• Corpus ID: 201932076

Performance Analysis of Big Data Intrusion Detection System over Random Forest Algorithm

@inproceedings{Alawsat2018PerformanceAO,
  title={Performance Analysis of Big Data Intrusion Detection System over Random Forest Algorithm},
  author={Al-Furat Al-awsat},
  year={2018}
}
The Internet has grown rapidly in the last ten years. Consequently, the interconnection of computers and network devices has become so complex for monitoring that even the security experts do not fully understand its deepest inner workings. Personal computers have become very fast every year. It is not rare for a very ordinary person to connect to the Internet through 20 Mbs lines or faster. With this huge network data the network security has becomes very important for monitoring the data. The… 

Figures and Tables from this paper

A Reliable Network Intrusion Detection Approach Using Decision Tree with Enhanced Data Quality

A reliable network intrusion detection approach using decision tree with enhanced data quality is proposed, which gives many advantages compared to the other models in terms of accuracy, detection rate (DR), and false alarm rate (FAR).

Big Data Security Analysis in Network Intrusion Detection System

Big data security analysis with the help of different techniques used in network intrusion detection system is introduced and results obtained after using NS-3 based svm classifier using KDD Cup 99 Dataset showed the accuracy of 99 percent.

Performance Evaluation of Classification Algorithms in the Design of Apache Spark based Intrusion Detection System

The design of Apache Spark and classification algorithm-based IDS is presented and Chi-square as a feature selection method for selecting the features from network security events data is employed and the performance of Logistic Regression, Decision Tree and SVM is evaluated.

Intrusion detection model using naive bayes and deep learning technique

The smart hybrid model was developed to explore any penetrations inside the network to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms.

Anomaly-based Intrusion Detection using Machine Learning Algorithms-A Review Paper

An overview of various IDS and also the detailed analyses of various machine learning techniques and datasets used for improving IDS are presented.

An Enhanced Intrusion Detection System Using Combinational Feature Ranking and Machine Learning Algorithms

A novel approach where the combination of feature selection techniques which includes Pearson Correlation (PC), Information Gain (IG), ExtraTreeClassifier (ET), and Chi-Square tests are used to rank the features using the weighted average, giving the best detection accuracy and precision.

Performance Analysis of Network Attack Detection Framework using Machine Learning

The intrusion detection model developed in this analytical research utilises various machine learning classifiers like Random Forest, SVM, K-Nearest Neighbor, and Naïve Bayes to demonstrate that the approach is modular in structure.

Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data

Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoefding Adaptive Tree performed the best, by dealing with concept drift in the most effective way.

An Approach for Optimizing Ensemble Intrusion Detection Systems

This study aims to find the best relevant selected features that can be used as important features in a new IDS dataset and demonstrates the optimized ensemble IDSs using (SU and BN) and using (OR and J48) with respective ten and six best respective selected features.

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time.

References

SHOWING 1-10 OF 19 REFERENCES

Towards MapReduce based classification approaches for Intrusion Detection

Naïve Bayes and K-Nearest Neighbor classifier in MapReduce framework and their performance comparison with WEKA implementations and the preliminary analysis over NSL-KDD seems to be promising.

Relevance feature selection with data cleaning for intrusion detection system

The approach presented in this paper leads to a selection of most relevance features and it is expected that the intrusion detection research using KDD'99-based datasets will benefit from the good understanding of network features and their influences to attacks.

Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification

  • R. ChitrakarHuang Chuanhe
  • Computer Science
    2012 8th International Conference on Wireless Communications, Networking and Mobile Computing
  • 2012
The attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique to group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification.

Development of an Intrusion Detection System based on Big Data for Detecting Unknown Attacks

A new model has been proposed based on Big Data for detecting unknown attacks based on pattern matching methods which is expected to be the basis of the future Advanced Persistent Threat detection and prevention system implementations.

Big Data Analytics for Network Intrusion Detection: A Survey

Methods and subsequent evaluation criteria for network intrusion detection, stream data characteristics and stream processing systems, feature extraction and data reduction, conventional data mining and machine learning, deep learning, and Big Data analytics in network intrusion Detection are presented.

Knowledge Discovery from Big Data for Intrusion Detection Using LDA

This paper identifies the "hidden" patterns of operations conducted by both normal users and malicious users from a large volume of network/systems logs by mapping this problem to the topic modeling problem and leveraging the well established LDA models and learning algorithms.

An Intrusion-Detection Model

  • D. Denning
  • Computer Science
    1986 IEEE Symposium on Security and Privacy
  • 1986
A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described. The model is based on the hypothesis that

The role of big data in improving power system operation and protection

  • M. KezunovicLe XieS. Grijalva
  • Computer Science
    2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid
  • 2013
This paper focuses on the use of extremely large data sets in power system operation, control, and protection, which are difficult to process with traditional database tools and often termed big

Mining Patterns with Attribute Oriented Induction

Mining data in human activity life such as business, education, engineering, health and so on, is important and help human itself in order to justify their decision making process, particularly for those who interest with AOI data mining technique as datamining technique which can summarize many pattern into simple patterns.

Secure Outsourcing of Network Flow Data Analysis

This paper identifies a new and challenging application for the growing field of research on data anonymization and secure outsourcing of storage and computations to the cloud, and presents representative use-cases and problems.