Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection

Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used in analyzing huge traffic data; thus, an efficient classification technique is necessary to overcome the issue. This problem is considered in this paper. Well-known machine learning techniques, namely, SVM, random forest, and extreme learning machine (ELM) are applied. These techniques are well-known because of their capability in classification. The NSL–knowledge discovery and data mining data set is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that ELM outperforms other approaches

Intrusion Detection: A Deep Learning Approach

Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class intrusion detection include the use of a deep neural network. However, it fails to take into account spatial relationships between the data objects and long term dependencies present in the dataset. The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored.Comment: presented at 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT 2023

Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru di Twitter

The significant increase in the number of users has caused public opinion on the Ruang Guru application to be widely spread through social media, especially Twitter. From 15,000 twitter data taken with the keyword Ruang Guru, a total of 2,358 datasets were obtained through the process of handling duplicates. In this study, sentiment analysis was carried out using the Support Vector Machine (SVM) algorithm which was optimized with Particle Swarm Optimization (PSO) then tested using the 10-Fold Cross Validation method which resulted in the highest accuracy rate of 89.20%, while the Support Vector Machine algorithm (SVM) only produces the highest accuracy rate of 88.56%. There is an increase of 0.64% with Particle Swarm Optimization optimization. Sentiment analysis results are positive, with positive results as much as 1463 data or 62.04% and 895 or 37.96% negative sentiment. From the results of this study, it is expected to be a material consideration for Ruang Guru to improve the quality of the service sector found on social media, especially Twitter.Peningkatan jumlah pengguna yang cukup signifikan menyebabkan opini masyarakat terhadap aplikasi Ruang Guru tersebar luas melalui media sosial khususnya twitter. Dari 15.000 data twitter yang diambil dengan keyword Ruang Guru, didapatkan dataset sejumlah 2.358 melalui proses handling duplicate. Pada penelitian ini dilakukan analisis sentimen dengan menggunakan algoritma Support Vector Machine (SVM) yang dioptimalisasikan dengan Particle Swarm Optimization (PSO) kemudian diuji dengan metode 10-Fold Cross Validation yang menghasilkan tingkat akurasi tertinggi sebesar 89.20%, sementara algoritma Support Vector Machine (SVM) hanya menghasilkan tingkat akurasi tertinggi sebesar 88.56%. Terdapat peningkatan sebesar 0.64% dengan optimalisasi Particle Swarm Optimization. Hasil pada analisis sentimen mayoritas bersifat positif, dengan hasil positif sebanyak 1463 data atau 62.04% dan 895 atau 37.96% sentimen negatif. Dari hasil penelitian ini diharapkan menjadi bahan pertimbangan bagi Ruang Guru untuk meningkatkan kualitas sektor pelayanan yang terdapat pada media sosial khususnya Twitter

Sistem Peringatan Dini Keterlambatan Masa Studi Mahasiswa Menggunakan Metode Support Vector Machine

Students graduating late from college are a common problem in universities. The study of students at universities is generally designed to be completed in 3.5 to 4 years. If a student has not graduated past that time, he is considered late in completing his education. Lambung Mangkurat University, as the oldest university in Kalimantan, also experienced these problems. Therefore, an early warning system was build to predict students' possibility of being late in completing their studies. This study uses a sample of students from the Faculty of Engineering, the University of Lambung Mangkurat, to predict students who will be late graduating from Lambung Mangkurat University since semester 5. This system was to develop using a model built using the Support Vector Machine (SVM) method. Model training conducted using 755 data from Lambung Mangkurat University Faculty of Engineering students from 2010 to 2014. Then, the performance of the model tested using 234 student data from 2015 and 2016. The parameters used were the number of credits, gender, GPA on semester 1 to 4, and study programs. The test results show that the model has good performance to predict students who will be late in completing their studies with 88.2% accuracy

Comparative Study on Multivariate Methods Using Chronic Kidney Disease

The human being is currently one of the most serious illnesses in the modern world, and accurate diagnosis is necessary as soon as possible. In this modern world, there are numerous diseases that exist. Chronic kidney disease is regarded as the most serious of these disorders in humans. There are several methods in the medical area for disease diagnosis, and the prediction criterion is also significant in the medical field for determining the consequences of the study in the future. Many statistical methods are employed in order to forecast the medical dataset and provide accurate and reliable findings. A lot of models are available in multivariate methods to predict the dataset. In this paper, the computational algorithms for detecting CKD using Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR) are reviewed. The first, based on the association, inference for the study. Decision tree and logistic regression approaches are used to more correctly diagnose chronic renal disease based on the results of the association. Finally, the study came to the conclusion that greatest fit for forecasting chronic renal disease

Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network

ABSTRACT Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%

Comparative Study of Machine Learning Algorithms and Correlation Between Input Parameters

The availability of big data and computing power have triggered a big success in Artificial Intelligence (AI) field. Machine Learning (ML) becomes major highlights in AI due to the ability of self-improved as it is fed with more data. Therefore, Machine Learning is suitable to be applied in financial industry especially in detecting financial fraud which is one of the main challenges in financial system. In this paper, 15 different types of supervised machine learning algorithms are studied in order to find the highest accuracy that should be able to detect credit card fraudulent transactions. The best algorithm among these algorithms is then further used and studied to find the correlation between the input variables and the accuracy of the results produced. The results have shown that Multilayer Perceptron (MLP) produced the highest accuracy among the 15 other algorithms with 98% accuracy of detection. Besides that, the input parameters also play an important role in determining the accuracy of the results. Based on the result, when input parameter known as ‘V4’ decreased, the recorded accuracy has increased to 99.17%

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained

Behavior-Based Interpretable Trust Management for IoT Systems

Establishing appropriate trust at the “right” level for the “right” application at the “right” time is important in the constantly evolving environment of the Internet of Things (IoT). Nevertheless, the process is challenging due to the lack of explainability and interpretability of the machine learning models. This paper presents a novel approach to managing IoT trust by employing explainable artificial intelligence (XAI) to connect complex algorithmic decisions with human understanding. Specifically, we propose a mutual information selection technique to determine the most significant behaviour-based features to identify trustworthy and untrustworthy behaviour in IoT device systems. Based on these behaviour-based features we develop a rule-based decision tree (DT) method to help enhance the explainability of our model. We evaluate our approach with a transformed UNSW NB 15 dataset, the results demonstrate improved user trust and system transparency. In addition, we did a comparative analysis of our XAI-behavioural-based trust management system (BB-TMS) with state-of-the-art methods, which demonstrated that our model surpasses competitors in terms of precision and interpretability. This highlights the effectiveness of integrating XAI with traditional machine learning approaches in the IoT domain