Mobile malware is a malicious code specifically designed to target mobile devices to perform multiple types of fraud. The number of attacks reported each day is increasing constantly and is causing an impact not only at the end-user level but also at the network operator level. Malware like FluBot contributes to identity theft and data loss but also enables remote Command & Control (C2) operations, which can instrument infected devices to conduct Distributed Denial of Service (DDoS) attacks. Current mobile device-installed solutions are not effective, as the end user can ignore security warnings or install malicious software. This article designs and evaluates MONDEO-Tactics5G - a multistage botnet detection mechanism that does not require software installation on end-user devices, together with tactics for 5G network operators to manage infected devices. We conducted an evaluation that demonstrates high accuracy in detecting FluBot malware, and in the different adaptation strategies to reduce the risk of DDoS while minimising the impact on the clients' satisfaction by avoiding disrupting established sessions.

Alloy is a declarative modeling language based on a first-order relational logic. Its constraint-based analysis has enabled a wide range of applications in software engineering, including configuration synthesis, bug finding, test-case generation, and security analysis. Certain types of analysis tasks in these domains involve finding an optimal solution. For example, in a network configuration problem, instead of finding any valid configuration, it may be desirable to find one that is most permissive (i.e., it permits a maximum number of packets). Due to its dependence on SAT, however, Alloy cannot be used to specify and analyze these types of problems.

Advanced persistent threats (APTs) are a particularly troubling challenge for software systems. The adversarial nature of the security domain, and APTs in particular, poses unresolved challenges to the design of self-* systems, such as how to defend against multiple types of attackers with different goals and capabilities. In this interaction, the observability of each side is an important and under-investigated issue in the self-* domain. We propose a model of APT defense that elevates observability as a first-class concern. We evaluate this model by showing how an informed approach that uses observability improves the defender's utility compared to a uniform random strategy, can enable robust planning through sensitivity analysis, and can inform observability-related architectural design decisions.