Intrusion Detection System Classification Using Different Machine Learning Algorithms on KDD-99 and NSL-KDD  Datasets - A Review Paper

Devi, Ravipati Rama; Abualkibash, Munther

doi:10.5121/ijcsit.2019.11306

Cited by 36 publications

(13 citation statements)

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“…These tree-based classifiers to attain above 99% for accuracy and precision. Different machine learning algorithms been implemented by(40) that include Logistic Regression, Decision Tree (DT), Stochastic Gradient Descent (Adaboost), SVM, Random Forest(RF), Naive Bayes and Multilayer Perceptron and as expected, RF performed the best with achieved the highest accuracy rate of 99.0%. In summary, Decision Tree and Random Forest (which are composed of multiple decision trees) performed well in most studies in comparison to other classifiers for they are known to be efficient and accurate.…”

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confidence: 72%

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

2021

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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of selected features have been adopted to train four machine-learning based classifiers. The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively. These evolutionary-based algorithms are known to be effective in solving optimization problems. The classifiers used in this study are Naïve Bayes, k-Nearest Neighbor, Decision Tree and Support Vector Machine that have been trained and tested using the NSL-KDD dataset. The performance of the abovementioned classifiers using different features values was evaluated. The experimental results indicate that the detection accuracy improves by approximately 1.55% when implemented using the PSO-based selected features than that of using GA-based selected features. The Decision Tree classifier that was trained with PSO-based selected features outperformed other classifiers with accuracy, precision, recall, and f-score result of 99.38%, 99.36%, 99.32%, and 99.34% respectively. The results show that using optimal features coupling with a good classifier in a detection system able to reduce the classifier model building time, reduce the computational burden to analyze data, and consequently attain high detection rate.

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confidence: 72%

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

2021

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“…Therefore, classifiers will not be biased toward more records. Because the testing sets have no history of duplicating, training performance is not influenced by approaches with higher detection capability in repeating data [95]. The proportion of records in the primary KDD dataset is inversely linked to the number picked from each category.…”

Section: Resultsmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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“…Using machine learning algorithms in Intrusion Detection System (IDS) is an attracting research area for cyber security researchers around the world [5][6][7][8][9][10][11][12][13][14]. In this research, experiments were made based on classifying NSL-KDD dataset to either normal or attack.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Machine Learning in Network Security Using KNIME Analytics

Abualkibash¹

2019

IJNSA

Self Cite

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Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.

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Intrusion Detection System Classification Using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets - A Review Paper

Cited by 36 publications

References 0 publications

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Machine Learning in Network Security Using KNIME Analytics

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