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Most existing trust-based security schemes for mobile ad-hoc networks (MANETs) consider packet loss an indicator of possible attacks by malicious nodes. There may be several reasons for packet losses, such as interference, queue overflow, and node mobility. Identifying the real underlying cause of a packet loss event is important for any security solution. To detect truly malicious nodes, it is necessary to carry out a fine-grained analysis (FGA) to determine the underlying cause of such loss. Without such analysis, the performance of any security solution may degrade, due to the punishment of innocent nodes while actual malicious nodes may remain undetected. Therefore, approaches are required that can correctly identify the reason for packet losses and can react accordingly. In this paper, we present a scheme that is able to correctly identify malicious nodes, using network parameters to determine whether packet losses are due to queue overflows or node mobility in MANETs. The contributions of this paper include the FGA scheme for packet loss and the development of a comprehensive trust model for malicious node identification and isolation. Our proposed FGA scheme is evaluated in terms of effectiveness and performance metrics under different network parameters and configurations. The experimental results show that our proposed trust model achieves a significant reduction in false positives rate and an increase in the rate of detection of truly malicious nodes compared with traditional non-FGA schemes.

Smart cities aim to improve the quality of urban services and their energy efficiency by utilizing information and communication technologies. In such context, drones can be utilized to support various services, such as traffic monitoring, search/rescue, and surveillance, by communicating with many different smart objects like sensors. Securing such communications is crucial to making correct decisions and requires efficient cryptographic protocols. However, the design of such protocols must consider: 1) the mobility and the limited battery of drones and 2) the constrained resources of smart objects. In this paper, a suite of cryptographic protocols is presented to deal with three different communication scenarios: one-to-one, one-to-many, and many-to-one. For one-to-one, we propose an efficient Certificateless Signcryption Tag Key Encapsulation Mechanism (eCLSC-TKEM) that supports authenticated key agreement, non-repudiation, and user revocation. eCLSC-TKEM reduces the time required to establish a shared key between a drone and a smart object by minimizing the computational overhead at the smart object. For one-to-many, we propose a Certificateless Multi-Recipient Encryption Scheme (CL-MRES) by which a drone can efficiently send privacy-sensitive data to multiple smart objects. For many-to-one, we present a Certificateless Data Aggregation (CLDA) protocol, which allows drones to efficiently collect data from hundreds of smart objects. Also, for efficiency, we propose a dual channel strategy that allows many smart objects to concurrently execute our protocols. We evaluate eCLSC-TKEM via a smart parking management test-bed. Also, we have implemented CL-MRES and CLDA on a board with a graphics processing unit (GPU) and show their GPU-accelerated performance.

Over the last 25 years, there has been much work on multimedia digital watermarking. In this domain, the primary limitation to watermark strength has been in its visibility. For multimedia watermarks, invisibility is defined in human terms (that is, in terms of human sensory limitations). In this paper, we review recent developments in the non-media applications of data watermarking, which have emerged over the last decade as an exciting new sub-domain. Since by definition, the intended receiver should be able to detect the watermark, we have to redefine invisibility in an acceptable way that is often application-specific and thus cannot be easily generalized. In particular, this is true when the data is not intended to be directly consumed by humans. For example, a loose definition of robustness might be in terms of the resilience of a watermark against normal host data operations, and of invisibility as resilience of the data interpretation against change introduced by the watermark. In this paper, we classify the data in terms of data mining rules on complex types of data such as time-series, symbolic sequences, data streams, and so forth. We emphasize the challenges involved in non-media watermarking in terms of common watermarking properties, including invisibility, capacity, robustness, and security. With the aid of a few examples of watermarking applications, we demonstrate these distinctions and we look at the latest research in this regard to make our argument clear and more meaningful. As the last aim, we look at the new challenges of digital watermarking that have arisen with the evolution of big data.