Ensemble technique of intrusion detection for IoT-edge platform.

Internet of Things (IoT) technology has revolutionized modern industrial sectors. Moreover, IoT technology has been incorporated within several vital domains of applicability. However, security is overlooked due to the limited resources of IoT devices. Intrusion detection methods are crucial for detecting attacks and responding adequately to every IoT attack. Conspicuously, the current study outlines a two-stage procedure for the determination and identification of intrusions. In the first stage, a binary classifier termed an Extra Tree (E-Tree) is used to analyze the flow of IoT data traffic within the network. In the second stage, an Ensemble Technique (ET) comprising of E-Tree, Deep Neural Network (DNN), and Random Forest (RF) examines the invasive events that have been identified. The proposed approach is validated for performance analysis. Specifically, Bot-IoT, CICIDS2018, NSL-KDD, and IoTID20 dataset were used for an in-depth performance assessment. Experimental results showed that the suggested strategy was more effective than existing machine learning methods. Specifically, the proposed technique registered enhanced statistical measures of accuracy, normalized accuracy, recall measure, and stability.

Distributed Blockchain-Based Platform for Unmanned Aerial Vehicles.

Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control systems must be protected to avoid potential vulnerabilities and improve coordination among scattered UAVs in the IoT context. In the current paper, a distributed UAV scheme is proposed that uses blockchain technology and a network topology similar to the IoT and cloud server to secure communications during data collection and transmission and reduce the likelihood of attack by maliciously manipulated UAVs. As an alternative to relying on a traditional blockchain approach, a unique, safe, and lightweight blockchain architecture is proposed that reduces computing and storage requirements while keeping privacy and security advantages. In addition, a unique reputation-based consensus protocol is built to assure the dependability of the decentralized network. Numerous types of transactions are established to characterize diverse data access. To validate the presented blockchain-based distributed system, performance evaluations are conducted to estimate the statistical effectiveness in the form of temporal delay, packet flow efficacy, precision, specificity, sensitivity, and security efficiency.

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused on analyzing mobile GPS data to accomplish this task. While this approach may guarantee high accuracy from the perspective of the data, it is considered inefficient since knowing the object's absolute geographic location is not required to accomplish this task. This work proposed the implementation of the unsupervised learning-based algorithm, namely convolutional autoencoder, to infer the co-location of people from a low-power consumption sensor data-magnetometer readings. The idea is that if the trained model can also reconstruct the other data with the structural similarity (SSIM) index being above 0.5, we can then conclude that the observed individuals were co-located. The evaluation of our system has indicated that the proposed approach could recognize the spatial co-location of people from magnetometer readings.

An intelligent healthcare system with data priority based on multi vital biosignals.

BACKGROUND AND OBJECTIVE: Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction. METHODS: The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients' urgency. RESULTS: Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow. CONCLUSION: The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.

Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities.

Information Centric Network (ICN) is expected to be the favorable deployable future Internet paradigm. ICN intends to replace the current IP-based model with the name-based content-centric model, as it aims at providing better security, scalability, and content distribution. However, it is a challenging task to conceive how ICN can be linked with the other most emerging paradigm, i.e., Vehicular Ad hoc Network (VANET). In this article, we present an overview of the ICN-based VANET approach in line with its contributions and research challenges.In addition, the connectivity issues of vehicular ICN model is presented with some other emerging paradigms, such as Software Defined Network (SDN), Cloud, and Edge computing. Moreover, some ICN-based VANET research opportunities, in terms of security, mobility, routing, naming, caching, and fifth generation (5G) communications, are also covered at the end of the paper.

GDTN: Genome-Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter-UA Collaboration.

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment.

A New Random Walk for Replica Detection in WSNs.

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.