ALBERT

Description: Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme.

Description: The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

Description: The revolution of computer network technologies and telecommunication technologies increases the number of Internet users enormously around the world. Thus, many companies nowadays produce various devices having network chips, each device becomes part of the Internet of Things and can run on the Internet to achieve various services for its users. This led to the increase in security threats and attacks on these devices. Due to the increased number of devices connected to the Internet, the attackers have more opportunities to perform their attacks in such an environment. Therefore, security has become a big challenge more than before. In addition, confidentiality, integrity, and availability are required components to assure the security of Internet of Things. In this article, an adaptive intrusion detection and prevention system is proposed for Internet of Things (IDPIoT) to enhance security along with the growth of the devices connected to the Internet. The proposed IDPIoT enhances the security including host-based and network-based functionality by examining the existing intrusion detection systems. Once the proposed IDPIoT receives the packet, it examines the behavior, the packet is suspected, and it blocks or drops the packet. The main goal is accomplished by implementing one essential part of security, which is intrusion detection and prevention system.