Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms.

Internet of Vehicles (IoV) is a multi-node network which switches data in an open and wireless environment. Numerous interaction activities occur among IoV entities to share significant information, which is essential for network operation. As part of intellectual transportation, IoV is a hot topic for researchers because it faces numerous unresolved challenges, particularly regarding privacy and security.

Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications.

Denial of Wallet (DoW) attacks are one kind of cyberattack whose goal is to develop and expand the financial sources of a group by causing extreme costs in their serverless computing or cloud environments. These threats are chiefly related to serverless structures owing to their features, such as auto-scaling, pay-as-you-go method, cost amplification, and limited control. Serverless computing, Function-as-a-Service (FaaS), is a cloud computing (CC) system that permits developers to construct and run applications without a conventional server substructure.

An intelligent ransomware based cyberthreat detection model using multi head attention-based recurrent neural networks with optimization algorithm in IoT environment.

The rapid growth of the Internet of Things (IoT) and its extensive use in many regions, such as smart homes, healthcare, and vehicles, have made IoT security increasingly critical. Ransomware is an advanced and adjustable threat influencing users globally, limiting admittance to their data or systems over models like file encryption or screen locking. Traditional ransomware detection methods frequently drop, deprived of the ability to combat these threats successfully.

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition.

Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator.

Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments.

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks.

Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks.

An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT.

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices.

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically.