scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals Optimized machine learning algorithm for intrusion detection

2021 ◽  
Vol 24 (1) ◽  
pp. 590
Author(s):  
Royida A. Ibrahem Alhayali ◽  
Mohammad Aljanabi ◽  
Ahmed Hussein Ali ◽  
Mostafa Abdulghfoor Mohammed ◽  
Tole Sutikno
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Detection System ◽  
Optimization Algorithms ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Subset ◽  
Training Time

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.

scholarly journals Towards Intelligent Machine Learning Models for Intrusion Detection System

2021 ◽  
Vol 12 (5) ◽  
pp. 643-655
Author(s):  
Pullagura Indira priyadarsini, P V R N S S V Sai Leela, Bankapalli Jyothi
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Hybrid Algorithm ◽  
Detection System ◽  
Feature Selection Method ◽  
Weighted Averaging ◽  
Support Vector ◽  
Imbalanced Learning ◽  
Data Set

The Internet has become an important resource for mankind. Explicitly information security is an interminable domain to the present world. Hence a more potent Intrusion Detection System (IDS) should be built. Machine Learning techniques are used in developing proficient models for IDS. Imbalanced Learning is a crucial task for many classification processes. Resampling training data towards a more balanced distribution is an effective way to combat this issue. There are most prevalent techniques like under sampling and oversampling.In this paper, the issues of imbalanced data distribution and high dimensionality are addressed using a novel oversampling technique and an innovative feature selection method respectively. Our work suggests a novel hybrid algorithm, HOK-SMOTE which considers an ordered weighted averaging (OWA) approach for choosing the best features from the KDD cup 99 data set and K-Means SMOTE for imbalanced learning. Here an ensemble model is compared against the hybrid algorithm. This ensemble integrates Support Vector Machine (SVM), K Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB) and Decision Tree (DT). Then weighted average voting is applied for prediction of outputs. In this work, much Experimentationwas conducted on various oversampling techniques and traditional classifiers. The results indicate that the proposed work is the most accurate one among other ML techniques. The precision, recall, F-measure, and ROC curve show notable outcomes. Hence K-Means SMOTE in parallel with ensemble learning has given satisfactory results and a precise solution to the imbalanced learning in IDS. It is ascertained whether ensemble modeling or oversampling techniques are dominating for Intrusion data set.

scholarly journals Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

2020 ◽  
Vol 7 (2) ◽  
pp. 329
Author(s):  
Eka Lailatus Sofa ◽  
Subiyanto Subiyanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Routing Attacks

<p class="Abstrak"><em>Internet of Things</em> (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya <em>smart city, smart home, smart street, </em>dan<em> smart industry </em>yang memanfaatkan internet untuk memantau informasi yang dibutuhkan<em>.</em> Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh <em>routing attacks</em> yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> untuk menganalisis model <em>routing attacks</em> lainnya pada jaringan IoT. IDS menggunakan <em>compression header</em> 6LoWPAN sebagai fitur untuk <em>machine learning algorithm</em> dalam mempelajari jenis <em>routing attacks</em>. Skenario simulasi dikembangkan untuk mendeteksi <em>routing attacks</em> berupa <em>selective forwarding attack</em> dan <em>sinkhole attack</em>. Pengujian dilakukan menggunakan <em>feature selection</em> dan <em>machine learning algorithm</em>. <em>Feature selection</em> digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara <em>machine learning algorithm</em> digunakan untuk mengklasifikasikan <em>routing attacks</em> pada jaringan IoT. Ada tujuh <em>machine learning algorithm</em> yang digunakan dalam klasifikasi antara lain <em>Random Forest, Random Tree, J48, Bayes Net, JRip, SMO,</em> dan <em>Naive Bayes</em>. Hasil percobaan disajikan untuk menunjukkan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> dalam menganalisis <em>routing attacks</em>. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan <em>non-</em>serangan.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Internet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol  <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

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