The increasing complexity of contemporary malware, driven by the use of advanced evasion and obfuscation techniques, combined with the rapid growth in the number of new variants emerging continuously, undermines the effectiveness of traditional signature-based detection mechanisms. In response to this scenario, this work proposes FORENSICS (Framework fOr malwaRe dEtectioN baSed on InstruCtion Sequences), an innovative deep learning-based framework that employs variable-length instruction sequences to detect malware efficiently and accurately. By integrating Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) neural networks, FORENSICS analyzes opcode sequences extracted from real-world malware and benign software artifacts. The framework introduces optimized methods for opcode extraction and representation, significantly reducing computational overhead while preserving detection performance. FORENSICS achieved 99.91% accuracy, 99.99% precision, and detection times ranging from 8 to 17 milliseconds, outperforming several state-of-the-art approaches across multiple metrics. Additionally, the framework demonstrated robustness in identifying zero-day malware samples, confirming its effectiveness in real-world cybersecurity scenarios. A new balanced dataset comprising over 40,000 labeled samples was created and made publicly available, facilitating reproducibility and encouraging further research. These results position FORENSICS as a robust, scalable, and highly effective solution for malware detection in modern threat landscapes.

Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. Such data streams are generally uploaded and centralized to be processed by third parties, potentially exposing sensitive personal information. In this context, existing protection mechanisms, such as Location Privacy Protection Mechanisms (LPPMs), have been investigated. Alas, none of them have actually been implemented, nor deployed in real-life, in mobile devices to enforce user privacy at the edge. Moreover, the diversity of embedded sensors and the resulting data deluge makes it impractical to provision such services directly on mobiles, due to their constrained storage capacity, communication bandwidth and processing power. This article reports on the FLI technique, which leverages a piece-wise linear approximation technique to capture compact representations of data streams in mobile devices. Beyond the FLI storage layer, we introduce Divide & Stay, a new privacy preservation technique to execute Points of Interest (POIs) inference. Finally, we deploy both of them on Android and iOS as the INTACT framework, making a concrete step towards enforcing privacy and trust in ubiquitous computing systems.

Traditional centralized systems cannot deal with the big data context. Distributed computing platforms such as Apache Spark have been widely adopted, but configuring their parameters is challenging given the number of factors and their interactions. This work employs Design of Experiments (DoE) techniques to screening most relevant factors regarding execution time of a Naïve Bayes machine learning distributed task on a subset of the PT7 Web Corpus, which has 14.88 GB of data. Employing a fractional factorial design with 192 experimental units and linear regression techniques with backward elimination, we obtained (i) the most relevant factors based on statistical significance and (ii) a model capable of predicting execution time according to parameters' values in the analyzed context. Our results also include a visualization technique based on Inselberg's Parallel Coordinates to comprehend the impact on performance facing various configuration possibilities.

This study thoroughly analyzes the cybersecurity status of Small Office/Home Office (SOHO) Wi-Fi routers. These routers are crucial but frequently overlooked elements in network infrastructure, particularly in light of the impact of the COVID-19 pandemic on network security. The pandemic has led to shifts in network usage patterns, blurring traditional security perimeters and extending them into private residences, creating additional points of vulnerability in urban environments. Our nationwide research evaluated an extensive dataset of router brands and models currently used at scale. We measured the prevalence of known vulnerabilities, assessed the currency of userspace and kernel software versions, and compared the security robustness of proprietary firmware against open-source alternatives. Our findings reveal a concerning landscape of widespread vulnerabilities and outdated software components, posing latent risks to end-users. The results indicate a predominance of Linux on MIPS and ARM architectures, with an average delay of 5 to 10 years between the release of the kernel and the implementation of the most recent firmware versions. As a result, we observed an average of 1344 and 72 vulnerabilities in the kernel and applications. One significant discovery from our research is that replacing the manufacturer's original firmware with open-source alternatives, such as DD-WRT, OpenWrt, and Tomato, can substantially enhance the security of the software stack. This enhancement results in improvements of up to 97% in the case of binaries and 98.42% in the kernel. Our research helps increase cybersecurity awareness by pinpointing critical home network environment weaknesses and alerting the need for more rigorous security practices in producing and maintaining SOHO routers. This investigation also allowed the report of a new remote code execution vulnerability (disclosed in CVE-2022-46552).

The computational power of Internet of Things (IoT) devices is usually low, which makes it necessary to process data and extract relevant information on devices with higher processing capacity. Edge Computing emerged as a complementary solution to cloud computing, providing devices at the network edge with computational resources to handle the data processing and analysis that constrained IoT devices eventually cannot perform. This solution allows data processing closer to the IoT devices, reducing latency for IoT applications. However, the resource constraints of edge nodes, which have lower computational power than the cloud nodes, make resource allocation and processing massive requests challenging. This study proposes an edge resource allocation mechanism based on task priority and machine learning. The proposed approach efficiently allocates resources for IoT requests based on their task priorities while monitoring the resource consumption of edge nodes. This study evaluates the performance of different classification algorithms by using well-known metrics for classifying models. The most efficient classifier achieved an accuracy of 92% and a precision of 90%. The results indicate good performance when using this classifier in the evaluated approach. The proposed mechanism demonstrated that resource management can be done more efficiently with significantly lower resource utilization when compared to an allocation method based only on distance. The study tested different scenarios regarding the number of requests, edge nodes, and a proposed failure mechanism to schedule failed node tasks to functional nodes. This failure control mechanism is a significant contribution of the proposal. Therefore, the proposed method in this study can become a valuable tool for efficient resource management with reduced computational cost and efficient resource allocation.

In the context of the COVID-19 pandemic, using contact-tracking apps and measures such as social isolation and mask-wearing has emerged as an efficient strategy to mitigate the spread of the virus. Nonetheless, these apps have raised privacy concerns. This paper introduces a technique for enhancing Privacy in contact-tracing systems while preserving the data for research purposes. The contact-tracing system employs a unique identifier signed with a key associated with the application and the user. In this system, mobile devices serve as sensors sending beacons, actively detecting nearby devices, and transmitting the identifiers of surrounding contacts to a cloud-based platform. When a user reports a positive COVID-19 diagnosis, a dedicated web service identifies and tracks the identifiers associated with at-risk contacts. The system uses a topic-based publish-subscribe broker, and each identifier represents an individual topic to abstract contact communication and disseminate alert messages. To assess the system's efficacy, we conducted a use case with twenty volunteers using the mobile application for two weeks, representing a small university campus. The quantitative results of the use case demonstrated the system's capability of analyzing potential virus transmission and observing user's social interactions while maintaining their anonymity.

Unmanned Aerial Vehicles (UAVs) and communication systems are fundamental elements in Mission Critical services, such as Search and Rescue (SAR) operations. UAVs can fly over an area, collect high-resolution video information, and transmit it back to a ground base station to identify victims through a Deep Neural Network object detection model. However, instabilities in the communication infrastructure can compromise SAR operations. For example, if one or more transmitted data packets fail to arrive at their destination, the high-resolution video frames can be distorted, degrading the application performance. In this article, we explore the relevance of computer vision application information, complementing the functionalities of Radio Access Network Intelligent Controllers for managing and orchestrating network components, through FramCo - a frame corrupted detection based on EfficientNet. Another contribution from this article is an architectural element that explores the components of the Open Radio Access Network (O-RAN) standard specification, with an assessment of a complex use case that explores new market trends, such as SAR operations assisted by UAV-based computer vision. The experimental results indicate that the proposed architectural element can act as an external trigger, integrated into the O-RAN cognitive control loop, significantly improving the performance of applications with sensitive Key Performance Indicators (KPIs).

There is a growing initiative on the part of regulatory bodies to employ control over firmware emissions destined for Internet of Things (IoT) devices. In this scenario, this paper presents a new approach, called SOTARU, which proposes the use of a consortium Blockchain among embedded system manufacturers as a way to allow access to the update history of devices from multiple manufacturers through a single infrastructure. The security and robustness of the proposal were evaluated with the help of the Common Open Research Emulator (CORE) distributed network emulator. As a result, it was found that SOTARU stands out in terms of security when compared to other approaches proposed in the literature, as well as being functional even in high latency scenarios.

Most modern 5G Vertical Service Orchestrators present various limitations. Among these, one may high-light (i) the employment of monolithic architectures, (ii) the lack of standardized APIs and methodologies, (iii) the minimum support for inter-domain scenarios, and (iv) the impossibility of performing run-time operations over Vertical Services. To achieve a fully-fledged Vertical Service Orchestrator, these problems must be solved. This is the focus of the work presented in this article. Our work presents a new 5G Vertical Service orchestration system named NetOr, which tackles all the listed limitations and can support complex and intricate use cases. NetOr was implemented according to a microservice architecture. Thus, it has increased flexibility, scalability, and maintainability. Moreover, NetOr inherited most advantages of the modern Vertical Service Orchestrators and, therefore, can be considered an improvement of said orchestrators. Furthermore, this work also presents a Proof-of-Concept solution to achieve inter-domain communication through the orchestration of an End-To-End Network Slice that establishes several VPN tunnels between the domains encompassed by the Network Slice.

The Internet of Things (IoT) end devices have major limitations related to hardware and energy autonomy. Generally, the highest energy consumption is related to communication, which accounts for up to 60% of consumption depending on the application. Among the strategies to optimize the energy consumed by communication, data compression methods are one of the most promising. However, most data compression algorithms are designed for personal computers and need to be adapted to the IoT context. This study aims to adapt classical algorithms, such as LZ77, LZ78, LZW, Huffman, and Arithmetic coding, and to analyse their performance and energy metrics in IoT end devices. The evaluation is performed in a device with an ESP32 processor and LoRa modulation. The study makes use of real datasets derived from two IoT applications. The results show compression rates close to 70%, a three-fold increase in the number of messages sent, and a reduction in energy consumption of 22%. An analytical model was also developed to estimate the gain in the battery life of the device using the adapted algorithms.