Electrical systems keep things running in modern life, but they often run into problems like imbalances, short circuits, ground faults, and overloading, which can cause equipment to break down, fires to break out, and even large-scale blackouts. To make matters worse, acts of sabotage, physical damage, or cyberattacks on systems like SCADA can mess up operations, throw grids off balance, and set off cascading failures. To avoid these risks, there is a growing need for smarter tools that can keep track of system performance and flag potential issues before they get out of hand. In this paper, we suggest a deep learning model built on the inception architecture, designed to monitor electrical systems, call out potential security faults, and spot malicious actions. Taking advantage of deep learning, our approach helps increase fault prediction accuracy and keep operations on track.

Emotion recognition from audio signals is essential in forensic applications, offering insight into emotional states during interrogations, threat assessments, and crime scene analysis. This paper proposes an Inception-based deep learning model tailored for forensic arabic audio emotion recognition. The Inception architecture, with its multiscale feature extraction capabilities, efficiently captures subtle emotional details from complex audio signals. The model was evaluated on a dataset that represents a diverse range of emotional expressions, achieving superior performance in accuracy, robustness, and adaptability compared to traditional approaches. Its precision and ability to handle real-world variability make it particularly suited for forensic investigations. This work underscores the potential of advanced neural architectures in enhancing forensic decision-making and analysis.

In the current landscape of IT, ransomware attacks pose a major threat to cybersecurity resulting in significant monetary losses and data breaches. The detection of ransomware in time presents a challenge due to its constant evolution and complex strategies for escaping detection. This study introduces a deep learning tool —named RansFighter—based on Gated Recurrent Unit (GRU) specifically developed for ransomware detection. Our model shows, at test time, an Accuracy of 96.67%, a Precision of 97.01%, a Recall of 96.37%, an F1-Score of 96.69% and an Area Under the Curve (AUC) of 96.67%. It showcases the potential of GRUs as valuable assets to safeguard systems against ransomware threats.

In forensic investigations, facial recognition techniques serve as critical tools for identifying and apprehending suspects. In this study, we investigate the impact of Convolutional Neural Networks (CNNs) parallelization on the performance of facial recognition models within forensic contexts. Through experiments, we demonstrate the potential benefits of parallelization in enhancing model accuracy and robustness. Leveraging a reduced dataset, we employ augmentation techniques to expand the diversity of training samples. Our findings highlight the advantages of CNN parallelization in achieving superior recognition outcomes. Nevertheless, we identify constraints linked to excessive parallelization, which may induce model overfitting.

Due to its versatility and wide variety of constructs, BPMN (Business Process Model and Notation) is today the leading standard notation for creating visual models of business or organizational processes. It is a rich and expressive graphical language specially designed to provide a notation that is easily understood by all members of a company. Sometimes, however, this large number of controls and action nodes available can become a weakness since a given semantics can be represented in many ways, causing some ambiguity and raising the question of bisimilarity between two models. Today, it is universally recognized that formal methods are useful for the specification, design and verification of almost all systems, and essential for the most critical ones. On the other hand, the Business Process Execution Language for Web Services (BPEL) is an executable language structured in blocks, supported by many execution platforms, making it possible to specify the actions in the business processes with Web services. Since BPMN and BPEL share almost the same level of abstraction, we present in this article a formalization of the BPMN language through a mapping to BPEL, aiming to remove its ambiguities, to solve the complex modeling and interaction problems and open the door to many formal analysis such as model checking. We first formalize the BPEL language using the K framework, we then map the BPMN language to this formalized version of BPEL. The K Framework is a rewriting/reachability based framework enabling language developers to formally define all programming languages. Once a language is formally specified in the K framework, the framework automatically outputs a range of formal verification tool sets, compilers, debuggers and other developer tools for it.

Web services (WS) composition is the center of many different types of information systems such as e-commerce, financial and healthcare systems, where sensitive data are shared, which raises important security problems. WSBPEL is a standard to specify business processes, however, this orchestration language does not support users to meet their security requirements. This paper proposes a formal and automatic approach for enforcing security policies in WS composition. More precisely, given a composition of WSs written in WS-BPEL and a security policy specified in the LTL logic, this paper aims to generate a new version of the services that respects the security policy. The LTL policy is transformed to a new service that can monitor some activities of the others.