Zobrazeno 1 - 10
of 528
pro vyhledávání: '"Network traffic classification"'
Publikováno v:
Tongxin xuebao, Vol 45, Pp 73-83 (2024)
Launching backdoor attacks against deep learning (DL)-based network traffic classifiers, and a method of malicious traffic escape was proposed based on the backdoor attack. Backdoors were embedded in classifiers by mixing poisoned training samples wi
Externí odkaz:
https://doaj.org/article/687444d7da334b5993d3c9455d132558
Publikováno v:
Радіоелектронні і комп'ютерні системи, Vol 2024, Iss 2, Pp 186-202 (2024)
Abstract. The increasing number of information security incidents in higher education underscores the urgent need for robust cybersecurity measures. This paper proposes a comprehensive framework designed to analyze the illegal use of internet resourc
Externí odkaz:
https://doaj.org/article/d48e189293a441bc919483046e8bcdd6
Publikováno v:
Heliyon, Vol 10, Iss 16, Pp e35962- (2024)
The current popular traffic classification methods based on feature engineering and machine learning are difficult to obtain suitable traffic feature sets for multiple traffic classification tasks. Besides, data privacy policies prohibit network oper
Externí odkaz:
https://doaj.org/article/fa72d1d965a14f4faec293ab8fce2fb8
Publikováno v:
PeerJ Computer Science, Vol 10, p e2145 (2024)
The Internet of Things (IoT) is becoming more prevalent in our daily lives. A recent industry report projected the global IoT market to be worth more than USD 4 trillion by 2032. To cope with the ever-increasing IoT devices in use, identifying and se
Externí odkaz:
https://doaj.org/article/09f306b77ae94677ae59720484766198
Publikováno v:
IEEE Access, Vol 12, Pp 150169-150179 (2024)
Network Traffic Classification (NTC) plays a critical role in modern network security and management, used in various applications such as performance monitoring, anomaly detection, and bandwidth management. Traditional methods like port-based identi
Externí odkaz:
https://doaj.org/article/24785b38bb494546a2b4fa9a4d3425da
Publikováno v:
IEEE Access, Vol 12, Pp 58031-58038 (2024)
Network traffic classification plays a crucial role in detecting malware threats. However, most existing research focuses on extracting statistical features from the network traffic, ignoring the rich information contained within raw packet capture (
Externí odkaz:
https://doaj.org/article/54a010c74bd1478484a3bd61748b7f5f
Publikováno v:
IEEE Access, Vol 12, Pp 19418-19431 (2024)
Tor, a network offering Internet anonymity, presented both positive and potentially malicious applications, leading to the need for efficient Tor traffic monitoring. While most current traffic classification methods rely on flow-based features, these
Externí odkaz:
https://doaj.org/article/60dbc33316f24c3ea83cfa4e25e10838
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 1, Pp 1527-1553 (2024)
Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability fo
Externí odkaz:
https://doaj.org/article/8866a2b089c34a32a85b547438d17b81
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 677-689 (2024)
In-network traffic classification presents an innovative approach to developing early-stage and accurate traffic classification solutions. However, despite its initial accuracy, the one-size-fits-all Machine Learning (ML) model becomes obsolete as tr
Externí odkaz:
https://doaj.org/article/77699d99d4af4fab866fd431c0f12ab3
Autor:
Jin, Zhiping a, Duan, Ke a, Chen, Changhui b, He, Meirong c, Jiang, Shan c, Xue, Hanxiao d, ⁎
Publikováno v:
In Heliyon 30 August 2024 10(16)