The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) t...
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Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical...
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The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) a...
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Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computi...
Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computing is poised to revolutionize our daily lives by enabling seamless real-time information sharing among interconnected objects. However, ensuring the safety and security of such network is crucial in preventing potential threats and attacks. The purpose of this study is to develop a sophisticated intrusion detection system that is effective, efficient, and intelligent using complex machine learning models trained on relevant intrusion detection datasets. In this article, the multiclass classification approaches for intrusion detection in IoT-driven aerial computing environment are presented (in short, MCA-IDAC). In the comparative study, it has been observed that proposed MCA-IDAC performs significantly better than the other existing competing schemes, in terms of important performance parameters.
Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secu...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.
In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carryi...
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ISBN:
(数字)9798350361261
ISBN:
(纸本)9798350361278
In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.
Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked...
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ISBN:
(数字)9798350361261
ISBN:
(纸本)9798350361278
Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.
Distributed ledger technology (DLT) provides decentralised services by removing the need of trust among distributed nodes in the distributed system. Transactions across the network are visible to all participants. How...
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In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more...
In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.
Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next...
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