# Security and Privacy in Wireless and Mobile Networks
###### Abstract  
Currently, at the dawn of 5G networks, and the era of the Internet-of-Things, wireless and mobile networking is becoming increasingly ubiquitous. In this landscape, security and privacy turn into decisive factors. That is, the mobile and wireless ecosystem is an ideal playground for many perpetrators: (a) handheld devices are used for critical tasks, such as e-commerce, bank transactions, payments, application purchases, as well as social interaction; (b) such devices uniquely identify their users and store sensitive and detailed information about them; and (c) despite all their sophistication, native security mechanisms of mobile operating systems can be bypassed, and several wireless interfaces and protocols have been proven to be vulnerable to attack. As the attacker is given so many alternative entry points for penetration, the creation of assaults against the end-user and the underlying systems and services have been augmented, both in amount, as well as in matters of complexity. It is, therefore, imperative that new and advanced security and privacy-preserving measures are deployed. To cope with the aforementioned challenges, this special issue has been dedicated to the security and privacy aspects of mobile networks, wireless communications, and their apps. Particularly, apart from network and link layer security, the focus is on the security and privacy of mobile software platforms and the increasingly differing spectrum of mobile or wireless apps. Via both invited and open call submissions, a total of nineteen papers were submitted and nine have been finally accepted. Each manuscript underwent a rigorous review process involving a minimum of three reviews. All the accepted articles constitute original research work addressing a variety of topics pertaining to the above-mentioned challenges. The first article by [PERSON], [PERSON] and [PERSON] [1], focuses on collaborative intrusion detection networks (CIDN), which allow intrusion detection system nodes to exchange data with each other. The authors deal with insider attacks which typically are more difficult to identify. Particularly, by examining challenge-based CIDNs, they analyze the influence of advanced on-off attacks, where the attacker responds truthfully to one IDS node but behaves maliciously to another. The authors report results from two experiments using both simulated and real CIDN environments. The work by [PERSON], [PERSON], [PERSON] and [PERSON] [2], copes with Sybil types of attacks in mobile Wireless Sensor Networks (WSN), and proposes a computationally lightweight watchdog-based algorithm for detecting it. According to the authors' algorithm, the watchdog nodes collect detection information, which is then passed to a designated node for processing and identifying Sybil nodes. The highlights of their algorithm are the low communication overhead, and a fair balance between true and false detection rates. These qualities are proved via simulation and comparison against recent watchdog-based Sybil detection algorithms. End-user privacy protection in smart home applications is the topic of the article contributed by [PERSON], [PERSON], [PERSON], and [PERSON] [3]. Given that attacks do not necessarilyneed access to the cipher, but can be mounted by simply analyzing the frequency of radio signals or the timestamp series, the authors argue that legacy encryption methods cannot satisfy the needs of privacy protection in such applications. Therefore, the daily activities of the people living in a smart home are at stake. To obfuscate the patterns of daily routines of smart home residents, they propose an adaptive method based on sample data analysis and supervised learning, which allows them to cope with fingerprint and timing-based snooping types of attacks. Via experimentation, the authors demonstrate that their method supersedes similar proposals in terms of energy consumption, latency, adaptability, and degree of privacy protection.