Editorial: Securing Internet of Things Through Big Data Analytics

Muhammad Alam, Ting Wu, Fazl Ullah, Yuanfang Chen
2019 Journal on spesial topics in mobile networks and applications  
The "IoT" heralds the connections of a nearly countless number of devices to the Internet thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks [1] . The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analyzed
more » ... a is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is of them. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth. One of the main challenge of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyze, detect and defend cyber-attacks in the distributed IoT systems. European Alliance for Innovation (EAI) took a step towards the realization of Future Intelligent Vehicular Technologies based on dependable and real-time communication, IoT, Big data and other related technologies and invite both academic and industrial research community by organizing the 2nd edition of Future 5V conference in Islamabad, Pakistan. Future 5V is an annual international conference by EAI (European Alliance for Innovation) and co-sponsored by Springer. Future 5V attracted more than 150 research articles in field of Vehicular networks/communications covering theory and practices in the after mentioned field of study. This special issue was organized for the extended and invited papers from Future5V conference held in Islamabad, Pakistan. Following are the details of accepted papers in this special issue. The first paper tittled "Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping" presents a comprehensive steganography (the hiding of data within a cover) [2] . A new algorithm for processing feature that makes two optimizations into a random vector functional link (RVFL) network is proposed in this paper. The first optimization locates the processing phase of RVFL, where we model the eigenspectrum by the eigenvalue distribution of the scatter matrix. This eigenspectrum is used to generate the transpose matrix and obtain final features after feature reduction. The second optimization is the use of the random subspace Fisher linear discriminant (FLD) instead of random weights in RVFL. The weights between the input and enhancement nodes more accurately represent the relative importance of the features. In the experiments, the authors have compared the performance of other classifiers with the proposed method using five high-dimensional features. It is shown that the proposed method outperforms other classifiers in these steganalysis methods. The 2nd accepted paper is about evolutionary game theoretic incentive mechanism to promote the cooperation of individual users to curb the expansion of unwanted traffic [3] . The existence of malicious users seriously undermine user privacy and network security by distributing a large amount of unwanted traffic, such as spam, popup, and malware. This, to some extent, can be identified with the cooperation of individual users by installing anti-virus toolkits. However, users need to purchase such software at an additional cost. Therefore, unless builtin incentive mechanisms exist, rational users will choose not to install virus software. If enough network entities
doi:10.1007/s11036-019-01279-7 fatcat:uy72lpnvvze7jo6fs5mrzqinqy