The ever-escalating prevalence of malware is a serious cybersecurity threat, often requiring advanced post-incident forensic investigation techniques. This paper proposes a framework to enhance malware forensics by leveraging reinforcement learning (RL). The approach combines heuristic and signature-based methods, supported by RL through a unified MDP model, which breaks down malware analysis into distinct states and actions. This optimisation enhances the identification and classification of malware variants. The framework employs Q-learning and other techniques to boost the speed and accuracy of detecting new and unknown malware, outperforming traditional methods. We tested the experimental framework across multiple virtual environments infected with various malware types. The RL agent collected forensic evidence and improved its performance through Q-tables and temporal difference learning. The epsilon-greedy exploration strategy, in conjunction with Q-learning updates, effectively facilitated transitions. The learning rate depended on the complexity of the MDP environment: higher in simpler ones for quicker convergence and lower in more complex ones for stability. This RL-enhanced model significantly reduced the time required for post-incident malware investigations, achieving a high accuracy rate of 94% in identifying malware. These results indicate RL’s potential to revolutionise post-incident forensics investigations in cybersecurity. Future work will incorporate more advanced RL algorithms and large language models (LLMs) to further enhance the effectiveness of malware forensic analysis.

This paper details the research into using audio signal processing methods to provide authentication and identification services for the purpose of enhancing cyber security in voice applications. Audio is a growing domain for cyber security technology. It is envisaged that over the next decade, the primary interface for issuing commands to consumer internet-enabled devices will be voice. Increasingly, devices such as desktop computers, smart speakers, cars, TV's, phones an Internet of Things (IOT) devices all have built in voice assistants and voice activated features. This research outlines an approach to securely identify and authenticate users of audio and voice operated systems that utilises existing cryptography methods and audio steganography in a method comparable to a PKI for sound, whilst retaining the usability associated with audio and voice driven systems.

Aerosol Optical Depth (AOD) is an significant parameter of aerosol optical properties. Operational production of AOD datasets over long time series, large-scale coverage puts on a severe challenge to computing technologies due to both the complexity of retrieval algorithm and the huge data amounts. The Grid computing solution-Remote Sensing Service Node (RSSN) was constructed as a high-throughput platform for remote sensing applications. Taking the sub-workflow level characteristics of some remote sensing retrieval applications into consideration, a sub-workflow parallel implementation for the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data was taken on the RSSN, and an initial experiment result proved that the subworkflow parallel could further reduce the runtime of data parallel solutions commonly used.

Asynchronous JavaScript and XML (AJAX) is a web development technique for building responsive web applications behaving in a similar fashion to traditional desktop applications. This poster illustrates ideas for teaching the AJAX technique in web-centric courses based on the experience of implementing these ideas at London Metropolitan University and provides links to resources appropriate for use in laboratorial work.

The paper proposes an approach to investigate the fine structure of the voltages circulating in the industrial network within the range of 300 kHz-20 MHz, which serves to do some measurements of real lines in real operating mode. It is shown that the interferences can be divided into basic- with spectral density changing slowly with frequency alteration and narrow-band (frequency-pulse). It is also indicated that the frequency concentrated interferences occupy usually 1-5% of the frequency range studied and their intensity is deeply modulated with the doubled frequency of the power supply voltage. There are areas with distinctly expressed low interferences around the zero transitions. The study is motivated by the search for sophisticated methods of high-speed data exchange through the power network.