IPv6 Network Attack Detection with HoneydV6

Author(s):  
Sven Schindler ◽  
Bettina Schnor ◽  
Simon Kiertscher ◽  
Thomas Scheffler ◽  
Eldad Zack
2014 ◽  
Vol 556-562 ◽  
pp. 6015-6018
Author(s):  
Xiang Yang

The proposed IPv6 protocol has brought new problems for network security. The new protocol and features were introduced for IPv6 and the new type of attack applicable in the environment of IPv6 was summarized. And then, according to the idea of the attack detection, computers themselves can discover the vulnerabilities and deficiencies of their own systems. Thus the vulnerabilities will be patched up and the security of the system will be improved. We focused our research on designing and realizing the IPv6 network attack platform by designing the overall structure of the platform. Finally, we tested the IPv6 network attack platform. Test results show that the attack effect is remarkable, which plays a role in finding computers’ system vulnerabilities, strengthening security protection and enhancing network security.


2021 ◽  
pp. 1-30
Author(s):  
Qingtian Zou ◽  
Anoop Singhal ◽  
Xiaoyan Sun ◽  
Peng Liu

Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.


2021 ◽  
Author(s):  
Tong Yu ◽  
Ming Xie ◽  
Xin Li ◽  
Ying Ling ◽  
Dongmei Bin ◽  
...  

2021 ◽  
Author(s):  
Youssef F. Sallam ◽  
Hossam El-din H. Ahmed ◽  
Adel Saleeb ◽  
Nirmeen A. El-Bahnasawy ◽  
Fathi E. Abd El-Samie

Author(s):  
Mouhammd Sharari Alkasassbeh ◽  
Mohannad Zead Khairallah

Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.


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