AI News, A Review of Feature Reduction in Intrusion Detection System Based ... artificial intelligence

Dr. Monther Aldwairi

Dr. Monther Aldwairi is an associate professor at the College of Technological Innovation at Zayed University since the fall of 2014.

and PhD in computer engineering from North Carolina State University (NCSU), Raleigh, NC, in 2001 and 2006, respectively.

Dr. Aldwairi’s research interests are in information, network and web security, intrusion detection systems, digital forensics, reconfigurable architectures, parallel architectures and algorithms, artificial intelligence and pattern matching algorithms.

M F2 -0- 012 +971 2 599 3238 Information Security Digital Forensics Ongoing Projects

Real-time big data analytics-based intrusion detections and alerts system.

Investigation of the impact of mobile and cloud-based technologies on efficiency and security of networked healthcare systems.

multilayer framework for assurance security.

Characterizing standard and realistic signature-based intrusion detection benchmarks and testing engine. Shadi Aljawarneh, Monther Aldwairi, Muneer Bani Yassein, Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model, 

140-163, Article TOBIOIJ-11-140, Jul 31, 2018, DOI:

Mahmoud Al-Ayyoub, Yaser Jararweh, Abdullateef Rabab'ah, Monther Aldwairi, Feature extraction and selection for Arabic tweets authorship authentication, Journal of Ambient Intelligence and Humanized Computing, Vol. Monther Aldwairi, Yaser Khamayseh, and Mohammad Al-Masri, “Application of artificial bee colony for intrusion detection systems”, Security and Communication Networks, John Wiley &

A classifier system for predicting RNA secondary structure.

Monther Aldwairi and Ali Alwahedi, Detecting fake news in social media networks, Procedia Computer Science,Vol 141, pp.

134, pp 371-376, August 13-15, 2018, Gran Canaria, Spain. Mohammad Abu Qbeitah and Monther Aldwairi, Dynamic Malware Analysis of Phishing Emails, the 9th International Conference on Information and Communication Systems (ICICS 2018), Irbid, Jordan, 3-5, April 2018.

Monther Aldwairi, Duaa Al-ansari, 'Exscind: Fast pattern matching for intrusion detection using exclusion and inclusion filters', 7th International Conference on Next Generation Web Services Practices (NWeSP), pp.24-30, Salamanca, Spain, 19-21 Oct. 2011.

Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection

In the study of Japkowicz [15], most previously designed concept-learning systems assume that a training dataset is generally well balanced.

Second, they discussed several basic resampling or cost-modifying methods to compare the efficiency of the previously proposed class imbalance problems.

Finally, they conducted studies with the assumption that class imbalance problems also affected other classification systems, such as decision trees, neural networks, and SVMs.

In data mining, most of the datasets have the class imbalance problem, and data mining tools learn from imbalanced datasets.

The classifier, which learns from a minority class with very few instances, tends to be biased towards a high accuracy in the prediction of the majority class.

In the experiments with SMOTEBoost applied to several datasets with a high or moderate class imbalance, the classification performance for the minority class and the overall F-measure was improved.

Drummond and Holte [18] used two commonly used sampling methods for applying machine learning to imbalanced classes and misclassification costs.

They adopted a performance analysis technique called cost curves to explore the interaction of oversampling and undersampling with the decision tree classifier C4.5.

However, it is recommended that the cheapest cost classifier becomes a part of the standard since it can be better than undersampling for relatively modest costs.

The experimental results showed that as the number of classes increases, the degree of class imbalance worsens and the efficiency of classification deteriorates.

First, the EasyEnsemble algorithm samples several subsets from the majority class, trains a learner using each subset, and then combines the outputs of the learners.

At each step, instances of the majority class that are correctly classified by the current trained learners are removed from further consideration.

They chose fifteen datasets in various applications and then conducted experiments with four learners (C4.5D, C4.5N, naive Bayes (NB), and repeated incremental pruning) to produce error reduction (RIPPER) over four evaluation matrices.

Compared with other intrusion detection systems that are based on the same dataset, this system showed better performance in the detection of DoS and Probe attacks, and the best performance in overall accuracy.

Building an intrusion detection system using a filter-based feature selection algorithm

Building an intrusion detection system using a filter-based feature selection algorithm in Java TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING ...

Building an intrusion detection system using a filter based feature selection algorithm

2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 and ..

12.2: Programming KDD99 with Keras TensorFlow, Intrusion Detection System (IDS) (Module 12, Part 2)

Creating an intrusion detection system (IDS) with Keras and Tensorflow, with the KDD-99 dataset. This video is part of a course that is taught in a hybrid format at ...

Building an Intrusion Detection System Using a Filter Based Feature Selection Algorithm

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm - Contact : 9108 159 759 , Email :

Building an intrusion detection system using a filter-based feature selection algorithm

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of ...

Machine Learning for Intrusion Detectors from attacking data

Jens Ludwig: "Machine Learning in the Criminal Justice System" | Talks at Google

Jens Ludwig, Director of the University of Chicago Crime Lab, talks about applying machine learning to reducing crime in Chicago and other public policy areas.

AI Final Presentation: Anomaly Based Intrusion Detection System

This is my submission for the final AI project. I've chosen Network Intrusion detection as my project topic. I've used multi-class classification and hierarchical ...

intrusion detection projects

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#HITB2018AMS D1T2 - Applying Machine Learning to User Behavior Anomaly Analysis - Eugene Neyolov

This talk is based on results of R&D project aimed to build a solution for user behavior security analytics. I will describe various methods and ideas for anomaly ...