186 research outputs found
Occupancy Controlled Lighting System for Smart Buildings
No full textEfficient utilization of the limited available energy in developing countries is a practical solution to the present challenges facing the power sector in the region. Automation of lighting systems in both residential and industrial buildings is one of the strategies for energy efficiency towards a sustainable economic development. In this paper, the authors developed an intelligent lighting system using occupancy control. The prototype consists of an Arduino uno microcontroller, Infrared module, Liquid Crystal Display (LCD), relay, buzzer, and a light bulb. The infrared module senses human presence in a room and transmits a corresponding electrical signal to the Arduino. The relay was connected to the Arduino to act as the control unit. The programmable microcontroller was also used to keep track of the number of people in the room while the information is displayed on the LCD. The system achieved a significant energy saving by switching ON the light bulb only when it is really needed. A large-scale implementation of this smart application in residential and office buildings will encourage energy efficiency, making electrical energy available for other areas which are yet to be connected to the power grid.
Aderemi A. Atayero, Victor Ademu-Eteh, Segun I. Popoola, Temitope O. Takpor, and Joke A. Badej
Federated deep learning for botnet attack detection in IoT networks
No full textThe wide adoption of the Internet of Things (IoT) technology in various critical infrastructure sectors has attracted the attention of cyber attackers. They exploit the vulnerabilities in IoT to form a network of compromised devices, known as botnet, which is used to launch sophisticated cyber-attacks against the connected critical infrastructure. Recently, researchers have widely explored the potentials of Machine Learning (ML) and Deep Learning (DL) to detect botnet attacks in IoT networks. However, there are still some challenges that need to be addressed in this area, which include the determination of optimal model hyperparameters, low classification performance due to imbalanced sample distribution in the training set, high memory space requirement for network traffic data storage, inability to detect zero-day attacks, and lack of data privacy. In order to address these problems, a Federated Deep Learning (FDL) method is developed for botnet attack detection in IoT-enabled critical infrastructure.
First, a hyperparameter optimisation method is developed for DL-based botnet attack detection in IoT networks to achieve high classification performance. The effectiveness of the method is evaluated using the Bot-IoT and N-BaIoT datasets, and the DL models achieved 99.99 ± 0.02% accuracy, 97.85 ± 3.77% precision, 98.72 ± 2.77% recall, and 97.72 ± 4.51% F1 score. Then, an oversampling algorithm is combined with DL models to improve the classification performance when the training data is highly imbalanced, without any significant increase in the overall computation time. This method improved the precision, recall, and F1 score of the DL models by 1.66-13.23%. Furthermore, a hybrid DL method is developed to reduce the amount of memory space required to store the network traffic data. This method reduced the memory space requirement for DL-based botnet attack detection by 86.45-98.26%. Finally, a FDL method, which also employed the hyperparameter optimisation, class balance, and memory space reduction methods, is developed to detect zero-day botnet attacks in IoT edge nodes, while preserving the data privacy of IoT users. The FDL models achieved high classification performance, and they had low communication overhead and low network latency
Consensus Algorithms and Deep Reinforcement Learning in Energy Market: a review
No full textBlockchain (BC) and artificial intelligence (AI) are often utilised separately in energy trading systems (ETS). However, these technologies can complement each other and reinforce their capabilities when integrated. This paper provides a comprehensive review of consensus algorithms (CA) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic related issues. Hence, the motivation to combine BC with AI to realise secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS
Hybrid Deep Learning for Botnet Attack Detection in the Internet of Things Networks
No full textDeep Learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained IoT devices. In this paper, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of Long Short-Term Memory Autoencoder (LAE). In order to classify network traffic samples correctly, we analyse the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep Bidirectional Long Short-Term Memory (BLSTM). Extensive experiments are performed with the BoT-IoT dataset to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92-27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model under-fitting and over-fitting. It also achieves good generalisation ability in binary and multi-class classification scenarios
Stacked recurrent neural network for botnet detection in smart homes
No full textInternet of Things (IoT) devices in Smart Home Network (SHN) are highly vulnerable to complex botnet attacks. In this paper, we investigate the effectiveness of Recurrent Neural Network (RNN) to correctly classify network traffic samples in the minority classes of highly imbalanced network traffic data. Multiple layers of RNN are stacked to learn the hierarchical representations of highly imbalanced network traffic data with different levels of abstraction. We evaluate the performance of Stacked RNN (SRNN) model with Bot-IoT dataset. Results show that SRNN outperformed RNN in all classification scenarios. Specifically, SRNN model learned the discriminating features of highly imbalanced network traffic samples in the training set with better representations than RNN model. Also, SRNN model is more robust and it demonstrated better capability to effectively handle over-fitting problem than RNN model. Furthermore, SRNN model achieved better generalization ability in detecting network traffic samples of the minority classes
Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment
No full textEmpirical models are most widely used for path loss predictions because they are simple, easy to use, and require less computational efficiency when compared to deterministic models. A number of empirical path loss models have been developed for efficient radio network planning and optimization in different types of propagation environments. However, data that prove the suitability of these models for path loss predictions in a typical university campus propagation environment are yet to be reported in the literature. In this data article, empirical prediction models are comparatively assessed using the path loss data measured and predicted for a university campus environment. Field measurement campaigns are conducted at 1800 MHz radio frequency to log the actual path losses along three major routes within the campus of Covenant University, Nigeria. Path loss values are computed along the three measurement routes based on four popular empirical path loss models (Okumura-Hata, COST 231, ECC-33, and Egli). Datasets containing measured and predicted path loss values are presented in a spreadsheet file, which is attached to this data article as supplementary material. Path loss prediction data of the empirical models are compared to those of the measured path loss using first-order statistics, boxplot representations, tables, and graphs. In addition, correlation analysis, Analysis of Variance (ANOVA), and multiple comparison post-hoc tests are performed. The prediction accuracies of the empirical models are evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error Deviation (SED). In conclusion, the high-resolution path loss prediction datasets and the rich data exploration provided in this data article will help radio network engineers and academic researchers to determine the empirical model that is most suitable for path loss prediction in a typical university campus environment. Keywords: Models, Forecasting, Path loss, Loss models, Radio propagation, Smart campu
Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment
No full textFederated Deep Learning for Collaborative Intrusion Detection in Heterogeneous Networks
No full textIn this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks
Secure IoT-based Smart Wheelchair System: From Implementation to Security Enhancement Strategy
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