IoT Protection Against Cyber Threats Based on Blockchain and Access Control: A Comprehensive Review

The Internet of Things (IoT) has undeniably transformed the way we interact with the world around us. As a revolutionary technology, it seamlessly integrates into our daily routines, offering unparalleled convenience and efficiency. By embedding connectivity into everyday objects, IoT has made it possible for devices to communicate, making our lives significantly easier. This constant communication and data exchange occur everywhere, from our homes to workplaces, and even in public spaces. Unfortunately, whenever connections increase, the threat of attacks increases too. Therefore, there is a critical need for systems that provide robustness at the service level. In this paper, a basic interface to IoT devices’ security architecture along with blockchain is introduced to provide scalability and authentication. This survey differs from the majority of existing reviews in that it presents a more comprehensive review of emerging research to help researchers and readers understand the state-of-the-art IoT protection against cyber threats. Additionally, different types of IoT protection against cyber threats based on blockchain and access control techniques are described in this paper. The findings demonstrate that blockchain technology offers IoT devices security along with scalability

An optimal clustering algorithm based distance-aware routing protocol for wireless sensor networks

Wireless Sensors Networks (WSN) consist of low power devices that are deployed at different geographical isolated areas to monitor physical event. Sensors are arranged in clusters. Each cluster assigns a specific and vital node which is known as a cluster head (CH). Each CH collects the useful information from its sensor member to be transmitted to a sink or Base Station (BS). Sensor have implemented with limited batteries (1.5V) that cannot have replaced. To resolve this issue and improve network stability, the proposed scheme adjust the transmission range between CHs and their members. The proposed approach is evaluated via simulation experiments and compared with some references existing algorithms. Our protocol seemed improved performance in terms of extended lifetime and achieved more than 35% improvements in terms of energy consumptio

A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset

Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best accuracy obtained for binary classification was 98.97% and that for multi-class classification was 98.41%. Furthermore, the results are compared with those from the related literature

A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset

Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best accuracy obtained for binary classification was 98.97% and that for multi-class classification was 98.41%. Furthermore, the results are compared with those from the related literature

A Comprehensive Study on Privacy and Security on Social Media

In many aspects of healthy, science, educational, functional, and social life, social media networks today are part of the human lifestyle. Social media has more impact on human life and introduced significant changes in the way people’s way of communication. People exchange a lot of information across social media networks, starting with the sharing of information with the growth of information sharing at the moment, and the advancement of technology Users create overt networks to reflect their current or new social connections. Users also upload and post a plethora of personal details. Maintaining the privacy and security of the user is a main challenge in social media. Users should feel the importance of preserving the privacy of their data and how valuable information such as banking details and confidential data should be kept away from social media. Users can also post personal information about others without their permission. The problem is exacerbated by users' lack of familiarity and knowledge, as well as the lack of appropriate resources and architecture of social media networks. This paper provides study on many privacy and security challenges encountered by social media networks and the privacy threats they pose, as well as current studies into possible solutions

Probabilistic Energy Value for Clustering in WirelessSensors Networks

Wireless sensors networks consist of a number of sensors nodes connected through a wireless network that collect datato be treated locally or relayed to the sink node using multi-hop wireless transmission. Several solutions were proposedto minimize the amount of information flowing within the network. Clustering algorithms is one solution andmechanism that enables the creation of sensor?s clusters; each sensor is dominated by elected routers. In order to limitenergy consumption, the clustering around the sensor is established: sensors linked to the router transmit relayed datathereafter outward. The number of messages sent and the transmission range are thus reduced. This article tackles thisissue by unveiling proposed techniques in the same line of researches and proposing a clustering mechanism based onthe amount of energy remaining in the sensors. The simulation results show that proposed method can achieve highernetwork lifetime by comparison to original LEACH

Synchronization in Wireless Sensors Networks Using balanced clusters

The advent of smart devices and continuousexpansions of smart environments make Wireless sensornetworks (WSNs) an important part of our daily lives. The usageof a myriad of devices in self-organizing networks in a variousfields such as home monitoring, medical and military etc.requires an efficient delivery of sensed information. For thisnecessity, a local clock of sensors nodes needs to be synchronizedand up-keep timely synchronization between sensors, to ensueseamlessly communication with each other via radio links aimedat sharing and treatment of reliable information. In this paper,we present balanced Timing-sync protocol for sensor networksthat aims at providing network-wide time synchronization in asensor network. Our schemes work in two steps. In the first step,a hierarchical structure is established in the edges of thisstructure to establish a global time scale through the network.Ultimately all node in the network synchronize their clock to areference node. We implement our algorithm on NS2. We arguethat our algorithm roughly gives better performance ascompared to the work in the same line of research like TPSN

Optimizing Quality of Service of Clustering Protocols in Large-Scale Wireless Sensor Networks with Mobile Data Collector and Machine Learning

The rise of large-scale wireless sensor networks (LSWSNs), containing thousands of sensor nodes (SNs) that spread over large geographic areas, necessitates new Quality of Service (QoS) efficient data collection techniques. Data collection and transmission in LSWSNs are considered the most challenging issues. This study presents a new hybrid protocol called MDC-K that is a combination of the K-means machine learning clustering algorithm and mobile data collector (MDC) to improve the QoS criteria of clustering protocols for LSWSNs. It is based on a new routing model using the clustering approach for LSWSNs. These protocols have the capability to adopt methods that are appropriate for clustering and routing with the best value of QoS criteria. Specifically, the proposed protocol called MDC-K uses machine learning K-means clustering algorithm to reduce energy consumption in cluster head (CH) election phase and to improve the election of CH. In addition, a mobile data collector (MDC) is used as an intermediate between the CH and the base station (BS) to further enhance the QoS criteria of WSN, to minimize time delays during data collection, and to improve the transmission phase of clustering protocol. The obtained simulation results demonstrate that MDC-K improves the energy consumption and QoS metrics compared to LEACH, LEACH-K, MDC maximum residual energy leach, and TEEN protocols