Towards Practical Anomaly-Based Intrusion Detection by Outlier Mining on TCP Packets

Manandhar, Prajowal; Aung, Zeyar

doi:10.1007/978-3-319-10085-2_14

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…K-Map is useful for distinction of the data but it is not supported for large volume of data. Support Vector Machine (SVM) [19][20][21][22][23] [ [25][26][27][28][29][30][31][32]: is beneficial for classification and reformation of data, but at the same time SVM only cannot powerfully in recognizing about new data. Given a set of trained-data-set which require more computational time for big-data [4,12].…”

Section: B Related Workmentioning

confidence: 99%

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Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Kumar*,

Kumar

2019

IJITEE

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 Abstract: Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.

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Section: B Related Workmentioning

confidence: 99%

“…Fuzzy Logic based techniques found best in detection of intrusion for as compared to Data Mining, K-Map [21], MLP [25][26][27][28][29][30], Random Forest [25][26][27][28][29][30], SVM [32,33], Neural Network [31][32][33] and dimensionality reduction [35][36][37].…”

Section: B Related Workmentioning

confidence: 99%

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Kumar*,

Kumar

2019

IJITEE

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“…SVM is based on the hyper-plane and transforms the data from lower dimension into the higher dimensions and there in high dimensional space tries to find a hyper-plane that effectively distinguishes two or more classes. From work [19] Given n training data points {(x 1 , y 1 ), (x 2 ,y 2 ), (x 3 , y 3 ), ..., (x n , y n )}, where xi ∈ R d and y i ∈ {+1, −1}. Consider a hyper-plane defined by (w, b), where w is a weight vector and b is a bias, new object x can be classified with…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Fusion of Feature Extraction and Feature Selection Technique for Network Intrusion Detection

Hamid¹,

Sugumaran²,

Journaux³

2016

IJSIA

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With varied and widespread attacks on information systems, intrusion detection systems (IDS) have become an indispensable part of security policy for protecting data. IDS monitor event logs and network traffic to uncover suspicious connections that deviate from the regular profile and identify them as threats or attacks. Like most of the cases the dataset used for intrusion detection i.e., KDD99 suffers two problems: imbalanced class distribution and curse of dimensionality. In this work SMOTE has been used for balancing the dataset and once balanced, Principal Component Analysis (PCA) has been used to extract the features. And after that on the transformed dataset Correlation based Feature Selection (CFS) is used to select a subset of important features. The reduced dimension dataset is tested with Support Vector Machines (SVM). Obtained results demonstrate improved detection accuracy, computational efficiency with minimal false alarms and less system resources utilization

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“…Intrusion detection is an efficient method of dealing with network security related problems [1]. Network Security has become a serious concern due to the development and expansion in the field of Information Technology [2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an effective and timely Intrusion Detection System, which helps to enhance the security of a network, is needed when attack(s) is/are noticed [3]. Intrusion detection is a security approach used to protect computer networks from unauthorised access [1].…”

Section: Introductionmentioning

confidence: 99%

Denial of Service (DoS) Attacks using PART Rule and Decision Table Rule

Isaiah¹

2017

J Electr Electron Syst

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Towards Practical Anomaly-Based Intrusion Detection by Outlier Mining on TCP Packets

Cited by 15 publications

References 12 publications

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

A Fusion of Feature Extraction and Feature Selection Technique for Network Intrusion Detection

Denial of Service (DoS) Attacks using PART Rule and Decision Table Rule

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