Results 11 to 20 of about 290,193 (257)
Using anomaly detection method to detect network attacks
Bulletin of V.N. Karazin Kharkiv National University, series «Mathematical modeling. Information technology. Automated control systems», 2023 The article is focused on the description of a model for detecting network intrusions in the network traffic based on the TCP/IP protocol stack. The main objects of a local area network have been analyzed. The main controlled parameters of each type of object have been described.
Svitlana Demenkova +3 more
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Detection and Defense Mechanism of LDoS Attack in SDN Environment
Jisuanji kexue yu tansuo, 2020 Low-rate denial of service (LDoS) attack is a new type of network attack, which is characterized by low attack cost and strong concealment. As a new type of network architecture, software defined network (SDN) is also threatened by LDoS attacks.
YAN Tong, BAI Zhihua, GAO Zhen, YAN Lina, ZHOU Lei
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Wormhole attack detection techniques in ad-hoc network: A systematic review
Open Computer Science, 2022 Mobile ad hoc networks (MANETs) are considered as decentralized networks, which can communicate without pre-existing infrastructure. Owning to utilization of open medium access and dynamically changing network topology, MANETs are vulnerable to different
Gupta Chitvan +2 more
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Message Dropping Attacks in Overlay Networks: Attack Detection and Attacker Identification [PDF]
2006 Securecomm and Workshops, 2006 Overlay multicast networks are used by service providers to distribute contents such as Web pages, static and streaming multimedia data, or security updates to a large number of users. However, such networks are extremely vulnerable to message-dropping attacks by malicious or selfish nodes that intentionally drop the packets they are required to ...
Liang Xie, Sencun Zhu
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Detection of DDoS Attacks in Software Defined Networking Using Entropy
Applied Sciences, 2021 Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN ...
Cong Fan +5 more
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LDoS attack detection method based on simple statistical features
Tongxin xuebao, 2022 Traditional low-rate denial of service (LDoS) attack detection methods were complex in feature extraction, high in computational cost, single in experimental data background settings, and outdated in attack scenarios, so it was difficult to meet the ...
Xueyuan DUAN, Yu FU, Kun WANG, Bin LI
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Multi-type low-rate DDoS attack detection method based on hybrid deep learning
网络与信息安全学报, 2022 Low-Rate distributed denial of service (DDoS) attack attacks the vulnerabilities in the adaptive mechanism of network protocols, posing a huge threat to the quality of network services.Low-Rate DDoS attack was characterized by high secrecy, low attack ...
Lijuan LI +3 more
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Wireless Network Intrusion Detection Algorithm Based on Multiple Perspectives Hierarchical Clustering [PDF]
Jisuanji kexue yu tansuo, 2022 Aiming at the problems of high false detection rate, difficult to find unknown attack behavior and high cost of obtaining marked data in existing wireless network intrusion detection algorithms based on supervised learning, this paper proposes an ...
DONG Xinyu, XIE Bin, ZHAO Xusheng, GAO Xinbao
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Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
Algorithms, 2022 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 ...
Xiaoyu Du +3 more
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Network Attacks Detection by Hierarchical Neural Network
Computer Engineering and Applications Journal, 2015 Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous.
Mohammad Hassan Nattaj +1 more
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