Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic

To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms.

Cucumber, a common economic crop, occupies a large proportion of vegetable cultivation in China. Plant diseases and insect pests, especially the cucumber downy mildew, are important causes for the decrease in the yield of cucumbers. In order to reduce the losses caused by pests and diseases and achieve rapid automatic identification of plant diseases and insect pests, this paper studies machine vision system and disease image detection with support vector machine (SVM) classification algorithm, taking cucumber downy mildew for example. This paper carries out a method study in image acquisition, image preprocessing, feature parameter extraction, and pattern recognition, which obtains satisfactory results. The accuracy of cucumber downy mildew detection reaches 90%, significantly higher than that of artificial recognition.

Electromyography (EMG) signal is an electrical indicator for neuromuscular activation. It provides direct access to physiological processes enabling the muscle to generate force and produce movement in order to accomplish countless functions. As a successful classification of the EMG signal is basically dependent on the selection of the best parameters carefully, this paper proposes a hybrid optimized classification model for EMG signals classification. The proposed system implements grey wolf optimizer (GWO) combined with support vector machines (SVMs) classification algorithm in order to improve the classification accuracy via selecting the optimal settings of SVMs parameters. The proposed approach consists of three phases; namely pre-processing, feature extraction, and GWO-SVMs classification phases. The obtained experimental results obviously indicate that significant enhancements in terms of classification accuracy have been achieved by the proposed GWO-SVMs classification system. It has outperformed the typical SVMs classification algorithm via achieving an accuracy of over 90 % using the radial basis function (RBF) kernel function.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

BBM, or fuel oil, is one of the essential needs of the Indonesian people. The government's policy regarding the increase in fuel prices raises many opinions from the public. Twitter is one of the social media that Indonesian people often use to express opinions on a topic. In this study, sentiment analysis was carried out on public opinion regarding the fuel price increase policy from Twitter social media. This research is expected to help determine public opinion regarding the fuel price increase policy with positive, neutral and negative sentiments. The sentiment analysis method used is the Support Vector Machine (SVM) classification algorithm. The results of the accuracy of SVM were compared with accuracy by adding a feature selection process. The Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) algorithms are used for the feature selection method. After several experiments using the three methods, the SVM method with the Radial Basis Function (RBF) kernel produced the best accuracy of 71.2%. The combination of the SVM method with the RBF and PSO kernels obtained an accuracy of 68.84%, and the combination of the RBF and GA kernel SVM methods obtained an accuracy of 69.52%.

The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heartbeats. The purpose of this work is to propose a diagnostic system for modelling and classification of heartbeat, by use of time features and Support vector machines (SVM) classification algorithm. Neural Networks learning allow us to select a features of each heart beat on the basis of Generalized Orthogonal Forward Regression (GOFR) algorithm and a library of 132 Gaussians with different standard deviations and different means, each beat is represented by five Gaussians with different amplitudes. The parameters of this system are determined and its performance is evaluated for the MIT-BIH arrhythmia database. For a database of 364 normal heartbeats and 1148 abnormal heartbeats, we apply the SVM algorithm with Radial Basis Function kernel. Our results demonstrate that the testing performance of the neural network and SVM diagnostic system is found to be very satisfactory with a recognition rate of 99.67%.

Russian is a language that is widely used by people in the world for various purposes because it is ranked 6th in the world as the language with the most speakers. This is one of the foundations of this study in addition to the high interest of the world population to use and learn the language. Russian language does not use alphabets but uses Cyrillic script, where the font is different from the letters in general so there are some obstacles to learn, understand and pronounce it. Capture to translate is one of the media that built to be a solution of this problem, built on image processing, feature extraction process with edges detection findcontours method and artificial intelligence using Support Vector Machine (SVM) classification algorithm with android mobile application interface that utilizes camera device as its input. In this study, Capture to translate using the Support Vector Machine (SVM) classification algorithm is able to produce a level of word classification accuracy of 93.8% in three syllable based on test that has been done, which is related to preprocessing, feature extraction and classification.

In order to improve classification capability, an advanced support vector machine (SVM) algorithm (L2-SVM) was studied in this chapter. Theoretical analysis of L1-SVM and L2-SVM illuminated the solving process of classification problem by using these two algorithms. The analysis also indicated that middle-scale datasets can be correctly classified by SVM algorithm. Both algorithms can be realized with the help of MATLAB. Simulation was conducted by using two spirals datasets. The results showed that both L1-SVM and L2-SVM had very good classification capability. The identification rates of these two algorithms were better than those of neural network algorithm and nearest neighbor algorithm. In some conditions, L1-SVM and L2-SVM were consistent. Compared with L1-SVM, L2-SVM had larger model parameter adaptable space. L2-SVM was proved to be an excellent algorithm for optimal parameter searching and classification both theoretically and experimentally, which will play an important role in recognition field.

Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.

Brain-computer interface (BCI) is a hardware and software communication system that allows controlling computers or external devices to utilize brain activity. BCI allows users to control computers or other devices using brain waves. The process of identifying patterns of brain activity depends on the classification algorithm. A portable BCI system classifiers need the ability to identify patterns obtained from electroencephalogram (EEG) channels. In this research, a reliable classification system was built using the Support Vector Machine (SVM) classification algorithm that are suitable for recognizing brain wave patterns. The SVM algorithm was implemented to identify five activity patterns from 4-channel EEG when performing different motor movements. The results show that SVM performance is reliable in recognizing and distinguishing those patterns based on the EEG’s gamma waves.

With the advancement of data processing technology, it is a significance task for machine learning to handle massive amounts of data. The traditional classification method is a supervised learning method, which requires a large number of labeled samples. But it is difficult to achieve. In this paper, a semi-supervised learning algorithm combining co-training with support vector machine (SVM) classification algorithm is introduced. Through an iterative learning procedure, the final new labeled data set can be determined based on unlabeled data set by training two SVM classifiers. Examples are provided for performance evaluation of the proposed method with detailed comparative studies to the traditional SVM and genetic algorithm SVM.

Brain–computer interface (BCI) technology provides a communication pathway using the human brain motor imaginary signal to develop applications like robotic hands and automated wheelchair, which are useful for the people who come with several motor disabilities. However, the signals which are acquired in a non-invasive approach come with various types of artifacts which badly effect on the accuracy of the prediction. For the above purposes, this paper proposes a model of human motor activity recognition using electroencephalogram (EEG) signal, in which three major time–frequency domain de-noising algorithms, i.e., Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Savitzky–Golay filter are adopted for de-noising the signals acquired in a non-invasive approach. In this paper, initially, the EEG signals are de-noised using de-noising algorithm. After that, important statistical features from selected EEG channels of the dataset are extracted. Finally, the Support Vector Machine (SVM) classification algorithm is used for classifying particular motor activities. Those three major time–frequency domain de-noising algorithms are compared based on five comparison metrics, i.e., mean squared error (MSE), mean absolute error (MAE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), cross-correlation and the classification accuracy using SVM classification. For evaluating the proposed model, an online benchmarked dataset with four classes of motor activities has been used. Among the four classes, the first two classes are used in this research. Those are ‘Left-hand movement’ as Class 1 and ‘Right-hand movement’ as Class 2. The experimental result, the DWT-based de-noising method, shows optimal performance.

A good and healthy life is the basic right of every human being. To achieve that, it is needed to make available better and cheaper medical facilities to everyone. There are a lot of disease a human life suffers with. Our focus in this paper is to study and analyze various classifiers algorithms to predict various liver diseases. We have compared the performance of Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Reduction Rule, Naive Bayes, and K-Nearest Neighbors classification algorithms. We have utilized an Irvine database of the University of California, namely the Indian Liver Patient Dataset (ILPD). The various characteristics of a patient such as gender, age, total bilirubins, direct bilirubins, albumins, globulin ratios, alkphos, sgot and sgpt were evaluated. The implementation of classification algorithms is done on the rapid miner tool along with Liver Patient dataset and comparison is done on the parameters such as training time, scoring time, accuracy, precision. The result of other parameters like classification error, recall is also compared on liver patient diseases dataset. We also proposed the use of a radial kernel function as a support vector machine, and it has observed that true prediction of class1 is about 81.30% having class recall is 96.15% and class2 is about 82.22% having class recall is 44.31%.

In this research, a new method for spectral-spatial classification of hyperspectral images based on hierarchical segmentation algorithm is introduced. Among the various spectral-spatial classification algorithms, the marker-based hierarchical segmentation algorithm has so far achieved the best results in combination with the support vector machine (SVM) classification algorithm. In the proposed method adopted in this research, the dimensions of the hyperspectral image were reduced via the minimum noise fraction (MNF) algorithm. Then, five spatial/texture features of wavelet transform, Gabor transform, mean, entropy, and contrast were extracted from the obtained bands. Later on, the multi-layer perceptron (MLP) neural network and SVM classification algorithms were applied to the obtained spectral and texture features, and their results were combined. The resulting map was then used to select the markers and to combine them with the marker-based hierarchical segmentation algorithm using the majority voting rule. The proposed method was applied to three hyperspectral images, Pavia, Telops, and Washington DC Mall. The results of the experiments demonstrate the superiority of the proposed method over the initial marker-based hierarchical algorithm. Quantitatively, it was better by 8, 12, and 9% for the Pavia, Telops, and Washington DC Mall datasets regarding the Kappa coefficient parameter, respectively.