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Emanuele Santonicola, Ennio Andrea Adinolfi, Simone Coppola and Francesco Pascale
Nowadays, a vehicle can contain from 20 to 100 ECUs, which are responsible for ordering, controlling and monitoring all the components of the vehicle itself. Each of these units can also send and receive information to other units on the network or exter...
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Omar Abdulkhaleq Aldabash and Mehmet Fatih Akay
An IDS (Intrusion Detection System) is essential for network security experts, as it allows one to identify and respond to abnormal traffic present in a network. An IDS can be utilized for evaluating the various types of malicious attacks. Hence, detecti...
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Max Schrötter, Andreas Niemann and Bettina Schnor
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusi...
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Nikola Andelic and Sandi Baressi ?egota
This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of this...
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Jiaming Song, Xiaojuan Wang, Mingshu He and Lei Jin
In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in ...
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Hongpo Zhang, Bo Zhang, Lulu Huang, Zhaozhe Zhang and Haizhaoyang Huang
Internet of Things (IoT) devices and services provide convenience but face serious security threats. The network intrusion detection system is vital in ensuring the security of the IoT environment. In the IoT environment, we propose a novel two-stage int...
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Gianmarco Baldini
Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel imple...
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Tianhao Hou, Hongyan Xing, Xinyi Liang, Xin Su and Zenghui Wang
Marine sensors are highly vulnerable to illegal access network attacks. Moreover, the nation?s meteorological and hydrological information is at ever-increasing risk, which calls for a prompt and in depth analysis of the network behavior and traffic to d...
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Minxiao Wang, Ning Yang, Dulaj H. Gunasinghe and Ning Weng
Utilizing machine learning (ML)-based approaches for network intrusion detection systems (NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to various threats. Of particular concern are two significant threats associate...
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Mohammed Zakariah and Abdulaziz S. Almazyad
The prevalence of Internet of Things (IoT) technologies is on the rise, making the identification of anomalies in IoT systems crucial for ensuring their security and reliability. However, many existing approaches rely on static classifiers and immutable ...
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Afnan Alotaibi and Murad A. Rassam
Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks befo...
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Kongpei Wu, Huiqin Qu and Conggui Huang
For the current stage of complex and changing network environments and correlated and synchronized vulnerability attacks, this study first fuses attack graph technology and Bayesian networks and constructs Bayesian attack graphs toportray the correlation...
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Mustafa Al Lail, Alejandro Garcia and Saul Olivo
Modern society has quickly evolved to utilize communication and data-sharing media with the advent of the internet and electronic technologies. However, these technologies have created new opportunities for attackers to gain access to confidential electr...
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Duc-Minh Ngo, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. Murphy and Emanuel Popovici
This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks? security has become a crucial issue. Anomaly...
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Ruohao Zhang, Jean-Philippe Condomines and Emmanuel Lochin
The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight ag...
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Sikha Bagui, Mary Walauskis, Robert DeRush, Huyen Praviset and Shaunda Boucugnani
This paper looks at the impact of changing Spark?s configuration parameters on machine learning algorithms using a large dataset?the UNSW-NB15 dataset. The environmental conditions that will optimize the classification process are studied. To build smart...
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Nejood Faisal Abdulsattar, Firas Abedi, Hayder M. A. Ghanimi, Sachin Kumar, Ali Hashim Abbas, Ali S. Abosinnee, Ahmed Alkhayyat, Mustafa Hamid Hassan and Fatima Hashim Abbas
Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civil a...
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Jovana Mijalkovic and Angelo Spognardi
Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of network...
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Bin Yang, Muhammad Haseeb Arshad and Qing Zhao
Powered by advances in information and internet technologies, network-based applications have developed rapidly, and cybersecurity has grown more critical. Inspired by Reinforcement Learning (RL) success in many domains, this paper proposes an Intrusion ...
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Sergey Golubev, Evgenia Novikova and Elena Fedorchenko
Recently, approaches based on the transformation of tabular data into images have gained a lot of scientific attention. This is explained by the fact that convolutional neural networks (CNNs) have shown good results in computer vision and other image-bas...
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