How to introduce expert feedback in one-class support vector machines for anomaly detection?

Lesouple, Julien; Baudoin, Cédric; Spigai, Marc; Tourneret, Jean-Yves

doi:10.1016/j.sigpro.2021.108197

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…There are three main sections in SVM, namely Supervised, Classified and Hyperplane with the largest margin. How the Support Vector Machine [25] [26] [27] works. Support vectors are two closely spaced data that come from different classes or groups, these two data will be used as support vectors.…”

Section: E Support Vector Machinementioning

confidence: 99%

Android APK Identification using Non Neural Network and Neural Network Classifier

Hindarto

Santoso²

2021

J-Cosine

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Currently adoption of mobile phones and mobile applications based on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of 91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of 99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

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Section: E Support Vector Machinementioning

confidence: 99%

Android APK Identification using Non Neural Network and Neural Network Classifier

Hindarto

Santoso²

2021

J-Cosine

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“…Through the simulation test, S3VMAD is compatible with both v-SVM and one-class SVM. Lsouple et al 8 defined a boundary between normal data and anomalies and made an assumption that the majority of unlabeled data are normal. Through the assumption, a one-class SVMs is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…According to the application type, there are two main directions of technical diagnostics: monitoring and diagnostics of rotating machinery, [1][2][3][4] and structural health monitoring (SHM), which is defined as a process of implementing a damage detection and characterisation strategy for various engineering structures. [5][6][7][8] According to the used technique, diagnostic methods can be divided into: destructive technique when maximum load or fatigue test of samples are performed 9,10 ; and non-destructive technique when the state of the structure is evaluated without causing damage to it. 11,12 Due to this advantage, the nondestructive technique has become the most common technique for technical diagnostics nowadays.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, for diagnostics are used: Visual diagnostics, Ultrasonic testing, Thermography; X-Ray testing, Eddy current; Electromagnetic Testing, Acoustic emission measurements, measurements and analysis of vibrations. 8,[13][14][15][16][17] Comparing all mentioned methods can be stated that vibration analysis is one of the most universal and commonly used methods of technical diagnostics since it is suitable for diagnostics of…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised dam anomaly detection with spatial–temporal variational autoencoder

Shu

Bao

Zhou

et al. 2022

Structural Health Monitoring

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The anomaly detection and health monitoring of dams have attracted increasing attention. To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal variational autoencoder is proposed. The proposed model is based on the sequential variational autoencoder, and its backbone is fulfilled by the recurrent neural network and graph convolutional network to capture the temporal and spatial features in both the generative and inference models. To obtain a normal pattern, we made an assumption that the normal values should be temporally smooth and spatially similar. Then, the smoothness and similarity-inducing operations are used in the framework of the proposed model. Through the addition of smoothness and similarity losses in sequential variational autoencoder, the proposed model can obtain a temporally smooth and spatially similar pattern. For verification, an arch dam is taken as an example. Through comparison among six baseline models, the proposed model detects the temporal and spatial anomalies accurately and stably.

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“…The anomaly detection literature is extensive and includes many different techniques and methods. One of the groups of methods is based on support vector machine, where a hyperplane separating classified data is sought [7], [8]. The second frequently used method is the k-nearest neighbor approach [9].…”

Section: Introductionmentioning

confidence: 99%

Isolation Forest Based on Minimal Spanning Tree

2022

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Detecting anomalies in data sets has been one of the most studied issues in modern data analysis. Therefore, there is a plethora of applications in a very wide range of fields of science and technology. One of the most frequently used anomaly detection methods is Isolation Forest. In this study, we propose a novel efficient approach based on this technique. In order to improve the classification accuracy of the base method, we make two-fold modifications. First, we propose a change of the technique of building isolation trees to merge nodes by minimal spanning tree algorithm. The second change is based on a modification of the function assessing the anomaly of the analyzed element (data record) to sum of factors correlated with tree height and nearest point distance. In the series of comprehensive computational experiments, the proposed method has proven to produce better results than other compared state-of-the-art methods available in popular data mining programming libraries. It is worth stressing that the final version of the new method in comparison to original Isolation Forest is 2.9% better in terms of AUC measure.

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How to introduce expert feedback in one-class support vector machines for anomaly detection?

Cited by 16 publications

References 27 publications

Android APK Identification using Non Neural Network and Neural Network Classifier

Android APK Identification using Non Neural Network and Neural Network Classifier

Unsupervised dam anomaly detection with spatial–temporal variational autoencoder

Isolation Forest Based on Minimal Spanning Tree

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