Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic

Ali, Wasim Ahmed; Manasa, Koppula; Aljunid, Mohammed Fadhel; Bendechache, Malika; Sandhya, P.

doi:10.18080/jtde.v8n4.307

Cited by 26 publications

(16 citation statements)

References 63 publications

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“…We measured our cryptography scheme’s performance by selecting some basic parameters to assess the algorithm. Visual inspection is one of the main parameters for assessing the encrypted images [ 51 , 52 , 53 ]. The characteristic diffusion survey is another parameter [ 54 , 55 ] determined for judging the randomization algorithm.…”

Section: Proposed Algorithmmentioning

confidence: 99%

A New Algorithm for Digital Image Encryption Based on Chaos Theory

Pourasad

Ranjbarzadeh

Mardani

2021

Entropy

128

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In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme’s effectiveness and show that this technique is a suitable choice for actual image encryption.

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Section: Proposed Algorithmmentioning

confidence: 99%

A New Algorithm for Digital Image Encryption Based on Chaos Theory

Pourasad

Ranjbarzadeh

Mardani

2021

Entropy

128

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“…Automated recognition of patterns in data by computers based on knowledge already obtained is called pattern recognition. It has applications in image analysis, information retrieval, signal processing, bioinformatics, data compression, statistical data analysis, computer graphics, and machine learning [27,31,33,[41][42][43][44].…”

Section: Local Directional Numbermentioning

confidence: 99%

Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Ranjbarzadeh

Ghoushchi

Bendechache

et al. 2021

BioMed Research International

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The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

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“…e set of type two fuzzy sets, the rules of algebraic operations, and the properties of the affiliation function are discussed [10]. e representation of type-two fuzzy numbers is newly defined in reference [11], by which theorems for the intersection, merge, and complement operations of type-two fuzzy numbers are derived without using the extension principle [11]. e intersection, merge, and complement operations for interval type-two fuzzy sets are defined, while the footprint uncertainty is proposed, and it is pointed out that the footprint uncertainty is the plane region constituted by the set of principal affiliation degrees [12].…”

Section: Status Of Researchmentioning

confidence: 99%

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

Tang

2021

Journal of Mathematics

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In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.

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Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic

Cited by 26 publications

References 63 publications

A New Algorithm for Digital Image Encryption Based on Chaos Theory

A New Algorithm for Digital Image Encryption Based on Chaos Theory

Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

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