This paper undertakes a systematic review to gain insight into existing studies on the application of Social Network Sites (SNS). Our systematic review of studies from 1995 to 2012 examines the background and trend of research in the area and provides critical factors that organizations should consider for effectively use social networking sites to communicate with their customers. We note a huge growth in the number of academic papers on the topic since 1998. Seventeen factors were identified as a result of review, which shaped two main themes: (i) A customercentric organizational culture, and (ii) SNS Know-How. The findings show that for a successful and effective use of SNSs, and in particular Facebook, a combination of good understanding of SNSs' tools and capabilities as well as a constant and transparent relationship with customers are essential. The findings show that for a successful and effective use of SNSs, and in particular Facebook, a combination of good understanding of SNSs' tools and capabilities as well as a constant and transparent relationship with customers are essential.

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Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.

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