Intelligent Cyber-Physical Transportation Systems (ICTS) rely deeply on data exchange between moving vehicles and fixed infrastructure. While exchanging data between vehicles in the network, security is a main constraint. ICTS encompasses many facilities and claims, such as managing road traffic, traveler data, public transportation systems, and autonomous vehicles. It is anticipated that ICTS will make significant contributions to future smart cities and urban design in road and traffic security, transportation and transport optimization, energy efficiency, and environmental pollution control. Flexibility, variety of quality-of-service needs, and massive volumes of data generated all present challenges when working with ITS. Previous works on cyber-physical attack detection lag in ensuring security at various levels. To monitor In-Vehicle Networks (IVN), Vehicle-to-Vehicle (V2V) Communications, and Vehicle-to-Infrastructure (V2I) Networks for malicious behavior, we introduce a deep learning-based Intrusion Detection System (IDS) for ICTS. In this research, we look into how Deep Learning (DL), a relatively new but rapidly growing subject, could help improve ICTS. This research investigates whether deep learning can enhance the security of autonomous vehicles and other forms of smart transportation. We improve the smart transport network using the deep learning method, simulate it, and statistically study its data transmission economy, accuracy projections of the future, and path-switching strategy. To identify malicious activity in the core networks of autonomous vehicles (AV), we have developed an ensemble Long-Short Term Memory (LSTM) technique based on Deep Learning architecture. The car hacking dataset (used to monitor communications within a vehicle) and the UNSWNB15 dataset (used to monitor communications with the outside world) are used to test and compare the performance of the proposed IDS. The experimental findings showed that our suggested system outperformed other intrusion detection methods by achieving 100% accuracy in detecting all sorts of assaults on the auto-hacking dataset and 99% on the UNSW-NB15 dataset.

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms.

Cybersecurity in the Internet of Things (IoT) is the practice of implementing measures to secure networks and connected devices from data breaches, cyber threats, and unauthorized access. It is essential owing to the increasing interconnectivity of devices, ranging from smart home appliances to industrial sensors. The potential attack surface expands, necessitating strong cybersecurity measures to protect sensitive data, ensure privacy, and prevent disruptions to critical services with these increasing number of IoT devices. Artificial intelligence (AI) technologies, particularly deep learning (DL) and machine learning (ML) approaches, hold the potential to mitigate and identify cyberattacks on IoT networks. DL demonstrates promise for effectively preventing and detecting security threats within IoT devices. Despite the importance of Intrusion Detection Systems (IDS) in maintaining confidentiality by detecting suspicious activities, classical IDS solutions might face difficulties in the IoT platform. Therefore, this study presents an Artificial Orca Algorithm with Ensemble Learning cyberattack detection and classification (AOAEL-CDC) methodology in an environment of IoT. The presented AOAEL-CDC technique exploited the feature selection (FS) approach with an ensemble learning approach for cyberattack recognition and identification in the IoT atmosphere. In the developed AOAEL-CDC model, the feature selection takes place using the AOA technique. For the cyberattack detection process, the ensemble learning process is carried out by the use of three models such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and extreme learning machine (ELM). Finally, the hyperparameter range of the DL techniques takes place using the marine predator’s algorithm (MPA). To examine the performance analysis of the AOAEL-CDC methodology, a series of simulations take place using a benchmark dataset. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.31%.

Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.

Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based on predefined signatures of known attacks. AI improves signature-based detection by automating the signature generation and reducing false positives through pattern recognition. It can automate threat detection and response, allowing for faster reaction times and reducing the burden on human analysts. With this motivation, this study introduces an Improved Sine Cosine Algorithm with a Deep Learning-Enabled Security Solution (ISCA-DLESS) technique. The presented ISCA-DLESS technique relies on metaheuristic-based feature selection (FS) and a hyperparameter tuning process. In the presented ISCA-DLESS technique, the FS technique using ISCA is applied. For the detection of anomalous activities or intrusions, the multiplicative long short-term memory (MLSTM) approach is used. For improving the anomaly detection rate of the MLSTM approach, the fruitfly optimization (FFO) algorithm can be utilized for the hyperparameter tuning process. The simulation value of the ISCA-DLESS approach was tested on a benchmark NSL-KDD database. The extensive comparative outcomes demonstrate the enhanced solution of the ISCA-DLESS system with other recent systems with a maximum accuracy of 99.69%.