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Dominic Lightbody, Duc-Minh Ngo, Andriy Temko, Colin C. Murphy and Emanuel Popovici
The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of...
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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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Sikha Bagui, Dustin Mink, Subhash Bagui, Sakthivel Subramaniam and Daniel Wallace
This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling before...
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Mantas Bacevicius and Agne Paulauskaite-Taraseviciene
Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such as ...
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Sikha S. Bagui, Dustin Mink, Subhash C. Bagui and Sakthivel Subramaniam
Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machi...
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Sapna Sadhwani, Baranidharan Manibalan, Raja Muthalagu and Pranav Pawar
The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vu...
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Tala Talaei Khoei and Naima Kaabouch
Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these a...
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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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Weijie Zhang, Lanping Zhang, Xixi Zhang, Yu Wang, Pengfei Liu and Guan Gui
Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training ...
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Jadil Alsamiri and Khalid Alsubhi
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort....
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