The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.

Data breaches result in data loss, including personal, health, and financial information that are crucial, sensitive, and private. The breach is a security incident in which personal and sensitive data are exposed to unauthorized individuals, with the potential to incur several privacy concerns. As an example, the French newspaper Le Figaro breached approximately 7.4 billion records that included full names, passwords, and e-mail and physical addresses. To reduce the likelihood and impact of such breaches, it is fundamental to strengthen the security efforts against this type of incident and, for that, it is first necessary to identify patterns of its occurrence, primarily related to the number of data records leaked, the affected geographical region, and its regulatory aspects. To advance the discussion in this regard, we study a dataset comprising 428 worldwide data breaches between 2018 and 2019, providing a visualization of the related statistics, such as the most affected countries, the predominant economic sector targeted in different countries, and the median number of records leaked per incident in different countries, regions, and sectors. We then discuss the data protection regulation in effect in each country comprised in the dataset, correlating key elements of the legislation with the statistical findings. As a result, we have identified an extensive disclosure of medical records in India and government data in Brazil in the time range. Based on the analysis and visualization, we find some interesting insights that researchers seldom focus on before, and it is apparent that the real dangers of data leaks are beyond the ordinary imagination. Finally, this paper contributes to the discussion regarding data protection laws and compliance regarding data breaches, supporting, for example, the decision process of data storage location in the cloud.

Cyber attacks have increased in frequency in recent years, affecting small, medium and large companies, creating an urgent need for tools capable of helping the mitigation of such threats. Thus, with the increasing number of cyber attacks, we have a large amount of threat data from heterogeneous sources that needs to be ingested, processed and analyzed in order to obtain useful insights for their mitigation. This study proposes a methodological framework to collect, organize, filter, share and visualize cyber-threat data to mitigate attacks and fix vulnerabilities, based on an eight-step cyber threat intelligence model with timeline visualization of threats information and analytic data insights. We developed a tool to address needs in which the cyber security analyst can insert threat data, analyze them and create a timeline to obtain insights and a better contextualization of a threat. Results show the facilitation of understanding the context in which the threats are inserted, rendering the mitigation of vulnerabilities more effective.

Information and Communication Technology (ICT) Governance is increasingly necessary and present in organizations aiming to improve the maturity of their ICT processes. This paper presents an analysis of the ICT Governance processes of a Brazilian Federal Public Administration agency. To assess the maturity of the ICT Governance processes, we surveyed and diagnosed the processes performed by the agency and organized a series of meetings/discussions to assist in the improvement and modeling of the processes related to the ICT Contract Planning process. As a result, we proposed improvements and identified the maturity level of the existing ICT processes, also assessing the awareness of employees of the General Coordination of Information Technology regarding these processes. Our findings reveal that the agency still needs to implement the following processes: (1) ICT People Management; (2) Business Process Modeling (Automated/to Automate); (3) Change Management; (4) Execution Monitoring of the ICT Projects and Services Portfolio; and (5) ICT Service Continuity Management. We also identified several artifacts that need to be implemented by the agency in different processes and collected survey participants’ suggestions about new processes to improve the maturity in ICT Governance.

This article presents an innovative approach to video steganography called Stego-STFAN, as by using a cheap model process to use the temporal and spatial domains together, they end up presenting fine adjustments in each frame, the Stego-STFAN had a $PSN{R}_c$ metric of 27.03 and $PSN{R}_S$ of 23.09, which is close to the state-of-art. Steganography is the ability to hide a message so that third parties cannot perceive communication between them. Thus, one of the precautions in steganography is the size of the message you want to hide, as the security of the message is inversely proportional to its size. Inspired by this principle, video steganography appears to expand channels further and incorporate data into a message. To improve the construction of better stego-frames and recovered secrets, we propose a new architecture for video steganography derived from the Spatial-Temporal Adaptive Filter Network (STFAN) in conjunction with the Attention mechanism, which together generates filters and maps dynamic frames to increase the efficiency and effectiveness of frame processing, exploiting the redundancy present in the temporal dimension of the video, as well as fine details such as edges, fast-moving pixels and the context of secret and cover frames and by using the DWT method as another feature extraction level, having the same characteristics as when applied to an image file.