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Internet of Things (IoT) as an emerging technology is based on the idea that smart things can connect to the Internet and exchange the collected data in a peer-to-peer paradigm. Due to its inherent features, IoT can be utilized in real-world scenarios and its expansion can improve human well-being. Internet of things is applied quite closely to humans and transmits serious information such as healthcare information, financial data, and private information through an insecure communication platform. Since almost all tasks are performed with minimal human intervention, and adversary may deploy its nodes among other legitimate elements of IoT, providing an effective mutual authentication is vital. In this Systematic Literature Review, authentication of IoT and its literature are reviewed systematically. In particular, it has endeavored that the collected literature covers the papers conducted from 2018 to 2022. Moreover, this study seeks to provide a comprehensive answer to six important Research Questions in the context of authentication of IoT that often engage the minds of scholars. It is hoped that this survey will be an effective guide for future research by addressing the relevant challenges, analyzing open issues, and providing future research directions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT), as an emerging technology, describes a smart world that enables objects to interact with each other and with end-users through developed communication platforms such as the Internet. The limited communication range of things allows packets to be transmitted between objects that are not in each other's range through intermediate objects, which highlights the necessity of routing in this technology. Therefore, the Quality of Service parameters of IoT entirely depend on the cooperation of things in the routing process. Several factors such as energy depletion, conquering objects by an enemy, and resource constraints lead to selfish behavior that may cause network collapse. In this paper, a new watchdog-based identification method is presented to diagnosis selfish objects in the Internet of Things, in which the selected watchdogs are responsible for evaluating the activity of other objects. In the proposed method to achieve a uniform distribution of watchdog objects in the network, the IoT objects are partitioned into disjoint cells using the Voronoi diagram, and a watchdog is selected for each cell. In this way, while the supervision overhead is distributed, the watchdog objects can identify the selfish things through examining the remaining energy and cooperation degree of other objects. The conducted simulations in the MATLAB tool have validated the effectiveness of the proposed method in terms of detection rate, false-positive rate, throughput, end-to-end delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Efficiency of position-based emergency message broadcast protocols of Vehicular Ad-hoc Networks (VANET) is affected by the position of vehicles that depends entirely on accuracy of the positioning system. In addition, GPS-based dissemination protocols encounter many challenges due to high-energy consumption, low accuracy, and the need for up-to-date maps for real-world automatic applications. In this paper, a Route Segmented Broadcast Protocol based on Radio Frequency identification technology (RSBP-RF) is proposed to eliminate GPS-based problems and increase the efficiency of emergency message broadcasting by improving the positioning accuracy. In this protocol, embedded passive RFID tags are used to segment the route and provide a position-aware technique. In RSBP-RF, each vehicle is informed of its position by crossing on the tags and immediately broadcasts a beacon to its neighbors. Moreover, the proposed protocol includes a proactive relay vehicle selection scheme that uses the received beacons to select the farthest neighboring node. The results of the conducted simulations in the NS-3 tool have validated the effectiveness of the proposed protocol in terms of end-to-end delay, delivery ratio, one-hop delay, collision ratio, relay node selection accuracy, and dissemination performance. Moreover, analysis of the time complexity and message complexity proves the lightness of the RSBP-RF protocol. [ABSTRACT FROM AUTHOR]

Generally, the ad hoc networks work correctly only if all nodes cooperate in the routing and forwarding of the packets. However, in some cases, selfish nodes are hesitated to share the resources with their neighbors and attempt to preserve their own assets, since splitting nodes into two distinct groups including absolute cooperator and absolute selfish and avoiding the selfish nodes group in network activities will be degraded network quality of service. In this paper, we propose a novel fuzzy-based selfish node detection approach for ad hoc networks that operates based on the social network principles. In the social-based proposed approach, the nodes' status will be determined by three variables, i.e., hop count (H.C.), residual energy (Re-En.), and cooperation history (Co-h.), through fuzzy interface process to prevent the isolating of the likely selfish nodes from the network and maintain more active node in the network as possible. The MobEmu tool is used to evaluate the effectiveness of the proposed approach in terms of the hit rate, delivery latency, delivery cost, and average hop count. The simulation results reveal that the proposed approach has a significant improvement in contrast to counterparts using UPB 2011 and UPB 2012 traces. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology connects a wide range of various elements that have a unique identifier to create an intelligent computing system. Given that most security mechanisms such as encryption and authentication are entirely based on shared keys, key distribution and management play an important role in their performance. In this paper, a lightweight hierarchical key management approach is proposed for IoT technology that is based on the position and remaining energy of nodes and includes three phases, key server selection, nodes clustering, and key generation and distribution. The proposed approach generates shorter and more secure keys due to the use of a hierarchical structure. The conducted simulations in Cooja have validated effectiveness of the proposed approach in comparison to counterpart methods in terms of energy consumption, end-to-end delay, and network lifetime. According to the formal analysis through ROR model and informal security analysis, proposed approach provides perfect forward secrecy, and resists impersonation, man-in-the-middle, replay, and key guessing attacks. Moreover, analysis of time complexity, message complexity, computational cost, and storage cost indicates that overhead of the proposed approach is negligible. [ABSTRACT FROM AUTHOR]

The positioning techniques and adjusting sensing radius of nodes have an important role in improving the network coverage of hybrid wireless sensor networks. In this paper, we propose a novel optimized bidding-based algorithm that able to identify the coverage holes after deploying the stationary sensor nodes, also adjust sensing radius of nodes effectively based on the Delaunay triangulation. In addition, if necessary, an endeavor is made to relocate a portion of mobile nodes at the appropriate positions to cover the holes and reduce the overlapping area. Furthermore, the structure of created triangles i.e. acute and obtuse, layout vertices, region coverage status in before and after the relocation are considered in moving strategy of the proposed algorithm. Extensive simulations have been performed to validate the effectiveness of the proposed algorithm in terms of the coverage ratio, number of moved nodes, number of alive nodes and average of nodes movement. [ABSTRACT FROM AUTHOR]

• Providing a lightweight RFID-based authentication approach for IoT application to address the computation and hardware limitations. • Applying nonlinear key-stream generators to eliminate the linearity property of LFSR-based generators. • Evaluating the efficiency of the proposed approach based on security analysis and statistical analysis. • Assessment of the effect of generator polynomial degree and the proposed combinational generators on the BER using the Cooja tool. • Assessment of overhead of the proposed approach in terms of computational cost, storage spaces, and communication costs. In this paper, a new lightweight RFID-based authentication approach is proposed that uses stream ciphering techniques to preserve privacy between legitimate components. The proposed approach uses various key-stream generators such as And2LFSR, Geffe, and majority-based to eliminate the inherent linearity of LFSRs and construct a non-linear Pseudo-Random Number Generators (PRNG). The informal security analysis indicates that the proposed approach provides forward security and resists eavesdropping, tag tracking, replay, cloning, and DoS attacks. Moreover, simulations conducted through Cooja tool illustrate the effect of generator polynomial degree and proposed combinational generators on BER. The BER of communications has been evaluated in different quantities of (E b / N 0), ranged between 1 and 10, using the proposed generators along with different error control techniques. In addition, evaluations indicate that the proposed approach outperforms in terms of computational cost, storage spaces, and communication cost. Furthermore, statistical analysis proves resistance of the proposed approach to guessing attacks. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: In most fault-detection algorithms in wireless sensor networks (WSNs), each sensor compares its data with the data of neighboring nodes. The majority of comparative methods will not work properly if more than half of the neighboring nodes of a sensor are faulty. Moreover, such comparative methods are unable to detect common mode failures (CMFs). Hence, having noticed the deficiencies of the existing comparative methods and as a reaction against such problems, we introduced a novel self-diagnosing approach to reduce the effect of neighboring node’s data in determining the status of nodes so that a sensor’s status will be determined independently of any comparisons. A sensor will be deemed to be fault-free if its components and the inner links between the components are flawless. In this paper, the behaviors of the components of a sensor are independently analyzed by means of the proposed model based on Petri nets and the links of the sensor’s components are investigated by means of the correlation graph. In addition, the authors extended and generalized the proposed method to all the nodes of a network and evaluated their operation. Simulation results showed that the modeling implemented by the HPSim tool can cover both permanent and transient faults accurately. Moreover, using the correlation graph and Pearson correlation coefficient helped us to gain confidence in the correctness of the inner links between the components of a sensor. Evaluation of the results indicated that the statistical results from the QI Macros tool were substantially similar to those from HPSim and Matlab tools. Furthermore, simulations results demonstrated that the detection accuracy and false alarm rate of the proposed method is acceptable even when the fault probability of each sensor is generally to be high. As a result, using these mechanisms leads to the development of the self-diagnosing capability in the sensors of a WSN. [Copyright &y& Elsevier]

The Internet of Things (IoT) implies a new model and concept in the world of information and communication technology that the non-cooperation of some nodes in providing services to other nodes may result in the disconnection of some things with each other. Consequently, the proximity of such nodes is considerably reduced the network performance. In this paper, a multiphase method based on game theory and direct and indirect reputation has been designed to stimulate selfish and malicious nodes to cooperate in the Internet of Things, which starts with setting up nodes in the IoT network. In the first phase, nodes are grouped in clusters with cluster heads to collect data. Then, they play a dynamic and multi-person game when they promote their own data packet or others' data packet in the second phase (multi-person game phase and data packet sending). The result of the game is applied to affect between nodes in the next phase. Nodes can pick their strategy when data packet forwarding in the third phase (direct and indirect reputation update). Nodes will update the neighboring node and the reputation table if they receive a confirmation message from the destination declaring that the data packet has a direct reputation. The results show that the accuracy of selfish node detection has increased by average of 8% and the false positive rate is 7% in comparison to comparative methods. [ABSTRACT FROM AUTHOR]

A wide range of network technologies and equipment used in network infrastructure are vulnerable to Denial of Service (DoS) attacks. Therefore, the identification of these attacks is of particular importance in security systems. Most of the previously presented solutions use a single machine learning model to detect DoS attacks; but it seems that improving the detection accuracy and reliability in the intrusion detection system will be possible by using the combination of learning models. This research, is an effort to improve the accuracy of DoS attacks detection, compared to previous methods. Also, overcoming the challenge of large number of classes in intrusion detection task using ECOC based hybrid classifiers is one of the main objectives of the research. In this paper, a novel method to detect DoS attacks in computer networks is proposed. The proposed method performs the intrusion detection process in three phases named as pre-processing, feature extraction and classification. Principal Component Analysis (PCA) is used for extracting features, while a combination of Error Correcting Output Codes (ECOC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for classification. In this classification model, Particle Swarm Optimization (PSO) algorithm has been used to optimize the structure of ANFIS. The performance of the proposed method has been evaluated using the NSLKDD database. Using a 10-fold cross validation experiment, the proposed IDS showed a sensitivity of 99.82%. The results also show that the proposed method can detect the types of DoS attacks with an average accuracy of 98.9%, which shows a significant improvement compared to the previous methods. [ABSTRACT FROM AUTHOR]