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Backdoor attacks embed hidden functionalities in deep neural networks (DNN), triggering malicious behavior with specific inputs. Advanced defenses monitor anomalous DNN inferences to detect such attacks. However, concealed backdoors evade…

Ad-hoc routing protocols use a number of algorithms for route discovery. Some use flooding in which a route request packet (RREQ) is broadcasted from a source node to other nodes in the network. This often leads to unnecessary…

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as…

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our…

Due to mobility in Ad-Hoc network the topology of the network may change randomly, rapidly and unexpectedly, because of these aspects, the routes in the network often disappear and new to arise. To avoid frequent route discovery and route…

Vehicular ad hoc networks is a modern technology that holds an important aspect in the transportation domain due to its abilities to increase traffic efficiency and safety. It is a another variant of Mobile ad-hoc networks that provides…

Automatic IP address assignment in Mobile Ad hoc Networks (MANETs) enables nodes to obtain routable addresses without any infrastructure. Different protocols have been developed throughout the last years to achieve this service. However,…

Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt gradient-based iterative attack methods, which usually limit…

In Mobile Ad-Hoc networks, broadcasting is a fundamental operation in the network layer. A node transmits a rebroadcast message to any or all other nodes whenever it receives for the first time. It will generate several redundant…

Deep neural networks (DNNs) have long been recognized as vulnerable to backdoor attacks. By providing poisoned training data in the fine-tuning process, the attacker can implant a backdoor into the victim model. This enables input samples…

Mobile Ad Hoc Network (MANET) is a collection of two or more devices or nodes or terminals with wireless communications and networking capability that communicate with each other without the aid of any centralized administrator also the…

This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry…

Connected autonomous vehicles (CAVs) rely on vision-based deep neural networks (DNNs) and low-latency (Vehicle-to-Everything) V2X communication to navigate safely and efficiently. Despite their advances, these systems remain vulnerable to…

We propose AWN (Algebra for Wireless Networks), a process algebra tailored to the modelling of Mobile Ad hoc Network (MANET) and Wireless Mesh Network (WMN) protocols. It combines novel treatments of local broadcast, conditional unicast and…

Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention…

Routing in mobile ad hoc networks (MANETs) presents a big challenge, especially when support for a large number of nodes is needed. This paper extends the local visibility concept of the recent DHT-based URBAN_XOR routing protocol, which…

Deep neural network (DNN) as a popular machine learning model is found to be vulnerable to adversarial attack. This attack constructs adversarial examples by adding small perturbations to the raw input, while appearing unmodified to human…

Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in $input$-$adaptive$ multi-exit architectures, such as…

This paper revisits the issue of route discovery in dynamic source routing (DSR) for mobile ad hoc networks (MANETs), and puts forward a proposal of a lightweight non-optimal route suppression technique based on the observation of a rarely…

The progress and integration of intelligent transport systems (ITS) have therefore been central to creating safer and more efficient transport networks. The Internet of Vehicles (IoV) has the potential to improve road safety and provide…