengthen the users' privacy. Further advances are needed in the areas of inter-operator protocols as well as secure baseband implementations. Additionally, mitigations against denial-of-service attacks by smart protocol design represent an open research question.

Sebastian Neuner, Victor van der Veen, Martina Lindorfer et al.

Expecting the shipment of 1 billion Android devices in 2017, cyber criminals have naturally extended their vicious activities towards Google's mobile operating system. With an estimated number of 700 new Android applications released every day, keeping control over malware is an increasingly challenging task. In recent years, a vast number of static and dynamic code analysis platforms for analyzing Android applications and making decision regarding their maliciousness have been introduced in academia and in the commercial world. These platforms differ heavily in terms of feature support and application properties being analyzed. In this paper, we give an overview of the state-of-the-art dynamic code analysis platforms for Android and evaluate their effectiveness with samples from known malware corpora as well as known Android bugs like Master Key. Our results indicate a low level of diversity in analysis platforms resulting from code reuse that leaves the evaluated systems vulnerable to evasion. Furthermore the Master Key bugs could be exploited by malware to hide malicious behavior from the sandboxes.