Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. Then, a modified version of opposition-based learning technique has been applied to the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth. The proposed hybrid algorithm for global optimization problems is called GWO-HS. The GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. Then, the GWO-HS has been compared with recent HS variants and other wellknown metaheuristic algorithms. The results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.

Abstract-Using information systems effectively requires an understanding of the organisation, management, and the technology shaping the systems. All information systems can be described as organisational and management solutions to challenges posed by the environment.The advances in information systems have affect on our day-to day lives . As the technology is evolving immensely so are the opportunities in a healthy way to prepare the organisation in the competitive advantage environment In order to manage the IS/IT based systems, it is important to have an appropriate strategy that defines the systems and provide means to manage them. Strategic Information Systems Alignment (SISA) is an effective way of developing and maintaining the IS/IT systems that support the business operations.Alignment of the IS/IT plans and the business plans is essential for improved business performance, this research looks at the key features of SISA in the changing business circumstances in Saudi Arabia.

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