bootstrap carousel

        

CYBER SECURITY 

 ARTIFICIAL NEURAL NET FOR INTRUSIUON DETECTION

Mobirise

Rapid increase in internet and network technologies has led to considerable increase in number of attacks and intrusions. Detection and prevention of these attacks has become an important part of security. Intrusion detection system is one of the important ways to achieve high security in computer networks and used to thwart different attacks. 

Mobirise themes are based on Bootstrap 3 and Bootstrap 4 - most powerful mobile first framework. Now, even if you're not code-savvy, you can be a part of an exciting growing bootstrap community.

Choose from the large selection of latest pre-made blocks - full-screen intro, bootstrap carousel, content slider, responsive image gallery with lightbox, parallax scrolling, video backgrounds, hamburger menu, sticky header and more.

Sites made with Mobirise are 100% mobile-friendly according the latest Google Test and Google loves those websites (officially)!

Mobirise themes are based on Bootstrap 3 and Bootstrap 4 - most powerful mobile first framework. Now, even if you're not code-savvy, you can be a part of an exciting growing bootstrap community.

Choose from the large selection of latest pre-made blocks - full-screen intro, bootstrap carousel, content slider, responsive image gallery with lightbox, parallax scrolling, video backgrounds, hamburger menu, sticky header and more.

Intrusion detection systems have curse of dimensionality which tends to increase time complexity and decrease resource utilization. As a result, it is desirable that important features of data must be analyzed by intrusion detection system to reduce dimensionality. This work proposes an intelligent system which first performs feature ranking on the basis of information gain and correlation. Feature reduction is then done by combining ranks obtained from both information gain and correlation using a novel approach to identify useful and useless features. These reduced features are then fed to a feed forward neural network for training and testing on KDD99 dataset. 

The system then behaves intelligently to classify test data into attack and non-attack classes. The aim of the feature reduced system is to achieve same degree of performance as a normal system. The system is tested on five different test datasets and both individual and average results of all datasets are reported. Comparison of proposed method with and without feature reduction is done in terms of various performance metrics. Comparisons with recent and relevant approaches are also tabled. Results obtained for proposed method are really encouraging.


Akashdeep, Ishfaq Manzoor, Neeraj, “A feature reduced intrusion detection system using ANN classifier” Expert Systems with Applications, Elsevier, IF 3.928, SCI Indexed, Vol 88, 2017, 249-257.

Address 

University Institute of Engineering & Tech, Panjab University, South Campus, Sector-25, Chandigarh 160036 India

Contact 

Email: maivriklabs@gmail.com 


CONTACT PAGE

Links 

Panjab University       
UIET    
Centre of SKill Devlopment & Entreprenureship  (CSDE)

Feedback 

Please send us your ideas, bug reports, suggestions !             Feedback would be appreciated.