scholarly journals Computer Network Intrusion Detection and Countermeasures

Author(s):  
Liguo Xu ◽  
Jingyuan Chi
Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1854
Author(s):  
Jevgenijus Toldinas ◽  
Algimantas Venčkauskas ◽  
Robertas Damaševičius ◽  
Šarūnas Grigaliūnas ◽  
Nerijus Morkevičius ◽  
...  

The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two publicly available benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.


2014 ◽  
pp. 126-134
Author(s):  
Akira Imada

This article is a consideration on computer network intrusion detection using artificial neural networks, or whatever else using machine learning techniques. We assume an intrusion to a network is like a needle in a haystack not like a family of iris flower, and we consider how an attack can be detected by an intelligent way, if any.


Author(s):  
Xiaolin Luo

Along with improvement of technology in network and continuous expansion of network economy and network applications, the Internet has gradually become an indispensable part of the modern society. However, an endless stream of hacker attacks and network virus events make network security issues stand out. Therefore, network security has become a hot spot in computer network research and development. This paper aims at establishing a real-time detection and dynamic defense security system and makes an in-depth study of intrusion detection technology and defense decision-making technology. The strategy involved in finding the intrusion behavior since the fuzzy base contains the better group of rules. We have utilized an automated fuzzy rule generation strategy. An adaptive network intrusion detection and defense system model is established, and the architecture of the model is discussed in detail. The platform independence, good self-adaptability, expansibility, multi-level data analysis and dynamic defense decision-making are expounded. The experiment proves that the model proposed in this article has a good self-adaptability and open construction, and effectively combines the functions of intrusion detection and defense decision-making.


2007 ◽  
Vol 177 (3) ◽  
pp. 1824-1838 ◽  
Author(s):  
B.A. Fessi ◽  
M. Hamdi ◽  
S. Benabdallah ◽  
N. Boudriga

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