A GPU-based Machine Learning Approach for Detection of Botnet Attacks

Rapid development and adaptation of the Internet of Things (IoT) has created new problems for securing these interconnected devices and networks. There are hundreds of thousands of IoT devices with underlying security vulnerabilities, such as insufficient device authentication/authorisation making them vulnerable to malware infection. IoT botnets are designed to grow and compete with one another over unsecure devices and networks. Once infected, the device will monitor a Command-and-Control (C&C) server indicating the target of an attack via Distributed Denial of Service (DDoS) attack. These security issues, coupled with the continued growth of IoT, presents a much larger attack surface for attackers to exploit in their attempts to disrupt or gain unauthorized access to networks, systems, and data. Large datasets available online provide good benchmarks for the development of accurate solutions for botnet detection, however model training is often a time-consuming process. Interestingly, significant advancement of GPU technology allows shortening the time required to train such large and complex models. This paper presents a methodology for the pre-processing of the IoT-Bot dataset and classification of various attack types included. We include descriptions of pre-processing actions conducted to prepare data for training and a comparison of results achieved with GPU accelerated versions of Random Forest, k-Nearest Neighbour, Support Vector Machine (SVM) and Logistic Regression classifiers from the cuML library. Using our methodology, the best-trained models achieved at least 0.99 scores for accuracy, precision, recall and f1-score. Moreover, the application of feature selection and training models on GPU significantly reduced the training and estimation times

Digital Forensic Acquisition and Analysis of Discord Applications

© 2020 IEEE. Digital forensic analyses are being applied to a variety of domains as the scope and potential of digital evidence available is vast. The importance of forensic analyses of web-based devices and tools is increasing, coinciding with the rise in online criminal activity. Discord - an application that allows text, image, video, and audio communication using VoIP - has become increasingly popular and is consequently subject to increased use by cybercriminals. While researching Discord servers and forensic artefacts, it is apparent that there is limited literature and experimentation in this domain. This paper presents our research into digital forensic analyses of Discord client-side artefacts and presents DiscFor, a novel tool designed for the extraction, analysis, and presentation of Discord data in a forensically sound manner. DiscFor creates a safe copy of said data, presenting the current cache state and converting data files into a readable format

Using deep learning to detect social media ‘trolls’

Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded text content. The project utilizes GloVe word embeddings for data augmentation for improved prediction capabilities. Our methodology details the implementation of Long Short-term memory Gated recurrent unit models and their Bidirectional variants, comparing our approach to related works, and highlighting evident improvements. Our experiments revealed that the best performing model, Bidirectional LSTM, achieved 0.92 testing accuracy and 0.88 inference accuracy with 0.92 and 0.88 F1-score accordingly

Analysis of Soot Deposition Mechanisms on Nickel-Based Anodes of SOFCs in Single-Cell and Stack Environment

Solid oxide fuel cells (SOFCs) can be fueled with various gases, including carbon-containing compounds. High operating temperatures, exceeding 600 °C, and the presence of a porous, nickel-based SOFC anode, might lead to the formation of solid carbon particles from fuels such as carbon monoxide and other gases with hydrocarbon-based compounds. Carbon deposition on fuel electrode surfaces can cause irreversible damage to the cell, eventually destroying the electrode. Soot formation mechanisms are strictly related to electrochemical, kinetic, and thermodynamic conditions. In the current study, the effects of carbon deposition on the lifetime and performance of SOFCs were analyzed in-operando, both in single-cell and stack conditions. It was observed that anodic gas velocity has an impact on soot formation and deposition, thus it was also studied in depth. Single-anode-supported solid oxide fuel cells were fueled with gases delivered in such a way that the initial velocities in the anodic compartment ranged from 0.1 to 0.7 m/s. Both cell operation and post-mortem observations proved that the carbon deposition process accelerates at higher anodic gas velocity. Furthermore, single-cell results were verified in an SOFC stack operated in carbon-deposition regime by dry-coupling with a downdraft 150 kWth biomass gasifier

Influence of the Contamination of Fuel with Fly Ash Originating from Biomass Gasification on the Performance of the Anode-Supported SOFC

The integration of solid oxide fuel cells (SOFCs) with biomass gasification reactors raises the possibility of solid particle contamination of the gaseous fuel entering the cell. Technical specifications from SOFC manufacturers, among other sources, claim that SOFCs do not tolerate the presence of solid particles in fuel. However, there is very limited literature on the experimental investigation of feeding SOFCs with particulate matter aerosols. In this study, a standard 5 × 5 cm anode-supported SOFC was fueled by two types of aerosols, namely, (1) inert powder of grain sizes and concentration equivalent to gasifier fly ash and (2) a real downdraft gasifier fly ash, both suspended in a gaseous fuel mixture. For reference, cells were also investigated with a dust-free fuel gas of the same composition. A straightforward negative influence of the inert powder aerosol could not be confirmed in experiments with a duration of 6 days. That said, the introduction of carbonaceous fly ash aerosol caused slow but irreversible damage to the SOFC. The degradation mechanisms were studied, and the presence of carbon-containing particles was found to clog the pores of the SOFC anode. The maximum measured power density of the SOFC equaled 855 mW/cm2 (850 °C, reference fuel). Feeding inert aerosol fuel caused no rapid changes in power density. A moderate drop in performance was observed throughout the experiment. The contamination of fuel with fly ash resulted in an initial performance gain and a ca. 25% performance drop longer term (43 h of contamination). Post-mortem analysis revealed contamination on the walls of the gas channels, with some visible alumina or fly ash spots in the anode area