A deep learning approach for anomaly detection and prediction in power consumption data

Chahla, C.; Snoussi, Hichem; Merghem, Leïla; Esseghir, Moez

doi:10.1007/s12053-020-09884-2

Cited by 32 publications

(11 citation statements)

References 12 publications

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“…In order to check the viability and the effectiveness of the proposed cumulative anomaly recognition method for security database based on discrete Markov chain, it is compared with three anomaly recognition methods in reference [3][4][5]. In this paper, the event log generated when the DARPA98 data set is replayed on the NT system is used as the experimental data for simulation experiment.…”

Section: Resultsmentioning

confidence: 99%

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Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Yang

Najafi

2022

Security and Communication Networks

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There exists an enormous volume of data in the database system, which is accountable for the storage of data and organization of data. The intruders can breach the security system of database and steal the important information. Therefore, it is of great significance to carry out the cumulative anomaly identification of the security database. In view of the shortcomings of traditional anomaly detection methods in detection performance and poor effect of anomaly recognition, this paper proposes a cumulative anomaly recognition method based on discrete Markov chain for security database. First, the sniffer is used to read the user access behaviour data, and then, it is processed, that is, standardized processing. Then, the segmentation method is used to extract the user behaviour features, and the normal feature data and abnormal feature data are obtained. Finally, the state sequence generated by the discrete Markov chain is used to calculate the state probability, which is used to evaluate the abnormal process behaviour. The proposed method in this paper is based on the Markov chain and can be used for better anomaly recognition. The results are obtained in terms of sensitivity score, precision score, and F1-score. The results are also compared with the results obtained by using some of the state-of-the-art traditional techniques. The comparison clearly indicated that the proposed method is more effective as compared to the tradition methods. The proposed method has the highest F1-score of 0.8586, and then the traditional methods have F1-scores of 0.7233, 0.8236, and 0.7562 for methods 1, 2, and 3, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Stealing of relevant information, duplication of records, denial of service, and the inability to get information on time are all issues that public entities face [4]. Cyber attackers are seeking a way in through a system breach and have a variety of tools for gaining access to an organization's systems or databases [5].…”

Section: Introductionmentioning

confidence: 99%

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Yang

Najafi

2022

Security and Communication Networks

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“…This stage often introduces blurriness in the processed images. [38]. Convolution shows the important relationship between pixels by learning features using small squares of input data.…”

Section: Methodsmentioning

confidence: 99%

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Masood¹,

Al-Sakhnini²,

Nawaz³

et al. 2023

Computers, Materials &Amp; Continua

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Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 5418 CMC, 2023, vol.74, no.3 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-ofthe-art techniques.

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“…e security performance requirements of the cloud data platform application in the communication process authentication were analyzed. e article designs the platform access framework based on fingerprint authentication by realizing the security of the multidomain access identity authentication in the cloud data platform [19,20]. And, for the fingerprint feature extraction of the platform fingerprint authentication module, it lays the foundation for connecting the cloud data platform and interdomain access, so as to realize the design of anonymous account encryption and decryption.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Database and Travel Smart Platform Design Based on LSTM Algorithm

Chen

2022

Mobile Information Systems

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Information technology has played a key role in the development of the tourism planning service industry and has now become an important foundation for the survival and rapid development of the industry. In this context, due to the fast updating and popularization of information technology, it has greatly promoted the development of the tourism industry. In order to meet the current public demand for tourism information, this paper integrates cloud computing, VR technology, and big data analysis technology to build a smart platform for intelligent perception tourism information services. The system can obtain tourism information through mobile Internet terminals. Among them, the database of the smart tourism planning platform is the most important module. Aiming at the many difficulties in adapting data encryption in cloud storage applications, this article designs an adaptive CloudCrypt data encryption system based on cloud computing technology and proposes dynamic JavaScript dynamic analysis and automatic identification of data technology, through adaptive different cloud applications to obtain data encryption protection. CloudCrypt is suitable for typical cloud applications, such as mail and storage. The entry cost of the system is extremely low; it can fully guarantee the security of the tourism information platform database and can integrate wireless sensor networks into the tourism information platform. The network system composed of sensor nodes activates detection, calculation, and communication modules through wireless communication and has the advantages of low cost, low power consumption, and fast networking speed. In this paper, through the construction of integrated wireless sensor technology and cloud computing database, it is applied to the construction of a tourism smart platform, thereby promoting the development of smart travel technology.

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A deep learning approach for anomaly detection and prediction in power consumption data

Cited by 32 publications

References 12 publications

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Cloud Computing Database and Travel Smart Platform Design Based on LSTM Algorithm

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