Search Results (4)

The Internet of Things (IoT) networks are the most prone to internal as well as external attacks in addition to energy consumption problems. Conventional security solutions are not able to address these issues effectively due to the limited resources of sensor nodes participating in IoT communications. In this work, an Efficient and Secure Fog Based Routing Mechanism (ESFRM) is proposed to protect the network from faulty internal as well as external attacks. Every node participating in IoT communications calculates the comprehensive trust value of the next intermediate node which is the addition of direct trust, indirect trust and energy trust values before forwarding the data. This comprehensive trust value is then compared with the comprehensive threshold trust value to decide whether the particular node is a rogue node or a valid normal node. Further, an enhanced RSA (Rivest, Shamir, Adleman) algorithm is implemented to provide three levels of data security from Cluster Head (CH) to fog node, from fog node to cloud server and directly from CH to cloud server. For this purpose, an efficient CH selection technique is also proposed in this work. The proposed methodology is compared with the Secure Energy-efficient Fog-based Routing (SEFR) protocol and Trust-aware Secure Routing Protocol (TSRP). The evaluation results show that the proposed ESFRM outperforms the conventional schemes with respect to energy consumption, malicious node detection and transmission rate. Full article

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes. Full article

The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance. Full article

In the last few decades, urban areas across the world have experienced rapid growth in transportation technology with a subsequent increase in transport-related challenges. These challenges have increased our need to employ technology for creating more intelligent solutions. One of the essential tools used to address challenges in traffic is providing vehicles with information about traffic conditions in nearby areas. Vehicle ad-hoc networks (VANETs) allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication with the aim of providing safe and efficient transportation. Since drivers might make life-critical decisions based on information provided by other vehicles, dealing with rogue vehicles that send invalid data or breach users’ privacy is an essential security issue in VANETs. This paper proposes a novel privacy-preserving vehicular rogue node detection scheme using fog computing. The proposed scheme improves vehicle privacy, communication between vehicles, and computation efficiency by avoiding the exchange of traffic data between vehicles, allowing communication only through roadside units (RSUs). This scheme also proposes an RSU authentication mechanism, along with a mechanism that would allow RSUs to detect and eliminate vehicles providing false traffic data, which will improve the accuracy and efficiency of VANETs. The proposed scheme is analyzed and evaluated using simulation, which presents significant improvements for data processing, accurately detecting rogue vehicles, minimizing overhead, and immunizing the system against colluding vehicles. Full article