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Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and locker ransomware from goodware. (3) Generate and make public a dynamic features dataset that includes these selected parameters for samples of different artifacts. (4) Apply the dynamic feature dataset to obtain models with machine learning algorithms. Five platforms, 20 ransomware, and 20 goodware artifacts were evaluated. The final feature dataset is composed of 2000 registers of 50 characteristics each. This dataset allows for a machine learning detection with a 10-fold cross-evaluation with an average accuracy superior to 0.99 for gradient boosted regression trees, random forest, and neural networks.

RESUMO

Recently, the number of vehicles equipped with wireless connections has increased considerably. The impact of that growth in areas such as telecommunications, infotainment, and automatic driving is enormous. More and more drivers want to be part of a vehicular network, despite the implications or risks that, for instance, the openness of wireless communications, its dynamic topology, and its considerable size may bring. Undoubtedly, this trend is because of the benefits the vehicular network can offer. Generally, a vehicular network has two modes of communication (V2I and V2V). The advantage of V2I over V2V is roadside units' high computational and transmission power, which assures the functioning of early warning and driving guidance services. This paper aims to discover the principal vulnerabilities and challenges in V2I communications, the tools and methods to mitigate those vulnerabilities, the evaluation metrics to measure the effectiveness of those tools and methods, and based on those metrics, the methods or tools that provide the best results. Researchers have identified the non-resistance to attacks, the regular updating and exposure of keys, and the high dependence on certification authorities as main vulnerabilities. Thus, the authors found schemes resistant to attacks, authentication schemes, privacy protection models, and intrusion detection and prevention systems. Of the solutions for providing security analyzed in this review, the authors determined that most of them use metrics such as computational cost and communication overhead to measure their performance. Additionally, they determined that the solutions that use emerging technologies such as fog/edge/cloud computing present better results than the rest. Finally, they established that the principal challenge in V2I communication is to protect and dispose of a safe and reliable communication channel to avoid adversaries taking control of the medium.

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RESUMO

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

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