scholarly journals Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning

2020 ◽  
Vol 5 (10) ◽  
pp. 1168-1173
Author(s):  
Shah Md. Istiaque ◽  
Asif Iqbal Khan ◽  
Sajjad Waheed
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Detection System ◽  
Regression Tree ◽  
Smart Phone ◽  
Classification And Regression Tree ◽  
Efficient Manner ◽  
Firewall System ◽  
Present World

In the present world, digital intruders can exploit the vulnerabilities of a network and are capable to collapse even a country. Attack in Estonia by digital intruders, attack in Iran's nuclear plant and intrusion of spyware in smart phone depicts the efficiency of attackers. Furthermore, centralized firewall system is not enough for ensuring a secured network. Hence, in the age of big data, where availability of data is huge and computation capability of PC is also high, there machine learning and network security have become two inseparable issues. In this thesis, KDD Cup’99 intrusion detection dataset is used. Total 3, 11,030 numbers of records with 41 features are available in the dataset. For finding the anomalies of the network four machine learning methods are used like Classification and Regression Tree (CART), Random Forest, Naive Bayes and Multi-Layer Perception. Initially all 41 features are used to find out the accuracy. Among all the methods, Random Forest provides 98.547% accuracy in intrusion detection which is maximum, and CART shows maximum accuracy (99.086%) to find normal flow of data. Gradually selective 15 features were taken to test the accuracy and it was found that Random Forest is still efficient (accuracy 98.266%) in detecting the fault of the network. In both cases MLP found to be a stable method where accuracy regarding benign data and intrusion are always close to 95% (93.387%, 94.312% and 95.0075, 93.652% respectively). Finally, an IDS model is proposed where Random Forest of ML method and MLP of DL method is incorporated, to handle the intrusion in a most efficient manner.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

scholarly journals Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia

2021 ◽  
Vol 11 (5) ◽  
pp. 2235
Author(s):  
Haewon Byeon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Prediction Performance ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Hypokinetic Dysarthria ◽  
Order Of Magnitude

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.

scholarly journals Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia

2021 ◽  
Vol 20 (2) ◽  
pp. 417-426
Author(s):  
Pardomuan Robinson Sihombing ◽  
Istiqomatul Fajriyah Yuliati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Naive Bayes ◽  
Regression Tree ◽  
Imbalanced Data ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Classification And Regression ◽  
Sensitivity Specificity

Penelitian ini akan mengkaji penerapan beberapa metode machine learning dengan memperhatikan kasus imbalanced data dalam pemodelan klasifikasi untuk penentuan risiko kejadian bayi dengan BBLR yang diharapkan dapat menjadi solusi dalam menurunkan kelahiran bayi dengan BBLR di Indonesia. Adapun metode meachine learning yang digunakan adalah Classification and Regression Tree (CART), Naïve Bayes, Random Forest dan Support Vector Machine (SVM). Pemodelan klasifikasi dengan menggunakan teknik resample pada kasus imbalanced data dan set data besar terbukti mampu meningkatkan ketepatan klasifikasi khususnya terhadap kelas minoritas yang dapat diihat dari nilai sensitivity yang tinggi dibandingkan data asli (tanpa treatment). Selanjutnya, dari kelima model klasifikasi yang iuji menunjukkan bahwa model random forest memberikan kinerja terbaik berdasarkan nilai sensitivity, specificity, G-mean dan AUC tertinggi. Variabel terpenting/paling berpengaruh dalam klasifikasi resiko kejadian BBLR adalah jarak dan urutan kelahiran, pemeriksaan kehamilan, dan umur ibu

scholarly journals Anomaly based Intrusion Detection System using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 255-260
Author(s):  
Akshat Runwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
High Detection Rate ◽  
Network Intrusion ◽  
Average Accuracy

Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on NSL- KDD dataset after extracting some of the relevant records and then several experiments have been performed and evaluated to assess various machine learning classifiers based on dataset. The implemented experiments demonstrated that the Random forest classifier has achieved the highest average accuracy and has outperformed the other models in various evaluations. Keywords: Intrusion Detection System, Anomaly Detection, Machine Learning, Random Forest, Network Security

scholarly journals Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Cybersecurity ◽  
2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Raisa Abedin Disha ◽  
Sajjad Waheed
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Performance Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Experimental Result ◽  
Feature Selection Technique ◽  
Selection Technique

AbstractTo protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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