Probabilistic framework of visual anomaly detection for unbalanced data

Wang, Yongxiong; Li, Xuan; Ding, Xueming

doi:10.1016/j.neucom.2016.03.038

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…The last category is semi-supervised learning. This is a type of learning whereby it requires a mixture of labeled and unlabeled data [11]- [14]. This approach inherits the advantages and disadvantages of both methods which will be discussed in the later part of the paper.…”

Section: Related Workmentioning

confidence: 99%

A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network

Tay

Connie

Ong

et al. 2018

Lecture Notes in Electrical Engineering

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A behavior is considered abnormal when it is seen as unusual under certain contexts. The definition for abnormal behavior varies depending on situations. For example, people running in a field is considered normal but is deemed abnormal if it takes place in a mall. Similarly, loitering in the alleys, fighting or pushing each other in public areas are considered abnormal under specific circumstances. Abnormal behavior detection is crucial due to the increasing crime rate in the society. If an abnormal behavior can be detected earlier, tragedies can be avoided. In recent years, deep learning has been widely applied in the computer vision field and has acquired great success for human detection. In particular, Convolutional Neural Network (CNN) has shown to have achieved state-ofthe-art performance in human detection. In this paper, a CNN-based abnormal behavior detection method is presented. The proposed approach automatically learns the most discriminative characteristics pertaining to human behavior from a large pool of videos containing normal and abnormal behaviors. Since the interpretation for abnormal behavior varies across contexts, extensive experiments have been carried out to assess various conditions and scopes including crowd and single person behavior detection and recognition. The proposed method represents an end-to-end solution to deal with abnormal behavior under different conditions including variations in background, number of subjects (individual, two persons or crowd), and a range of diverse unusual human activities. Experiments on five benchmark datasets validate the performance of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network

Tay

Connie

Ong

et al. 2018

Lecture Notes in Electrical Engineering

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show abstract

“…Now a day's big data is utilized to gather and process huge data having capacity to process and receive data rapidly and efficiently with less computational time [32]. Data mining and data warehousing are the most advanced techniques in data analysis and also used to determine the type of mining and recovery [33].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System on Big data using Deep Learning Techniques

Dahiya*,

Srivastva

2020

IJITEE

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Big data is the huge amount of data with different types of V’s: Velocity, Variety as well as Volume. It can be semi-structured, unstructured or structured, due to which it is not easy to analyze the data. To extract the hidden knowledge and to detect the attacks on large amount of data new architecture, techniques, algorithms, and analytics are required. Using traditional techniques to detect attacks is very difficult. In this paper, the detailed review has been done on intrusion detection on various fields using deep learning and gives an idea of applications of deep learning. The number of attacks has been increased in computer networks. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. Based on review, it is found that some studies have been done in this field, but a deep and exhaustive work has still not been done. Many researchers proposed an IDS using deep learning for unforeseen and unpredictable attacks but not for Big Data. The proposed work is based on Deep learning based intrusion detection System for big datasets named hybrid-DeepResNet-RNN run till 1,000 epochs with learning rate varying range [0.01-0.5] and three ensemble techniques, Random Forest, Decision tree regression and Gradient Boosting Tree (GBT). It is used to develop the hybrid, secure, scalable NIDS which is based on deep learning and big data techniques. The proposed classifiers produce a more reliable classification than a single classifier. The experimental results are in terms of detection rate (98.86%), false positive rate (1.110%), accuracy (99.34%) and F-Measure (97.90%). The results illuminate the better performance than existing anomaly detection techniques in the big data environment.

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“…For the past few years, the applications of big data were developed and a number of researchers from various stream realized the advantage of extracting knowledge in this type of issue. Now a day"s big data is used to gather and process a large number of data having capacity to process and receive data rapidly and efficiently with less computational time [5]. The big data also made its entry in scientific, business, engineering streams and in a common network to observe the operator"s action and location to provide an enhanced plan, junk and fraud recognition [6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Dahiya,

Srivastva

2019

IJEAT

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Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

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Probabilistic framework of visual anomaly detection for unbalanced data

Cited by 17 publications

References 21 publications

A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network

A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network

Intrusion Detection System on Big data using Deep Learning Techniques

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

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