An Estimation of Machine Learning Approaches for Intrusion Detection System

Tahiri, Paryana; Sonia, Sonia; Jain, Pankaj; Gupta, Gaurav; Salehi, Ahmad Waleed; Tajjour, Salwan

doi:10.1109/icacite51222.2021.9404643

Cited by 15 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper adds to this growing effort of research intoxicity classification, an analysis of approaches to relieve bias in text classification tasks achieving high accuracy and F1-score which were the measures of classification as in [ 29 ]. Our proposed model used pretrained word embeddings to pertaining classification models instead of training them on the training dataset solely which causes vulnerability to bias.…”

Section: Related Workmentioning

confidence: 99%

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

Alsharef

Aggarwal

Sonia

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative data containing a large number of comments classified as toxic or not. Results found that acceptable accuracy of 94% and an F1-score of 0.89 were achieved using LSTM with BERT word embeddings in the binary classification of comments (toxic and nontoxic). A combination of LSTM and BERT performed better than both LSTM unaccompanied and LSTM with Glove word embedding. This paper tries to solve the problem of classifying comments with high accuracy by pertaining models with larger corpora of text (high-quality word embedding) rather than the training data solely.

show abstract

Section: Related Workmentioning

confidence: 99%

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

Alsharef

Aggarwal

Sonia

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The results show that the Random Forest classifier has better performance with 87% accuracy, 98% precision and 84% Fmeasure. Tahiri, P. et al [11] evaluated the KddCup99 dataset, they extracted 494020 instances and 42 attributes as a training set to find the best performing classifier. In terms of accuracy, the lowest is 92.10% for Naive Bayes classifier; the highest is 97.69% for Random Forest classifier.…”

Section: Ids and Machine Learningmentioning

confidence: 99%

An Ensemble Learning Voting Technique for Network Intrusion Detection Systems

Ding

Popoola

2023

Preprint

View full text Add to dashboard Cite

In the face of growing and complex network threats, intrusion detection systems are important to detect and mitigate these network threats. Existing research has shown that using a single machine learning classifier or traditional ensemble model for threat detection methods cannot adapt to today's complex network environment. To address the above problems, this paper proposes a novel voting model based on ensemble learning to handle hard-to-classify data such as complex network datasets. This model combines multiple voting mechanisms, and also supports human intervention for more complex data where the classification results may not be highly accurate. The model adopts a voting mechanism among seven base models which includes decision tree, LightGBM, linear discriminant analysis, logistic regression, random forest, gaussian naive bayes, and AdaBoost. The resulting ensemble model has been trained and validated on three popular datasets KDDCup99, UNSW-NB15, CIC-IDS2017 and an additional dataset namely “UC dataset” obtained from University of Cincinnati. We use the popular four metrics of accuracy, precision, recall and f1-score to measure the effectiveness of the model. The results show that the accuracy of the classifiers can be improved to more than 0.99. The model can also be used to effectively improve low-performing classifiers.

show abstract

“…2 The Machine learning (ML) techniques are able to provide better inputs to even the most experienced experts in many domains and proved their capability to handle sophisticated problems. [3][4][5][6][7][8][9][10][11][12][13][14][15][16] Skin cancer problem has attracted the attention of researchers in the last decades due to the availability of many datasets. Therefore, there is a huge potential to improve the accuracy of the ML models.…”

Section: Introductionmentioning

confidence: 99%

“…Dermatoscopy is one of the non‐invasive examination tools, which is widely used by dermatologists to get more clear details rather than an examination with the naked eye 2 . The Machine learning (ML) techniques are able to provide better inputs to even the most experienced experts in many domains and proved their capability to handle sophisticated problems 3–16 …”

Section: Introductionmentioning

confidence: 99%

A novel hybrid artificial neural network technique for the early skin cancer diagnosis using color space conversions of original images

Tajjour

Garg

Chandel

et al. 2022

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

In this study, an innovative hybrid machine learning-technique is used for the early skin cancer diagnosis fusing Convolutional Neural Network and Multilayer Perceptron to analyze images and information related to the skin cancer. This information is extracted manually after applying different color space conversions on the original images for better screening of the lesions. The proposed architecture is compared with standalone architecture in addition to some other techniques by commonly used evaluation metrics. HAM10000 dataset is used for training and testing as this data contain seven different skin lesions. The novelty of the proposed hybrid model is the structure of the network which handles structured data (patients' metadata and other useful features from different color spaces related to the illumination, energy, darkness, etc.) and unstructured data (images). The results show an overall 86%, 95% top-1 and top-2 accuracy respectively, and 96% area under the curve for the seven classes. The study demonstrates the superiority of the proposed hybrid model with a 2% improvement in the accuracy over the standalone model and a promising behavior as compared to the ensemble techniques. The follow-up research will include more patient data to develop a skin cancer detection device.

show abstract

An Estimation of Machine Learning Approaches for Intrusion Detection System

Cited by 15 publications

References 8 publications

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

An Ensemble Learning Voting Technique for Network Intrusion Detection Systems

A novel hybrid artificial neural network technique for the early skin cancer diagnosis using color space conversions of original images

Contact Info

Product

Resources

About