The advent of the Internet of Things (IoT) has revolutionised the way devices connect to each other, but it has also brought with it significant security concerns. Intrusion detection systems (IDS) have been deployed as a countermeasure against the increasing number of cyber attacks on IoT devices. Machine learning algorithms have shown promising capabilities in detecting these attacks. In this research paper, we present a comprehensive performance analysis of different machine learning algorithms used in IoT intrusion detection systems. Specifically, we evaluate six different algorithms: Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Gradient Boosting and XGBoost. Our evaluation focuses on their effectiveness in detecting fraudulent activities in the IoT domain. Several evaluation metrics such as accuracy, precision, recall, F1 score and Matthews correlation coefficient are used to assess performance. This comprehensive analysis helps improve IoT security and provides valuable insights into the efficiency of machine learning algorithms for intrusion detection. Our experimental results highlight the outstanding performance of the Random Forest model on all evaluation metrics.
Voice activity detection (VAD), is a signal processing technique used to determine whether a given speech signal contains voiced or unvoiced segments. VAD is used in various applications such as Speech Coding, Voice Controlled Systems, speech feature extraction, etc. For example, in Adaptive multi-rate (AMR) speech coding, VAD is used as an efficient way of coding different speech frames at different bit rates. In this paper, we implemented the application of a Zero-Phase Zero Frequency Resonator (ZP-ZFR) as VAD on hardware. ZP-ZFR is an Infinite Impulse Response (IIR) filter that offers the advantage of requiring a lower filter order, making it suitable for hardware implementation. The proposed system is implemented on the TIMIT database using the Nexys Video Artix-7 FPGA board. The hardware design is carried out using Vivado 2021.1, a popular tool for FPGA development. The Hardware Description Language (HDL) used for implementation is Verilog. The proposed system achieves good performance with low complexity. Therefore this work is implemented on hardware, which can be used in various applications.
Hyperspectral data plays a crucial role in various fields, including remote sensing, environmental monitoring and medical imaging. With the increasing importance of hyperspectral data, the need for secure transmission and storage has become imperative. Protecting the ownership and integrity of hyperspectral data is essential to prevent unauthorized access and ensure its reliability. This paper presents a novel approach to address these challenges by introducing a secure and efficient reversible watermarking method for ownership protection. The proposed method embeds ownership information into the hyperspectral data without affecting the spectral information. The technique involves three steps: embedding, extraction and restoration, and enables reversible watermark removal. The performance of the method is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Normalized Correlation (NC) and shows high ownership protection while maintaining visual quality and fidelity. The experimental results confirm the effectiveness of the proposed technique and show its robustness to various attacks and computational efficiency. This research helps preserve the integrity of hyperspectral data and provides a reliable solution for property protection in critical decisionmaking applications.
WebRTC videoconferencing has risen to prominence in recent years with a sharp increase in remote work and communication. Conferences with large numbers of participants and/or high-quality video streams can overload network and CPU resources and degrade performance, limiting the use of the technology. Several partial solutions exist to mitigate these issues, such as simulcast and Scalable Video Coding (SVC), but these solutions have shortcomings and require trade-offs that make them inadequate for some scenarios. We propose Demand-Aware Adaptive Streaming (DAAS), a novel method of increasing network and CPU efficiency that reduces stream quality—and thus resource consumption—at the source when possible. The method entails using WebRTC data channels to track the presented resolution of each video stream and adapting stream quality to match presentation requirements. The video publisher maintains full quality when required and adaptively reduces quality when possible. We show that the proposed method has advantages over existing methods in that it reduces network bandwidth and CPU requirements for all participants—senders and receivers—in a WebRTC videoconference.
Cognitive radio can be considered as a viable frequency access framework that overcomes the disadvantages of the licensed-based transmission procedure by allowing the secondary users to access the spectrum to transmit their data. However, due to the evolution of latency-sensitive real-time communication applications such as gaming and extended reality, it becomes more vital that cognitive radio networks should be studied with latency requirements on the data transmission. Recently, Age of Information has introduced itself as a important metric for evaluating the freshness of the transmitted data. In this paper, we investigate the latency and stability analysis of a two-user cognitive radio network that consists of one primary user, one secondary user and their destinations. The latency requirements of the transmitted data packets are taken into consideration by imposing Age of Information constraints on the data transmission of the users. We present two optimization problems, in the first problem, the secondary user stable throughput is maximized under an Age of Information constraint imposed on the data transmission of the secondary user. While, in the second problem, we maximize the stable throughput of the secondary user with respect to Age of Information constraints set on the data transmission of both the primary and secondary users. The resultant problems are found to be non-linear programming optimization problems. An appropriate algorithm is used to solve the problems and provide the numerical solutions. Our results characterize the impact of setting Age of Information constraints on the stability region of the network; we demonstrate that the stability region, in certain cases, is reduced by only 11% with strict Age of Information restrictions if compared to the scenario where no latency requirements is considered. Our results also show the potential accuracy of the algorithm adopted in this paper to solve the formulated optimization problems.
