Network Intrusion Detection with Auto-encoder and One-class Support Vectors Machine

Recent advances in machine learning have shown promising results for detecting network intrusion through supervised machine learning. However, such techniques are ineffective for new types of attacks. In the preferred unsupervised and semi-supervised cases, these newer techniques suffer from lower accuracy and higher rates of false alarms. This work proposes a machine learning model that combines auto-encoder with one-class support vectors machine. In this model, the auto-encoders learn the representation of the input data in a latent space and reduces the dimensionality of the input data. The dimensionality-reduced input is then extracted from the auto-encoder and passed to a one-class support vectors machine to classify the network event as an attack or a normal event. The model is trained on normal network events only. The proposed model is then evaluated and compared with several existing models. It achieves high accuracy when tested on the NSL-KDD and KDD99 datasets, with total accuracies of 96.24% and 99.45%, respectively.

Quantum-Inspired Classification Algorithm from DBSCAN–Deutsch–Jozsa Support Vectors and Ising Prediction Model

Quantum computing is suggested as a new tool to deal with large data set for machine learning applications. However, many quantum algorithms are too expensive to fit into the small-scale quantum hardware available today and the loading of big classical data into small quantum memory is still an unsolved obstacle. These difficulties lead to the study of quantum-inspired techniques using classical computation. In this work, we propose a new classification method based on support vectors from a DBSCAN–Deutsch–Jozsa ranking and an Ising prediction model. The proposed algorithm has an advantage over standard classical SVM in the scaling with respect to the number of training data at the training phase. The method can be executed in a pure classical computer and can be accelerated in a hybrid quantum–classical computing environment. We demonstrate the applicability of the proposed algorithm with simulations and theory.

Komparasi Algoritma Naïve Bayes dan Support Vectors Machine pada Analisis Sentimen SMS HAM dan SPAM

SMS is a form of communication in the form of messages sent using mobile phones between the designated numbers. SMS is now rarely used because many of the features that have changed are used by chat applications. However, the SMS feature was not removed for one thing, official messages from various applications for leveraging or other official information still use SMS as a sign that the phone number used is there. However, since 2011 there have been so many misuses of this function, so it is suspected that many frauds use SMS as a tool to influence victims. This sms category goes to SMS spam. Therefore, SMS needs to be classified so that users can find out that the SMS is included in the category of Spam or ham (the opposite of spam). Using 400 datasets taken from the UCI repository which is divided into two classes, namely spam and ham, we compare two classification methods, namely Naive Bayes and Support vector Machine in order to get SMS filtering correctly. And after the calculations are done, the accuracy is obtained in Naive Bayes, which is 90.00% Support Vector Machine 81.00%.

Projection of the Impact of Climate Change on Crude Oil Prices Based on Relevance Vector Machine

We propose an alternative algorithm referred to RVM (relevance vector machine) to circumvent the support vector machine’s (SVM) unnecessary use of basic functions, a large number of support vectors, lack of probabilistic prediction, and longer time computation complexity (TCC). Global annual land-ocean average temperature (GASAT) data and WTI oil market price data extracted from the National Aeronautic and Space Administration US and the US Energy Administration, respectively. The data were preprocessed and used to build RVM models. To evaluate the proposed RVM, its performance was compared to that of a SVM. The results were validated using ANOVA. A significant correlation between the two datasets was found. The relevance vectors for the RVM were significantly less than the support vectors for the SVM, and the TCC for the RVM was significantly better than the TCC for the SVM. The prediction accuracy of both the RVM and the SVM were found to be statistically equal. The RVM model was able to project the impact of GASAT on WTI crude oil prices from 2014 to 2023. The projection can be used by intergovernmental organizations to formulate a global response to combat WTI crude oil price negative impact, which is expected to worsen in the next decade.

Support vector machine and its difficulties from control field of view

The application of the support vector machine (SVM) classification algorithm to large-scale datasets is limited due to its use of a large number of support vectors and dependency of its performance on its kernel parameter. In this paper, SVM is redefined as a control system and iterative learning control (ILC) method is used to optimize SVM’s kernel parameter. The ILC technique first defines an error equation and then iteratively updates the kernel function and its regularization parameter using the training error and the previous state of the system. The closed loop structure of the proposed algorithm increases the robustness of the technique to uncertainty and improves its convergence speed. Experimental results were generated using nine standard benchmark datasets covering a wide range of applications. Experimental results show that the proposed method generates superior or very competitive results in term of accuracy than those of classical and state-of-the-art SVM based techniques while using a significantly smaller number of support vectors.