The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

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Islamic marketing (IM) is gaining momentum in the development of marketing knowledge and its practical implication to business and customer is undeniably significant. In fact, it has been growing rapidly in the past years and has grabbed the attention of both academics and practitioners of marketing. Nevertheless, this area still remains understudied with limited empirical evidence, particularly on marketing mix decisions or four Ps (product, price, promotion and place) in line with Islamic principles. Therefore, this paper attempts to identify critical factors in conducting Islamic marketing mix activities and their relative importance. Using combination of both qualitative and quantitative research method, this paper proposed a set of 23-item instrument that measures Islamic marketing mix which has been empirically tested for its unidimensionality, reliability and validity through factorial analyses. Findings suggest that Muslim customers consider five critical factors that must be emphasized by businesses in conducting their marketing mix. In their order of importance using multiple regression analysis, the factors are ‘Conformity’, ‘Character’, ‘Commitment’, ‘Conscience’ and ‘Customer Centrism’. Therefore, it is proposed that businesses can adopt the strategies in engaging with Muslim customers through their marketing mix activities by consistently conforming to Syariah, exhibiting distinctive characteristics, instilling assurance, embedding morality and conscience and lastly embracing a ‘customer oriented’ approach.Keywords: Islamic Marketing; Marketing Mix; Factorial Analyses.

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.