In the dynamic landscape of cyber threats, multistage malware botnets have surfaced as significant threats of concern. These sophisticated threats can exploit Internet of Things (IoT) devices to undertake an array of cyberattacks, ranging from basic infections to complex operations, such as phishing, cryptojacking, and Distributed Denial-of-Service (DDoS) attacks. Existing machine learning solutions are often constrained by their limited generalizability across various data sets and their inability to adapt to the mutable patterns of malware attacks in real world environments, a challenge known as model drift. This limitation highlights the pressing need for adaptive intrusion detection systems (IDSs), capable of adjusting to evolving threat patterns and new or unseen attacks. This article introduces MalBoT-DRL, a robust malware botnet detector using deep reinforcement learning (RL). Designed to detect botnets throughout their entire lifecycle, MalBoT-DRL has better generalizability and offers a resilient solution to model drift. This model integrates damped incremental statistics with an attention reward mechanism, a combination that has not been extensively explored in the literature. This integration enables MalBoT-DRL to dynamically adapt to the ever-changing malware patterns within IoT environments. The performance of MalBoT-DRL has been validated via trace-driven experiments using two representative data sets: 1) MedBIoT and 2) N-BaIoT, resulting in exceptional average detection rates of 99.80% and 99.40% in the early and late detection phases, respectively. To the best of our knowledge, this work introduces one of the first studies to investigate the efficacy of RL in enhancing the generalizability of IDS.

Anonymous networks provide security for users by obfuscating messages with encryption and hiding communications amongst cover traffic provided by other network participants. The traditional goal of academic research into these networks has been attacks that aim to uncover the identity of network users. But the success of an anonymous network relies not only on it's technical capabilities, but on adoption by a large enough user base to provide adequate cover traffic. If anonymous network nodes can be identified, the users can be harassed, discouraging participation. Tor is an example of widely used anonymous network which uses a form of Onion Routing to provide low latency anonymous communications. This paper demonstrates that traffic from a simulated Tor network can be distinguished from regular encrypted traffic, suggesting that real world Tor users may be vulnerable to the same analysis.

Since the advent of modern computing, researchers have striven to make the human–computer interface (HCI) as seamless as possible. Progress has been made on various fronts, e.g., the desktop metaphor (interface design) and natural language processing (input). One area receiving attention recently is voice activation and its corollary, computer-generated speech. Despite decades of research and development, most computer-generated voices remain easily identifiable as non-human. Prosody in speech has two primary components—intonation and rhythm—both often lacking in computer-generated voices. This research aims to enhance computer-generated text-to-speech algorithms by incorporating melodic and prosodic elements of human speech. This study explores a novel approach to add prosody by using machine learning, specifically an LSTM neural network, to add paralinguistic elements to a recorded or generated voice. The aim is to increase the realism of computer-generated text-to-speech algorithms, to enhance electronic reading applications, and improved artificial voices for those in need of artificial assistance to speak. A computer that is able to also convey meaning with a spoken audible announcement will also improve human-to-computer interactions. Applications for the use of such an algorithm may include improving high-definition audio codecs for telephony, renewing old recordings, and lowering barriers to the utilization of computing. This research deployed a prototype modular platform for digital speech improvement by analyzing and generalizing algorithms into a modular system through laboratory experiments to optimize combinations and performance in edge cases. The results were encouraging, with the LSTM-based encoder able to produce realistic speech. Further work will involve optimizing the algorithm and comparing its performance against other approaches.

The Internet’s default inter-domain routing system, the Border Gateway Protocol (BGP), remains insecure. Detection techniques are dominated by approaches that involve large numbers of features, parameters, domain-specific tuning, and training, often contributing to an unacceptable computational cost. Efforts to detect anomalous activity in the BGP have been almost exclusively focused on single observable monitoring points and Autonomous Systems (ASs). BGP attacks can exploit and evade these limitations. In this paper, we review and evaluate categories of BGP attacks based on their complexity. Previously identified next-generation BGP detection techniques remain incapable of detecting advanced attacks that exploit single observable detection approaches and those designed to evade public routing monitor infrastructures. Advanced BGP attack detection requires lightweight, rapid capabilities with the capacity to quantify group-level multi-viewpoint interactions, dynamics, and information. We term this approach advanced BGP anomaly detection. This survey evaluates 178 anomaly detection techniques and identifies which are candidates for advanced attack anomaly detection. Preliminary findings from an exploratory investigation of advanced BGP attack candidates are also reported.