scholarly journals Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks

2014 ◽  
Vol 70 ◽  
pp. 103-117 ◽  
Author(s):  
Wei Wang ◽  
Thomas Guyet ◽  
René Quiniou ◽  
Marie-Odile Cordier ◽  
Florent Masseglia ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Data Streams ◽  
Audit Data

Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining

2014 ◽  
pp. 89-102
Author(s):  
Mrutyunjaya Panda ◽  
Manas Ranjan Patra
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Computer Networks ◽  
Time Data ◽  
Security Risks ◽  
Security Systems ◽  
Online Data ◽  
Audit Data ◽  
Using Data ◽  
Intrusion Detection And Prevention

Intrusion Detection and Prevention Systems (IDPS) are being widely implemented to prevent suspicious threats in computer networks. Intrusion detection and prevention systems are security systems that are used to detect and prevent security threats to computer networks. In order to understand the security risks and IDPS, in this chapter, the authors make a quick review on classification of the IDPSs and categorize them in certain groups. Further, in order to improve accuracy and security, data mining techniques have been used to analyze audit data and extract features that can distinguish normal activities from intrusions. Experiments have been conducted for building efficient intrusion detection and prevention systems by combining online detection and offline data mining. During online data examination, real-time data are captured and are passed through a detection engine that uses a set of rules and parameters for analysis. During offline data mining, necessary knowledge is extracted about the process of intrusion.

Processing of massive audit data streams for real-time anomaly intrusion detection

2008 ◽  
Vol 31 (1) ◽  
pp. 58-72 ◽  
Author(s):  
Wei Wang ◽  
Xiaohong Guan ◽  
Xiangliang Zhang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Data Streams ◽  
Audit Data ◽  
Anomaly Intrusion Detection

Implementasi Iptables Firewall dan Intrusion Detection System Untuk Mencegah Serangan DDoS Pada Linux Server

2021 ◽  
pp. 19-23
Author(s):  
Theodorus Kristian Widianto ◽  
Wiwin Sulistyo
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Ddos Attacks ◽  
Server Side ◽  
Ddos Attack ◽  
Internet Users ◽  
Data Theft ◽  
Server Computer

Security on computer networks is currently a matter that must be considered especially for internet users because many risks must be borne if this is negligent of attention. Data theft, system destruction, and so on are threats to users, especially on the server-side. DDoS is a method of attack that is quite popular and is often used to bring down servers. This method runs by consuming resources on the server computer so that it can no longer serve requests from the user side. With this problem, security is needed to prevent the DDoS attack, one of which is using iptables that has been provided by Linux. Implementing iptables can prevent or stop external DDoS attacks aimed at the server.

scholarly journals Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks

2020 ◽  
Vol 9 (3) ◽  
pp. 1137-1148
Author(s):  
Jafar Majidpour ◽  
Hiwa Hasanzadeh
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Computer Networks ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Training Time ◽  
Modern Computer ◽  
Normal Behavior ◽  
Review Protocol ◽  
Future Work

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.

Abstracting massive data for lightweight intrusion detection in computer networks

2018 ◽  
Vol 433-434 ◽  
pp. 417-430 ◽  
Author(s):  
Wei Wang ◽  
Jiqiang Liu ◽  
Georgios Pitsilis ◽  
Xiangliang Zhang
Keyword(s):  
Intrusion Detection ◽  
Computer Networks ◽  
Massive Data
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