Deep neural network (DNN)-based vehicle detection systems deployed on unmanned aerial vehicles (UAVs) are susceptible to adversarial attacks, resulting in significant implications for public safety and system reliability. Despite advancements in DNN-based detection, the adversarial robustness of these systems in aerial video contexts remains underexplored. Existing attack models fail to exploit the sequential and periodic nature of video frames in aerial vehicle detection systems. To address this, we propose a Periodic Adversarial Attack for Aerial Video (P3AV), which is the first to take advantage of the periodic nature of tasks related to road traffic parameters and improve the success of attacks. P3AV systematically selects critical video frames to be attacked by employing Bayesian optimization combined with domain-specific knowledge. The sensitive pixels in the frames are then chosen based on the gradient magnitudes of the loss function. Finally, an improved version of the projected gradient descent algorithm is developed by using gradient norms to generate perturbations and enhance the manipulation of selected pixels. Our experiments using four adversarial attacks against 10 DNN architectures, which are developed based on Convolutional Neural Network (CNN) and YOLO, on two datasets demonstrate that P3AV can improve the false rate in detection systems by 6% and the attack success rate by 5% over other attack models. Meanwhile, CNN models perform the worst against adversarial attacks. These findings highlight the critical need for improved adversarial defenses in UAV-based detection systems and underscore the broader implications for secure and reliable ITS.

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in various fields. A major concern with UAV operation is their limited power capacity which impacts mission planning, operational efficiency, and battery management, presenting significant research and engineering challenges. This paper evaluates the applications of multiple AI algorithms in predicting the energy consumption of low-cost quadcopter drones. One of the primary contributions involves developing four prediction models, including random forest, regression tree, support vector machine, artificial neural network, and adaptive Neuro-Fuzzy Inference System (ANFIS) on an open-source dataset of small quadcopter flights. This paper also performs a comparative study on the performance of the aforementioned algorithms in predicting the energy consumption of a UAV. This research enhances the field not only by leveraging established machine learning techniques but also by adopting and examining ANFIS, which has received limited prior research attention. By introducing and applying ANFIS, this study not only expands the existing knowledge but also offers a unique perspective, potentially paving the way for further research, especially in addressing uncertainty like weather conditions. According to our study, the power consumption of the UAV is notably influenced by the aircraft’s altitude, wind speed, and velocity. The Random Forest model demonstrates superior accuracy in forecasting UAV power consumption compared to other models. We also provide an overview of the ongoing challenges and potential future endeavors.

The increased reliance on Unmanned Aerial Vehicles (UAVs) in various industries exalts the security requirements since it is critical to protect these systems from any cyber-attack. GPS spoofing presents an important challenge by deceiving UAVs through false GPS signals that would disrupt their operations, thereby endangering them. As a countermeasure, this study introduces a method of detecting GPS spoofing attacks that are aimed at UAV systems. This involves developing a robust methodology to detect the GPS spoofing attack based on the UAV’s current reported location and Received Signal Strength (RSS) data at several base stations. In this study, we developed a smart verification algorithm using the K-Nearest Neighbors (KNN) algorithm to authenticate the reported locations of UAVs, based on RSS from various base stations antenna. We evaluated the performance of the algorithm using metrics such as accuracy, precision, and F1-score. The results indicate that the algorithm’s effectiveness improves with an increase in the number of base stations used. Additionally, the paper will pinpoint the possible direction for UAV security and the adaptive countermeasures to improve the level of resilience against spoofing tactics, which are rapidly evolving.

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive learning and multitask learning into a hybrid ML model to address these two challenges. The hybrid model consists of an autoencoder for feature extraction and a support vector machine (SVM) for detecting intrusions. In the cost-sensitive learning phase for the class imbalance problem, the hinge loss layer is enhanced to make a classifier strong against low-distributed intrusions. Moreover, to detect unknown attacks, we formulate the SVM as a multitask problem. Experiments on the UNSW-NB15 and BoT-IoT datasets demonstrate the superiority of our model in terms of recall, precision, and F1-score averagely 92.2%, 96.2%, and 94.3%, respectively, over other approaches.

Unmanned Aerial Vehicles (UAVs) have immense potential to enhance Intelligent Transport Systems (ITS) by aiding in real-time traffic monitoring, emergency response, and infrastructure inspection, leading to rich data collection, lower response times, and efficient urban mobility management. Machine learning (ML) is a crucial component in UAV-assisted ITS as it processes UAV-captured data in both the perception layer and decision layers of intelligent components for vehicle/pedestrian detection, trajectory optimization, and resource allocation. Importantly, the integration of UAVs and cutting-edge deep learning (DL) techniques is fostering an exciting synergy, equipping UAVs with unparalleled intelligence and autonomy, particularly, for the perception layer of UAVs. Despite these enhancements, their usefulness for detection and traffic extraction tasks remains largely unexplored. The contributions of this paper are divided into two main aspects: (1) UAVs in different ITS application scenarios that are empowered by ML technologies are reviewed. (2) A thorough survey aiming to explore a quantitative understanding of widely used DL models via a series of experiments and comparisons is presented. Four DL models, namely Convolution Neural Network (CNN), regions with CNN (R-CNN), Faster R-CNN, and You Only Look Once (YOLO)), in combination with different backbones, are designed and employed on five aerial datasets. Finally, we present a discussion of the remaining challenges and future works.

Federated Learning is particularly challenging in IoT environments, where edge and cloud nodes have imbalanced computation capacity and networking bandwidth. The main scalability barrier in distributed stochastic gradient descent-based machine learning frameworks is the communication overhead from frequent model parameter exchanges between workers and the central server. One way to reduce this overhead is by employing constant and periodic averaging, which sends model parameters to the server after a few iterations of local updates from workers. However, investigations have shown that the optimal communication period for balancing communication and convergence is not constant. Although some studies have explored the effectiveness of federated learning with a constant period, dynamically adjusting the period for optimal convergence remains under-explored. To address this, we investigate the impact of the period on global model convergence and propose an adaptive period control mechanism (AdaPC). This mechanism adaptively adjusts the aggregation period of the federated learning framework to achieve fast convergence with minimal communication. Our theoretical and empirical findings demonstrate that our proposed solution achieves faster convergence, lower final training loss, and minimized communication overhead compared to the constant period averaging strategy and other existing solutions.

Nowadays, vehicle-oriented Internet of Things (IoT) is a new generation of IoT networks in which sensors are deployed on electronic hardware modules of vehicles. A secure and feasible IoT-assisted vehicle environment should include a robust data transmission mechanism for transferring and collecting data packets from both onboard and roadside sensors, resulting in the accurate delivery of packages without delay. When designing such Internet of Vehicles (IoV) networks, the vulnerability of the network should be considered to facilitate data transmission in the remaining network under the condition that some nodes (e.g., vehicles) and channels are damaged due to the dynamic environmental factors and unpredicted failures at various nodes. Fractional Critical Deleted Graph (FCDG), which is used in graph theory, can act as Fractional Factor (FF) in the IoV networks to maintain the IoT network stable and provide reliable network connectivity when a part of data transmission network is damaged. Toughness is an important condition to measure the sturdiness of such FF-encoded network. In this work, we study the relationship between toughness and FCDG in IoV networks. Moreover, the graph conditions are considered together with the tight lower bound of the toughness for the existence of path factor. Such feasibility analysis of IoV networks help to find the bound in the effort to recover or realign lost links in networks, which is critical for the next generation of intelligent transportation systems where all vehicles are connected seamlessly.

Cyber attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems (IDSs). To address this issue, we introduce EvolCostDeep, a hybrid model of stacked autoencoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning (DL) on Big data hinders the scalability of IIoT IDSs. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-level mechanisms. After aggregating detection outputs from worker nodes to the master, the result is passed to the cloud platform for mitigating attacks. A series of experiments is conducted on the ToN-IoT and UNSW-NB15 data sets to evaluate the performance of EvolCostDeep and DeepIDSFog. The results show that our frameworks can effectively handle both class imbalance problem and scalability of big IIoT traffic data compared with the other models. The averaged values of the EvolCostDeep for recall, precision, and$F1$-score on the data sets are of 93.3%, 97.6%, and 95.2%, respectively, which are higher than the compared methods. Also, the DeepIDSFog provides an average speedup of$38.7\times $over other comparing models.

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each type of misclassification because the cost matrix is specifically generated for each partition. To determine its utility, we implement the proposed cost-sensitive learning method in two deep learning classifiers, namely: stacked autoencoder and convolution neural networks. Our experiments on the ISCX VPN-nonVPN dataset show that the proposed approach can obtain higher classification performance on low-frequency classes, in comparison to three other NTC methods.