Search

The development of Information and Communication Technologies (ICT) has now reached some entirely unexpected domains. Many applications in modern cities lead to novelties, resulting in new habits of the citizens of tomorrow's smart cities. These innovations include Intelligent Transportation Systems (ITS). An important application domain of ITS is undoubtedly represented by the dynamic and optimized management of traffic-lighted road intersections. Although several works have already been presented in the literature over the years, many have not considered the new perspectives regarding the source of data to manage traffic lights, i.e., not only Wireless Sensor Networks (WSNs), but, mainly, the vehicular communications. This paper introduces an innovative approach to dynamically regulating traffic light cycles and phases in an isolated intersection. The suggested method can fit data management from WSNs and vehicular communications through IEEE 802.11p and LTE-V2V, employing various Fuzzy Logic Controllers (FLCs) that manage vehicles turning movements for dynamic controls of both the phase and the green time of traffic lights. The results obtained will allow us to observe that the proposed application is better than the others. Each of the implemented configurations will bring advantages and disadvantages that allow choosing one configuration or another based on specific project requirements., Author(s): Fabio Arena [1]; Giovanni Pau [1]; Anca Ralescu [2]; Alessandro Severino (corresponding author) [3]; Ilsun You [4] 1. Introduction Many countries are experiencing a rapid increase in the growth [...]

The widespread and growing interest in the Internet of Things (IoT) may be attributed to its usefulness in many different fields. Physical settings are probed for data,which is then transferred via linked networks. There are several hurdles to overcome when putting IoT into practice, frommanaging server infrastructure to coordinating the use of tiny sensors.When it comes to deploying IoT, everyone agrees that security is the biggest issue. This is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory, processing power, and square footage. This research intends to analyse resource-constrained IoT devices, including RFID tags, sensors, and smart cards, and the issues involved with protecting them in such restricted circumstances. Using lightweight cryptography, the information sent between these gadgets may be secured. In order to provide a holistic picture, this research evaluates and contrasts well-known algorithms based on their implementation cost, hardware/software efficiency, and attack resistance features.We also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio. [ABSTRACT FROM AUTHOR]

The advancement of cellular communication technology has profoundly transformed human life. People can now watch high-definition videos anytime, anywhere, and aim for the implementation of advanced autonomous driving capabilities. However, the sustainability of such an environment is threatened by false base stations. False base stations execute attacks in the Radio Access Network (RAN) of cellular systems, adversely affecting the network or its users. To address this challenge, we propose a behavior rule specification-based false base station detection system, SMDFbs. We derive behavior rules from the normal operations of base stations and convert these rules into a state machine. Based on this state machine, we detect network anomalies and mitigate threats. We conducted experiments detecting false base stations in a 5G RAN simulator, comparing our system with seven machine learning-based detection techniques. The experimental results showed that our proposed system achieved a detection accuracy of 98% and demonstrated lower overhead compared to other algorithms. [ABSTRACT FROM AUTHOR]

The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system's high precision with a 99% detection rate in identifying anomalies. [ABSTRACT FROM AUTHOR]

Summary: Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance. [ABSTRACT FROM AUTHOR]

The Medical Internet-of-Things (MIoT) has developed revolutionary ways of delivering medical care to patients. An example system, showing increasing demand, is the artificial pancreas system that offers convenience and reliable support care to patients with Type 1 Diabetes. Despite the apparent benefits, the system cannot escape potential cyber threats that may worsen a patient's condition. The security risks need immediate attention to ensure the privacy of the patient and preserve safe functionality. Motivated by this, we proposed a security protocol for the APS environment wherein support to essential security requirements is guaranteed, the security context negotiation is resource-friendly, and the protocol is resilient to emergencies. Accordingly, the security requirements and correctness of the design protocol were formally verified using BAN logic and AVISPA, and proved its feasibility through the emulation of APS in a controlled environment using commercial off-the-shelf devices. Moreover, the results of our performance analysis indicate that the proposed protocol is more efficient than the other existing works and standards. [ABSTRACT FROM AUTHOR]

In a typical mobile-sensing scenario, multiple autonomous vehicles cooperatively navigate to maximize the spatial–temporal coverage of the environment. However, as each vehicle can only make decentralized navigation decisions based on limited local observations, it is still a critical challenge to coordinate the vehicles for cooperation in an open, dynamic environment. In this paper, we propose a novel framework that incorporates consensual communication in multi-agent reinforcement learning for cooperative mobile sensing. At each step, the vehicles first learn to communicate with each other, and then, based on the received messages from others, navigate. Through communication, the decentralized vehicles can share information to break through the dilemma of local observation. Moreover, we utilize mutual information as a regularizer to promote consensus among the vehicles. The mutual information can enforce positive correlation between the navigation policy and the communication message, and therefore implicitly coordinate the decentralized policies. The convergence of this regularized algorithm can be proved theoretically under certain mild assumptions. In the experiments, we show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly in the execution phase. The results validate that consensual communication plays very important role in coordinating the behaviors of decentralized vehicles. [ABSTRACT FROM AUTHOR]

Research to deal with distributed denial of service (DDoS) attacks was kicked off from long ago and has seen technological advancement along with an extensive 5G footprint. Prior studies, and still newer ones, in the realm of DDoS attacks in the 5G environment appear to be focused primarily on radio access network (RAN) and voice service network, meaning that there is no attempt to mitigate DDoS attacks targeted on core networks (CN) by applying artificial intelligence (AI) in modeling. In particular, such components of a CN as the Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF), all being principal functions enabled to provide 5G services as base stations do, provide expansive connectivity with geographically very large area coverage that cannot be matched by the base stations. Moreover, to complete re-registration for one UE, required messages in protocols Packet Forwarding Control Protocol (PFCP) and HTTP/2 are approximately 40 in number. This implies that a DDoS attack targeting the CN has, once accomplished, a greater than expected impact, when compared to DDoS attacks targeting the RAN. Therefore, security mechanisms for the CN must be put into practice. This research proposes a method, along with a threat detection system, to mitigate signaling DDoS attacks targeted on 5G SA (standalone) CNs. It is verified that the use of fundamental ML classifiers together with preprocessing with entropy-based analysis (EBA) and statistics-based analysis (SBA) enables us to proactively react against signaling DDoS attacks. Additionally, the evaluation results manifest that the random forest achieves the best detection performance, with an average accuracy of 98.7%. [ABSTRACT FROM AUTHOR]

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network. [ABSTRACT FROM AUTHOR]