Searched for: subject%3A%22intrusion%255C+detection%22
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)
The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion...
journal article 2024
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Pellegrino, G. (author)
doctoral thesis 2023
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Sennema, Erik (author)
Intrusion detection systems (IDSs) are essential for protecting computer systems and networks from malicious attacks. However, IDSs face challenges in dealing with dynamic and imbalanced data, as well as limited label availability. In this thesis, we propose a novel elastic gradient boosting decision tree algorithm, namely Elastic CatBoost...
master thesis 2023
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Kabbara, N. (author), Mwangi, A.W. (author), Gibescu, Madeleine (author), Abedi, A. (author), Stefanov, Alexandru (author), Palensky, P. (author)
As power system's operational technology converges with innovative information and communication technologies, the need for extensive resilience testing for scenarios covering the electrical grid, networking bottlenecks, as well as cyber security threats, become a necessity. This paper proposes a comprehensive, multi-disciplinary simulation...
conference paper 2023
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author), Roy, Satyabrata (author)
Internet of Things (IoT) applications are growing in popularity for being widely used in many real-world services. In an IoT ecosystem, many devices are connected with each other via internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major concern in its deployment is network security and user privacy. To...
journal article 2023
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Telikani, Akbar (author), Rudbardeh, Nima Esmi (author), Soleymanpour, Shiva (author), Shahbahrami, Asadollah (author), Shen, Jun (author), Gaydadjiev, G. (author), Hassanpour, Reza (author)
A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive...
journal article 2023
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Sharma, Bhawana (author), Sharma, Lokesh (author), Lal, C. (author)
IoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this...
conference paper 2023
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Aliyu, Ibrahim (author), van Engelenburg, S.H. (author), Mu'azu, Muhammed Bashir (author), Kim, Jinsul (author), Lim, Chang Gyoon (author)
The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be...
journal article 2022
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Apruzzese, Giovanni (author), Pajola, Luca (author), Conti, M. (author)
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labeled. Such labels demand costly expert knowledge, resulting in a lack of real deployments, as well as on papers always relying...
journal article 2022
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Vermeer, M. (author), van Eeten, M.J.G. (author), Hernandez Ganan, C. (author)
Notwithstanding the predicted demise of signature-based network monitoring, it is still part of the bedrock of security operations. Rulesets are fundamental to the efficacy of Network Intrusion Detection Systems (NIDS). Yet, they have rarely been studied in production environments. We partner with a Managed Security Service Provider (MSSP) to...
conference paper 2022
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Herrera-Semenets, Vitali (author), Hernández-León, Raudel (author), van den Berg, Jan (author)
We live in a world that is being driven by data. This leads to challenges of extracting and analyzing knowledge from large volumes of data. An example of such a challenge is intrusion detection. Intrusion detection data sets are characterized by huge volumes, which affects the learning of the classifier. So there is a need to reduce the size...
journal article 2022
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Brussen, Arjen (author)
Year after year, the amount of network intrusions and costs associated to them rises. Research in this area is, therefore, of high importance and provides valuable insight in how to prevent or counteract intrusions. Machine learning algorithms seem to be a promising answer for automated network intrusion detection, as their results often reach...
master thesis 2021
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Simidžioski, Maria (author)
Adversarial attacks pose a risk to machine learning (ML)-based network intrusion detection systems (NIDS). In this manner, it is of great significance to explore to what degree these methods can be viably utilized by potential adversaries. The majority of adversarial techniques are designed for unconstrained domains such as the image recognition...
master thesis 2021
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Herrera-Semenets, Vitali (author), Bustio-Martínez, Lázaro (author), Hernández-León, Raudel (author), van den Berg, Jan (author)
Every day the number of devices interacting through telecommunications networks grows resulting into an increase in the volume of data and information generated. At the same time, a growing number of information security incidents is being observed including the occurrence of unauthorized accesses, also named intrusions. As a consequence of...
journal article 2021
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Aliyu, Ibrahim (author), Feliciano, Marco Carlo (author), van Engelenburg, S.H. (author), Kim, Dong Ok (author), Lim, Chang Gyoon (author)
In-vehicle communication systems are usually managed by controller area networks (CAN). By broadcasting packets to their bus, the CAN facilitates the interaction between Electronic Control Units (ECU) that coordinate, monitor and control internal vehicle components. With no authentication mechanism for identifying the legitimacy and source of...
journal article 2021
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Ding, Kaixin (author)
Intrusion detection problem in Industrial Control Systems(ICS), such as water treat- ment plant and power grid, is an important real-world problem. Real-time anomaly detection have been proposed to minimize the risk of cyber attack. In this study, two different kind of intrusion detection mode-based approach are learned from normal behaviour of...
master thesis 2020
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Simidžioski, Magdalena (author)
To detect malicious activities in a network, intrusion detection systems are used. Even though these solutions are widely deployed for this purpose they have one serious shortcoming which is the huge amount of false alarms that they are generating. Different measures are taken to tackle this problem such as manually changing the settings of the...
master thesis 2020
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Erba, Alessandro (author), Taormina, R. (author), Galelli, Stefano (author), Pogliani, Marcello (author), Carminati, Michele (author), Zanero, Stefano (author), Tippenhauer, Nils Ole (author)
Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process models learned a priori....
conference paper 2020
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Herrera Semenets, V. (author), Pérez-García, Osvaldo Andrés (author), Hernández-León, Raudel (author), van den Berg, Jan (author), Dörr, C. (author)
In the last few years, the telecommunications scenario has experienced an increase in the volume of information generated, as well as in the execution of malicious activities. In order to complement Intrusion Detection Systems (IDSs), data mining techniques have begun to play a fundamental role in data analysis. On the other hand, the...
journal article 2018
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Luchs, M. (author)
The world is rapidly embracing networked technology and transitioning into one of hyperconnectivity, a term first coined by social scientists Anabel Quan-Haase and Barry Wellman. Increased connectivity provides benefits such as automation and, remote access and control of networks and equipment, thereby decreasing operational costs. Maritime and...
master thesis 2016
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