Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the traditional A* algorithm has some limitations, such as slow planning speed, close to obstacles. In this paper, we propose an improved A*-based algorithm, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning. The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions. Bidirectional search is a strategy searching path from the start node and the goal node simultaneously. Smoothing improves path robustness by reducing the number of right-angle turns. In addition, simulation tests for the EBS-A* algorithm are performed, and the effectiveness of the proposed algorithm is verified by transferring it to a robot operating system (ROS). The experimental results show that compared with the traditional A* algorithm, the proposed algorithm improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89% and the number of right-angle turns by 100%.

Physical unclonable functions (PUFs) are promising solutions for low-cost device authentication; hence, ignoring the security of PUFs is becoming increasingly difficult. Generally, strong PUFs are vulnerable to classical machine learning (ML) attacks; however, classical ML attacks do not perform well on strong PUFs with complex structures. Side-channel analysis (SCA) hybrid attacks provide efficient approaches to modeling XOR APUF. However, owing to the inadequate exploitation of all available data, recent SCA hybrid attacks may fail on novel PUF designs, such as MPUF and iPUF. Thus, herein, we introduce a method that combines challenge-response pairs with side-channel information to construct challenge-synthetic-feature pairs (CSPs) via feature cross, thereby making it possible to model strong PUFs through multiclass classification. We propose multiclass classification-based SCA hybrid attacks to model strong PUFs with complex structures. When provided with CSPs, the proposed hybrid attacks use a feed-forward neural network with a softmax activation function to build combined models of PUFs. The combined models predict class labels for given challenges and then reveal responses through simple mappings from these labels. Experimental results show that the proposed attacks could model 16-XOR APUF, (128,5)-MPUF, (8,8)-iPUF, and (2,16)-iPUF with accuracies exceeding 94%. Compared with state-of-the-art modeling techniques, the proposed attack has advantages in terms of modeling accuracy, time cost, and the size of required training data.

With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.