Narrow Results

Under the Vehicle-to-Vehicle (V2V) environment, connected vehicles (CVs) can share the traveling information with each other to keep the traffic flow stable. However, the open network cooperation environment makes CVs vulnerable to cyberattacks, which leads to changes in driving behavior. The existing theories divide cyberattacks into three types: bogus information, replay/delay and collusion cyberattacks. In addition, the mixed flow consisting of truck and car is a common form of road traffic. In order to clarify the potential impact of cyberattacks on mixed traffic flow, this paper proposes an extended car-following model considering cyberattacks under CVs environment. Subsequently, the stability of the model is analyzed theoretically, and the stability condition of the model is obtained. The numerical simulation is carried out and the result shows that the cyberattacks lead to different degrees of traffic behavior hazards such as queue time extension, congestion and even rear end collision. Among them, cooperative attack is the most serious.

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

The following topics are under this section:

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• Asia-Pacific — Potential cancer treatment strategies for humans from mechanisms behind low cancer occurrence in bats
• Asia-Pacific — Singapore scientists uncover mechanism behind development of viral infections
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• Asia-Pacific — National platforms created to accelerate Singapore's drug development efforts
• Rest of the World — Cybersecurity company threat report details cyberattacks targeting the healthcare industry
• Rest of the World — Artificial intelligence – designed influenza vaccine begins clinical trials in the US
• Rest of the World — Clinical – stage biotechnology company presents Murlentamab phase II study results in colorectal cancer

The following topics are under this section:

• Collaborative launch of translational R&D hub to enhance clinical diagnostic development efforts
• Development of artificial intelligence (AI) tools and analytics platforms for improving cell therapy and immuno-oncology
• Entering the China market to provide regional support
• New funding help to support expansion of eye treatment facilities across Vietnam
• Alarm raised over increased targeted cyberattacks against pharmaceutical industry
• Joint fundraising acceleration programme to support development of tech companies and expand investor community
• Asia’s first FemTech innovations group to offer healthcare solutions unique to Asian women
• Population-Based Public Health initiative in Singapore leverages on digital health platforms
• Malaysian subsidiary of major biopharmaceutical company receives GMP Certification for compliance
• E-paper technology to support unmanned retail outlet in Hong Kong Science Park
• Novena Global Lifecare acquires three Taiwanese healthcare players