Evidence on the effectiveness of ManMachine-At-The-End (MATE) software protections, such as code obfuscation, has mainly come from limited empirical research. Recently, however, a meta-model and an automatable method waswere proposed to obtain statistical models of the required effort to attack (protected) software. The proposed method was sketched for a number of attack strategies but not instantiated, evaluated, or validated for those that require human interaction with the attacked software. In this paper, we present an fullend-to-end instantiation, formalisation, implementation, and validation of thethat existing meta-model and method to obtain statistical effort models for game resource localisation attacks, which represent a major step towards creating game cheats, a prime example of MATE attacks. We discuss in detail all relevant aspects of our instantiation and the results obtained for two game use cases. Our results confirm the feasibility of the proposedexisting meta-model and method, and itstheir utility for decision support for users of software protection tools. These results open up a new avenue for obtaining models of the impact of software protections on reverse engineering attacks, which will scale much better than empirical research involving human participants.

Several critical contexts, such as healthcare, smart cities, drones, transportation, and agriculture, nowadays rely on IoT, or more in general embedded, devices that require comprehensive security analysis to ensure their integrity before deployment. Security concerns are often related to vulnerabilities that result from inadequate coding or undocumented features that may create significant privacy issues for users and companies. Current analysis methods, albeit dependent on complex tools, may lead to superficial assessments due to compatibility issues, while authoritative entities struggle with specifying feasible firmware analysis requests for manufacturers within operational contexts. This paper urges the scientific community to collaborate with stakeholders—manufacturers, vendors, security analysts, and experts—to forge a cooperative model that clarifies manufacturer contributions and aligns analysis demands with operational constraints. Aiming at a modular approach, this paper highlights the crucial need to refine security analysis, ensuring more precise requirements, balanced expectations, and stronger partnerships between vendors and analysts. To achieve this, we propose a threat model based on the feasible interactions of actors involved in the security evaluation of a device, with a particular emphasis on the responsibilities and necessities of all entities involved.

Nowadays, mobile devices are massively used in everyday activities. Thus, they contain sensitive data targeted by threat actors like bank accounts and personal information. Through the years, Machine Learning approaches have been proposed to identify malicious Android applications, but recent research highlights the need for better explanations for model decisions, as existing ones may not be related to the app’s malicious functionalities. This paper proposes an explainable approach based on static analysis to detect Android malware. The novelty lies in the specific analysis conducted to select and extract the features (i.e., APIs taken from the DEX Call Graph) that immediately provide meaningful explanations of the model functionality, thus allowing a significant correlation of the malware behavior with its family. Moreover, since we contain the number and type of features, the distinct impacts of each one appear more evident. The attained results show that it is possible to reach comparable results (in terms of accuracy) to existing state-of-the-art models while providing easy-to-understand explanations, which may yield significant insights into the malicious functionalities of the samples.