Deep Learning for Combating Misinformation in Multicategorical Text Contents.

Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional 'editorial office', placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work.

Sensors and Pattern Recognition Methods for Security and Industrial Applications.

Contemporary cyberthreats continue to evolve, powering the neverending development arms race [...

Advances in Computer Recognition, Image Processing and Communications.

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...

New explainability method for BERT-based model in fake news detection.

The ubiquity of social media and their deep integration in the contemporary society has granted new ways to interact, exchange information, form groups, or earn money-all on a scale never seen before. Those possibilities paired with the widespread popularity contribute to the level of impact that social media display. Unfortunately, the benefits brought by them come at a cost.

How to Effectively Collect and Process Network Data for Intrusion Detection?

The number of security breaches in the cyberspace is on the rise. This threat is met with intensive work in the intrusion detection research community. To keep the defensive mechanisms up to date and relevant, realistic network traffic datasets are needed.

A $10 million question and other cybersecurity-related ethical dilemmas amid the COVID-19 pandemic.

Cybercrime and cybersecurity are like two sides of the same coin: They are opposites but cannot exist without each other. Their mutual relation generates a myriad of ethical issues, ranging from minor to vital. The rapid development of technology will surely involve even more ethical concerns, like the infamous example of a fitness tracking company allegedly paying $10 million worth of ransom.

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity.

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented.

Implementation of the BERT-derived architectures to tackle disinformation challenges.

Recent progress in the area of modern technologies confirms that information is not only a commodity but can also become a tool for competition and rivalry among governments and corporations, or can be applied by ill-willed people to use it in their hate speech practices. The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools.

The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset.

Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture.

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection.

Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient.