A short review on different clustering techniques and their applications
In modern world, we have to deal with huge volumes of data which include image, video,
text and web documents, DNA, microarray gene data, etc. Organizing such data into rational …
text and web documents, DNA, microarray gene data, etc. Organizing such data into rational …
Analysis of autoencoders for network intrusion detection
As network attacks are constantly and dramatically evolving, demonstrating new patterns,
intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques …
intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques …
A review of clustering algorithms for big data
K Djouzi, K Beghdad-Bey - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
Big data is usually defined by five (05) characteristics called 5Vs+ 1C (Volume, Velocity,
Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and …
Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and …
Classification of attack types for intrusion detection systems using a machine learning algorithm
In this paper, we present the results of our experiments to evaluate the performance of
detecting different types of attacks (eg, IDS, Malware, and Shellcode). We analyze the …
detecting different types of attacks (eg, IDS, Malware, and Shellcode). We analyze the …
Network intrusion detection method based on PCA and Bayes algorithm
B Zhang, Z Liu, Y Jia, J Ren… - Security and …, 2018 - Wiley Online Library
Intrusion detection refers to monitoring network data information, quickly detecting intrusion
behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion …
behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion …
Anomaly detection using Support Vector Machine classification with k-Medoids clustering
R Chitrakar, H Chuanhe - 2012 Third Asian himalayas …, 2012 - ieeexplore.ieee.org
Anomaly based Intrusion Detection System, in the recent years, has become more
dependent on learning methods-specially on classifications schemes. To make the …
dependent on learning methods-specially on classifications schemes. To make the …
Anomaly based intrusion detection using hybrid learning approach of combining k-medoids clustering and naive bayes classification
R Chitrakar, C Huang - 2012 8th International Conference on …, 2012 - ieeexplore.ieee.org
The role of Intrusion Detection System (IDS) has been inevitable in the area of Information
and Network Security-specially for building a good network defense infrastructure. Anomaly …
and Network Security-specially for building a good network defense infrastructure. Anomaly …
Detecting malicious websites by learning IP address features
Web-based malware attacks have become one of the most serious threats that need to be
addressed urgently. Several approaches that have attracted attention as promising ways of …
addressed urgently. Several approaches that have attracted attention as promising ways of …
A bayesian classification intrusion detection method based on the fusion of PCA and LDA
Z Shen, Y Zhang, W Chen - Security and Communication …, 2019 - Wiley Online Library
The rapid development of network technology is facing severe security threats while
bringing convenience to people. How to build a secure network environment has become an …
bringing convenience to people. How to build a secure network environment has become an …
An overview of intrusion detection based on data mining techniques
Intrusion Detection System (IDS) is a vital component of any network in today's world of
Internet. IDS are an effective way to detect different kinds of attacks in interconnected …
Internet. IDS are an effective way to detect different kinds of attacks in interconnected …