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In vehicular ad hoc networks, peers make decisions on the basis of the information provided by the other peers. If the received information is fake, then the result could be catastrophic, such as road accidents. Therefore, many researchers use the concept of trust to evaluate the trustworthiness of the received data. However, existing trust management schemes (proposed so far for the vehicular networks) are suffered from various limitations. For example, some schemes measure trust on the basis of the history of interactions, which is infeasible for vehicular networks due to its ephemeral nature. Also, none of the existing schemes operate in an identity anonymous environment. In order to overcome these limitations, we propose a novel trust management scheme for identity anonymous vehicular ad hoc networks. The proposed method is simple and completely decentralized that makes it easy to implement in the vehicular networks. Also, we prove that the proposed method is not only robust, but it also detects false location and time information. Furthermore, it introduces linear time complexity, which makes it suitable to use in real time. Copyright © 2013 John Wiley & Sons, Ltd.

Sensor network security solutions that have been proposed so far are mostly built on the assumption of a trusted environment, which is not very realistic so we need trust management before deploying any other security solution. Traditional trust management schemes that have been developed for wired and wireless ad-hoc networks are not suitable for wireless sensor networks because of higher consumption of resources such as memory and power. In this paper, we propose a novel lightweight group based trust management scheme (GTMS) for distributed wireless sensor networks in which the whole group will get a single trust value. Instead of using completely centralized or distributed trust management schemes, GTMS uses hybrid trust management approach that helps in keeping minimum resource utilization at the sensor nodes. 1

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Full network level privacy has often been categorized into four sub-categories: Identity, Route, Location and Data privacy. Achieving full network level privacy is a critical and challenging problem due to the constraints imposed by the sensor nodes (e.g., energy, memory and computation power), sensor networks (e.g., mobility and topology) and QoS issues (e.g., packet reach-ability and timeliness). In this paper, we proposed two new identity, route and location privacy algorithms and data privacy mechanism that addresses this problem. The proposed solutions provide additional trustworthiness and reliability at modest cost of memory and energy. Also, we proved that our proposed solutions provide protection against various privacy disclosure attacks, such as eavesdropping and hop-by-hop trace back attacks.

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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.