Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technologies have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. In recent years, various computing paradigms, e.g., mobile cloud computing (MCC), vehicular cloud computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Therefore, in this study, we provide a comprehensive overview of all computing paradigms related to vehicular networks. We also present the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we conclude the study with open research challenges in vehicular networks along with future research directions.

Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and off-peak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. As a result, a fairer system is realized, and the total revenue of the utility company is assured.

The increasing demand for communication between networked devices connected either through an intranet or the internet increases the need for a reliable and accurate network defense mechanism. Network intrusion detection systems (NIDSs), which are used to detect malicious or anomalous network traffic, are an integral part of network defense. This research aims to address some of the issues faced by anomaly-based network intrusion detection systems. In this research, we first identify some limitations of the legacy NIDS datasets, including a recent CICIDS2017 dataset, which lead us to develop our novel dataset, CIPMAIDS2023-1. Then, we propose a stacking-based ensemble approach that outperforms the overall state of the art for NIDS. Various attack scenarios were implemented along with benign user traffic on the network topology created using graphical network simulator-3 (GNS-3). Key flow features are extracted usingcicflowmeterfor each attack and are evaluated to analyze their behavior. Several different machine learning approaches are applied to the features extracted from the traffic data, and their performance is compared. The results show that the stacking-based ensemble approach is the most promising and achieves the highest weighted F1-score of 98.24%.

Meeting the energy needs of a growing population is of paramount importance in today’s society. The use of renewable energy sources, especially solar energy, can help reduce greenhouse gas emissions from traditional sources. Solar irradiance, which depends on several factors such as the availability of sunlight, cloud cover index, latitude, orientation and tilt of solar panels, and technical factors, plays a crucial role in the use of solar energy. Predicting solar irradiance is therefore essential for enhancing the reliability and efficiency of solar energy systems. Artificial intelligence (AI) and machine learning (ML) models are increasingly being used to predict solar irradiance forecasting. These models can learn from historical weather data and identify complex patterns to predict future solar irradiance with high accuracy. Techniques such as regression, neural networks and ensemble methods are frequently used and enable more efficient planning and operation of solar energy systems. This study provides a comprehensive analysis of the existing state-of-the-art models for solar irradiance forecasting. The study evaluates a variety of forecasting models, including machine learning, numerical weather prediction and hybrid approaches for assessing their accuracy, strengths and weaknesses. As an outcome of this study, potential future improvements in the prediction of solar irradiance are highlighted and the importance of interdisciplinary collaboration and emerging technologies is emphasized. To ensure a sustainable and resilient energy future, it is crucial to continue efforts to better integrate solar energy into mainstream electricity systems. This work can serve as a fundamental analysis for future researchers to identify the most appropriate approaches for medium and long-term solar irradiance forecasts.