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Advances in synthetic biology and nanotechnology have contributed to the design of tools that can be used to control, reuse, modify, and re-engineer cells' structure, as well as enabling engineers to effectively use biological cells as programmable substrates to realize Bio-Nano Things (biological embedded computing devices). Bio-NanoThings are generally tiny, non-intrusive, and concealable devices that can be used for in-vivo applications such as intra-body sensing and actuation networks, where the use of artificial devices can be detrimental. Such (nano-scale) devices can be used in various healthcare settings such as continuous health monitoring, targeted drug delivery, and nano-surgeries. These services can also be grouped to form a collaborative network (i.e., nanonetwork), whose performance can potentially be improved when connected to higher bandwidth external networks such as the Internet, say via 5G. However, to realize the IoBNT paradigm, it is also important to seamlessly connect the biological environment with the technological landscape by having a dynamic interface design to convert biochemical signals from the human body into an equivalent electromagnetic signal (and vice versa). This, unfortunately, risks the exposure of internal biological mechanisms to cyber-based sensing and medical actuation, with potential security and privacy implications. This paper comprehensively reviews bio-cyber interface for IoBNT architecture, focusing on bio-cyber interfacing options for IoBNT like biologically inspired bio-electronic devices, RFID enabled implantable chips, and electronic tattoos. This study also identifies known and potential security and privacy vulnerabilities and mitigation strategies for consideration in future IoBNT designs and implementations., 41 pages, 9 tables, 6 figures

The emergence of augmented reality (AR) and virtual reality (VR) has revolutionized the trends in computing devices and modern technologies drastically. With this revolution, there is a need to extend existing architectures of security to serve as the key protective feature in all computing devices. In this experimental study, the aim is to develop a novel authentication technique with a fusion of graphical doodle password approach and AR environments. The mash-up of both doodle passwords and AR in a 3D space gives a promising direction to set off to a modern, more usable, and satisfying authentication techniques. The proposed approach works on real-time size and coordinate matching of doodles in an AR environment for the authentication of users. The creation of doodle passwords in an AR space is carried on by touch-gesture-recognition on a smartphone. The usability and usefulness of the proposed technique is evaluated by conducting an extensive survey, based on tasks and user experience assessments. The randomized-post-test-only study model is used to conduct experimentation that is also followed by the analysis of security parameters with the help of confusion matrix. The obtained results predict the use of AR during the authentication process more satisfying for users, where the proposed technique is useful, usable, and secure in comparison to the existing authentication approaches. This paper also highlights the importance of research needed for the utilization of modern techniques during the creation of security frameworks.

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

High dimensionality problem in spectra datasets is a significant challenge to researchers and requires the design of effective methods that can extract the optimal variable subset that can improve the accuracy of predictions or classifications. In this study, a hybrid variable selection method, based on the incremental number of variables using bootstrapping soft shrinkage method (BOSS) and interval random variable selection (IRVS) method is proposed and named BOSS-IRVS. The BOSS method is used to determine the informative intervals, while the IRVS method is used to search for informative variables in the informative interval determined by BOSS method. The proposed BOSS-IRVS method was tested using seven different public accessible near-infrared (NIR) spectroscopic datasets of corn, diesel fuel, soy, wheat protein, and hemoglobin types. The performance of the proposed method was compared with that of two outstanding variable selection methods i.e. BOSS and hybrid variable selection strategy based on continuous shrinkage of variable space (VCPA-IRIV). The experimental results showed clearly that the proposed method BOSS-IRVS outperforms VCPA-IRIV and BOSS methods in all tested datasets and improved the percentage of the prediction accuracy, by 15.4 and 15.3 for corn moisture,13.4 and 49.8 for corn oil, 41.5 and 50.6 for corn protein, 12.6 and 5.6 for soy moisture, 0.6 and 6.3 for total diesel fuel, 19.9 and 14.3 for wheat protein, and 5.8 and 20.3 for hemoglobin. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

An overwhelmed number of vehicles has wrecked the current system of transportation due to rapid growth in population. Smart cities are the novel innovation that is inevitable to curb the problems of traffic jams, unorganized traffic, environmental pollution, and slow response rate to emergency situations. The intelligent transportation system (ITS) is an integral part of smart cities allowing communications and interaction among vehicles. An autonomous vehicle is the key element of ITS and the mass implementation of this emerging technology is the solution to traffic problems linked to the current transportation system. Autonomous vehicles lead to the need for efficient and reliable external vehicular communications particularly through vehicular ad hoc networks (VANET). However, utilizing a suitable routing protocol to provide stable routing and efficient performance for vehicular communications in autonomous vehicles is a key factor. Routing protocols are particularly important for establishing vehicular to vehicular and vehicular to infrastructure (V2X) communication, which is incredibly challenging due to the movement of nodes. The quality of inter-vehicular communications is widely affected by numerous factors such as routing protocols, traffic environment, and traffic density. This article presents a detailed evaluation of three commonly used protocols, i.e., Ad-hoc On-demand Distance Vector Routing (AODV), Dynamic Source Routing (DSR), and Destination-Sequenced Distance-Vector Routing (DSDV) under three different traffic environments. To investigate the performance of these routing protocols under diverse environments, simulations are extended further by using the varying density of vehicles. This study aims at finding the best routing protocol for efficient and reliable packet dissemination among vehicles under different scenarios.

Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.

Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform. 2013 IEEE. The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through the Vice Deanship of Scientific Research Chair. Scopus 2-s2.0-85067231140

The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one's car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloud-based applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking.

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

Supervised intrusion detection system is a system that has the capability of learning from examples about the previous attacks to detect new attacks. Using artificial neural network (ANN)-based intrusion detection is promising for reducing the number of false negative or false positives, because ANN has the capability of learning from actual examples. In this paper, a developed learning model for fast learning network (FLN) based on particle swarm optimization (PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training extreme learning machine and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning.

Smart devices, the carriers of a huge amount of private, sensitive and confidential data are pervasive in today's world with innovative and enhanced functionalities. Smartphones have brought tremendous change in people's lives with the launch of a new platform of communication and an ease of access to a wide range of applications. Due to the swift increase in the users of Android smartphones and the increasing demands based on advanced ease and features, developers are working hard to achieve the needful. Easy access to certain features and applications gave rise to the powerfulness and an efficacy of various threats, risks and vulnerabilities that can victimize users' private data residing in smartphone paradigm. With the developments and enhancements in malware, for Android-based smartphones, attacks continue to occur. In this paper, we investigate one of the possibly most destructive attacks for Android, that is, screenshot attack. We have developed “ScreenStealer”application and explored the vulnerabilities which make Android more inclined to risks and threats. Furthermore, we evaluated capture ratio of screenshots, resources consumption and execution time to determine effectiveness, efficiency and stealthiness of such a malicious application.