Classical and Deep Learning Classifiers for Anomaly Detection

Raza, Manahil; Qayyum, Usman

doi:10.1109/ibcast.2019.8667245

Cited by 12 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, to pump and dump mani-pulations, the main goal is to artificially drive the stock price up to sell for a higher profit. Deep learning approaches to this problem were introduced in [1] [7] to find an innovative solution to detect stock market manipulations. A deep general adversarial network (GAN) approach was first introduced in [1] and it showed a promise in detecting pump and dump manipulations.…”

Section: Stock Market Fraudmentioning

confidence: 99%

“…However, the solution was limited only to detect this one type of manipulation and had a drop-in accuracy when executed on unseen data. A 10-layer deep learning approach called Variational Auto Encoder was implemented in [7], highlighting the ability to detect anomalies without needed a labeled dataset. This solution did not perform as well as some supervised approaches as it has a high False Positive Rate.…”

Section: Stock Market Fraudmentioning

confidence: 99%

See 1 more Smart Citation

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

View full text Add to dashboard Cite

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and real-time data transactions, the stock market has increased vulnerability to attacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipulations are not available for training. This paper discovers a competitive alternative to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

show abstract

Section: Stock Market Fraudmentioning

confidence: 99%

Section: Stock Market Fraudmentioning

confidence: 99%

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

View full text Add to dashboard Cite

show abstract

“…Activation functions are used to bound the outputs of this layer and to provide a given behavior, e.g., non-linear [15]. Equation 1shows the general formulation of this kind of layer: (1) in which l refers to the current layer, i and j are the indexes of the elements of the previous and current layers, respectively, is a set of input maps, k is the weight matrix of the i-th convolutional kernel of the l -th layer applied to the j-th input feature map. b is the bias.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

“…Anomaly detection has gained importance in many applications, and it can be applied to multiple domains, e.g., fraud detection [1], network security [2], among others. It consists of identifying non-conforming patterns (anomalies) regarding an expected behavior [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Monteiro¹,

Filho²

2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

Anomaly detection is an import field of study, which has many applications, e.g., fraud and disease detection. It consists of identifying non-conforming patterns regarding an expected behavior. Despite the improvements provided by deep learning techniques in several areas, their use for anomaly detection is not widespread. The main reason is the difficulty to learn discriminative models when all the information available regards one class, or the classes are highly unbalanced. We propose a new deep learning-based solution for the anomaly detection problem. It consists of a hybrid system, composed of a feature extractor and a one-class classifier. The feature extractor is a convolutional neural network, trained as a regressor to learn a predefined distribution. The classifier is the one-class support vector machine, which performs the anomaly detection by using the outputs provided by the feature extractor. We used a gearbox failure diagnosis data set to assess the performance of our proposal. We also compared our anomaly detection system with other deep learning-based techniques commonly found in the literature. Our proposal presented an average accuracy close to 0.95, outperforming techniques based on the reconstruction error and hybrid models.

show abstract

Credit Card Fraud Detection: A Systematic Review

Priscilla

Prabha

2020

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

Classical and Deep Learning Classifiers for Anomaly Detection

Cited by 12 publications

References 2 publications

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Credit Card Fraud Detection: A Systematic Review

Contact Info

Product

Resources

About