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Shweta More, Moad Idrissi, Haitham Mahmoud and A. Taufiq Asyhari
The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for compani...
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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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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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Faeiz Alserhani and Alaa Aljared
With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to...
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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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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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Maya Hilda Lestari Louk and Bayu Adhi Tama
As a system capable of monitoring and evaluating illegitimate network access, an intrusion detection system (IDS) profoundly impacts information security research. Since machine learning techniques constitute the backbone of IDS, it has been challenging ...
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Xiaoyu Du, Cheng Cheng, Yujing Wang and Zhijie Han
Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network att...
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Leonid Safonov
Pág. 109 - 112
Unsupervised anomaly detection in high-dimensional data is an important subject of research in theoretical machine learning and applied areas. One of important applications is anomaly detection in network traffic data, which can be useful for preventing ...
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