Over the past few decades, as vehicles have become increasingly intelligent, the applications of in-vehicle electronic systems have expanded significantly. However, with the growing complexity of vehicle networks, there is an ever-increasing concern for their network security. In particular, the Controller Area Network (CAN) bus has become a critical medium for communication between various Electronic Control Units (ECUs) within a vehicle. Since the design of the CAN bus lacks sufficient security measures, it is vulnerable to various network intrusions. To address this security challenge, researchers have been searching for ways to enhance the network security of the CAN bus to ensure that vehicle systems are not compromised by unauthorized access or network attacks. This paper introduces a robust intrusion detection system (IDS) for the CAN bus in vehicles, employing a novel Multi-Scale Feature Fusion technique. Leveraging the distinct capabilities of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Transformer neural network architectures, the proposed methodology adeptly captures and prioritizes both shallow and deep features of CAN bus data. Besides, it enhances detection accuracy and robustness against various cyber-attacks. Evaluation on the Carhacking dataset demonstrates superior performance, achieving a precision, recall, and F1-score of 100%. Verified by an ablation study, this approach promises a substantial advancement in safeguarding in-vehicle networks.

Optical Coherence Tomography (OCT) is an advanced biomedical imaging technology that provides noninvasive, real-time, high-resolution images of tissues with high scattering properties. Widely employed in ophthalmology, OCT conducts diagnostic imaging of the anterior eye and retina, particularly in clinical studies evaluating its efficacy for various retinal diseases. Despite its significance, the multiple images generated by OCT pose a time-consuming challenge for ophthalmologists during the analysis process. In this paper, the pre-trained visual transformer is extended and trained with adversarial noise images. The proposed approach demonstrates the ability to effectively classify a broad spectrum of adversarial attacks while operating without prior knowledge of the attackers and without compromising classification performance. The results achieved on retinal OCT images, susceptible to adversarial attacks, exhibit a high accuracy of 98.1%.

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

With the rapid advancement of vehicle connectivity and intelligent technologies, an increasing number of vehicles are now connected to the Internet. However, these connected vehicles are vulnerable to malicious attacks, posing serious security events. In particular, the in-vehicle controller area network (CAN) bus has witnessed a rise in incidents involving various network attacks, such as denial of service (DoS), fuzzy attacks, and gear attacks. In response, this paper proposes an enhanced cuckoo filter-based intrusion detection system (ECF-IDS) for in-vehicle network. The ECF-IDS builds on an enhanced version of the cuckoo filter. It first utilizes the cuckoo filter to establish two lists (a normal list and an intrusion list) based on the labeled dataset using Car Hacking Dataset (CHD) and can-train-and-test dataset. Then, the input CAN traffic is sequentially compared with these two lists, where the conflicting traffic is further identified using a BERT-based model. The ECF-IDS is experimentally validated using the CHD and can-train-and-test dataset, demonstrating higher detection efficiency, lower resource consumption, and detection success exceeding 99% compared to other algorithms presented in previous studies. Furthermore, we conducted real in-vehicle environment testing on the ECF-IDS model, and its detection performance proved to be excellent.

As a convenient means of transportation, unmanned aerial vehicles (UAVs) can provide consumers and merchants with safe, efficient, and contactless instant delivery services. However, online orders are often concurrent, dynamic, and with time constraints in an instant delivery system. Therefore, it's important to optimally schedule the delivery sequence upon the UAVsystem. In this paper, we design a real-time UAV delivery scheduling model considering dynamic online orders. Through our instant delivery management system, the urban delivery network can be updated in real time according to order information. Considering the practical situation, customers with spatial diversity may produce orders with common order requirements, so we consider the overlap of UAV routes in the scheduling process. An order merging algorithm (OMA) is then proposed to improve the delivery efficiency (the ratio of the number of completed orders to the times UAVs visit stores) of UAVs. With the help of proposed system-level decision-making method, our system can meet real-time concurrent order demands across urban delivery networks. To evaluate the performance of system, we further carry out simulation experiments to verify the effectiveness of our scheme. Results show that the proposed UAVscheduling scheme improves the efficiency of UAV instant delivery.

Pattern matching in Intrusion Detection Systems (IDS) is one of the most critical and time-consuming elements, allowing the system to make decisions based on the real-time threats across the network. A pattern-matching method can be software-based or hardware-based. In this paper, a hardware implementation of bit-split algorithm has been discussed for pattern matching in order to detect unwanted traffic for a maximum number of rulesets. A hardware-based string matching scheme has been preferred here due to its fast speed and quick data parallelism for the high-performance Intrusion Detection System (IDS). The prototype of the detection method is implemented on Spartan SP605 with a small number of rules. For a large number of rules, a multi-scale Field Programmable Gate Array (FPGA)-based hardware architecture has been implemented in which we have used NetFPGA-SUME. This FPGA board has examined incoming packets at a bit rate of 1.25 Gb/sec with an operational frequency of 156.25MHZ. Furthermore, high-level data parallelism has been implemented by instantiating more than one match engine for handling multiple packets to achieve high throughput (Tp).

Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regional languages. Thus the Urdu FN purveyors cannot be discerned using fact-checking portals. State-of-the-art (SOTA) approaches for Fake News Detection (FND) count upon appropriately labelled and large datasets. FND in regional and resource-constrained languages lags due to the lack of limited-sized datasets and legitimate lexical resources. The previous datasets for Urdu FND are limited-sized, domain-restricted, publicly unavailable and not manually verified where the news is translated from English into Urdu. In this paper, we curate and contribute the first largest publicly available dataset for Urdu FND, "Ax-to-Grind Urdu", to bridge the identified gaps and limitations of existing Urdu datasets in the literature. It constitutes 10,083 fake and real news on fifteen domains collected from leading and authentic Urdu newspapers and news channel websites in Pakistan and India. FN for the Ax-to-Grind dataset is collected from websites and crowdsourcing. The dataset contains news items in Urdu from the year 2017 to the year 2023. Expert journalists annotated the dataset. We benchmark the dataset with an ensemble model of mBERT, XLNet, and XLM-RoBERTa. The selected models are originally trained on multilingual large corpora. The results of the proposed model are based on performance metrics, F1-score, accuracy, precision, recall and MCC value. F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902 demonstrate the effectiveness of the proposed approach for Urdu FND. Comparison analysis with SOTA ML and DL models and existing Urdu benchmark datasets exhibit that the ensemble model outperforms them for Urdu FND. The dataset used for our experiments is publicly available at https://github.com/HjH-Whu-CRC/Ax-to-Grind-Urdu for further analysis and validation.