Cloud Intrusion Detection Model Based on Deep Belief Network and Grasshopper Optimization

Parganiha, Vivek; Shukla, Soorya Prakash; Sharma, Lokesh Kumar

doi:10.4018/ijaci.293123

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…The outcomes of the study imply that extreme learning‐model overtakes the other existing techniques. Similar to this study, the multilevel hybridization intrusion‐detection design for enhancing the detection efficiency of all the unknown threats and the known attacks 35 . This hybrid model utilizes the ELM and SVM‐classifier.…”

Section: Related Workmentioning

confidence: 94%

“…Similar to this study, the multilevel hybridization intrusion-detection design for enhancing the detection efficiency of all the unknown threats and the known attacks. 35 This hybrid model utilizes the ELM and SVM-classifier. The higher training quality data set has been constructed by using the K-means algorithm for the improvisation of the classifier's performance.…”

Section: Intrusion-detection-model Using Svm-classification Techniquementioning

confidence: 99%

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An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

Sharma

Shandilya

2022

Concurrency and Computation

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The necessity of cyber-security has obtained immense importance in day-to-day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber-physical system) model using a dual mutation-based genetic algorithm, with feature classification through Ada-Boost and SVM classifier is proposed in this paper. Dual-mutation based genetic-algorithm overcomes the issues of conventional techniques including convergence issues and local fine-tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating-point representation, every chromosome vector has been coded as a floating-point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual-mutation algorithm that handles local fine-tuned features.The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov-resampling process. Followed by this, a hybrid approach of ESVM (enhanced support-vector machine) algorithm with Ada-Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy-factor, F1-score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1-score (99%) rates, and less execution time (15.33 s).

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Section: Related Workmentioning

confidence: 94%

Section: Intrusion-detection-model Using Svm-classification Techniquementioning

confidence: 99%

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

Sharma

Shandilya

2022

Concurrency and Computation

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“…CNN is the second kind of discriminative deep networks, along with several convolutional and gathering layers stacked in an array to produce a multi-layer neural network [65], [67]. The max pooling layer should come after each convolutional layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

2023

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The security of the internet is seriously threatened by a distributed denial of service (DDoS) attacks. The purpose of a DDoS assault is to disrupt service and prevent legitimate users from using it by flooding the central server with a large number of messages or requests that will cause it to reach its capacity and shut down. Because it is carried out by numerous bots that are managed (infected) by a single botmaster using a fake IP address, this assault is dangerous because it does not involve a lot of work or special tools. For the purpose of identifying and analyzing DDoS attacks, this paper will discuss various machine learning (ML) and deep learning (DL) techniques. Additionally, this study analyses and comparatives the significant distinctions between ML and DL techniques to aid in determining when one of these techniques should be used.

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“…The image encryption-then-compression (ETC) technique was recommended. The encrypted and compressed images it produces are strongly secured by this algorithm, which operates in the prediction error domain [10], [11].This work suggests a paradigm shift in which a single plain image is divided into a number of component pictures These are then separately encrypted to form a number of component cypher pictures. The original picture is created by combining the decrypted component cipher-images [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient and Secure Optimized Homomorphic Encryption (OHE) Securing Sensitive Images Data in the Age of Cloud Computing

Dewangan,

Soni,

Misal

2023

Preprint

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The goal of the project is to create a cutting-edge picture encryption technique that can be used to significantly boost the security of encrypted photographs. Since visible electromagnetic spectrum pictures are a difficulty, we provide an ideal homomorphic image encryption method to address the issue. Therefore, in our encryption phase, the numerical intensity value of a pixel from each channel is represented as the average of smaller pixel intensity sub-values. Therefore, each R, G, and B-channel picture is composed of a number of separate photographs. using an encryption key and aOptimized Homomorphic Encryption method, intensity of every pixel sub-value in every component picture is individually encrypted to provide a distributed picture encryption solution. Before being moved or stored, each encrypted component picture may be compressed. Before beginning the decryption process, each encrypted component picture is first, if required, decompressed. The homomorphic characteristic of the encryption technique is then used to encrypt the total of each encrypted component image's individually encrypted pixel intensity sub-values. To optimise the embedding rate of additional data during the encryption phase, six surrounding pixels are combined as a set, creating a novel method. Then, each channel image's original pixel intensities are restored, and extraneous data is removed from the total data. The intensity value for each channel's pixel in this kind of RGB image encryption and decryption, which is likewise created and simulated using software, is represented as the sum of merely two sub-values. The produced cypher pictures are subjected to several security evaluations and tests. The outcomes of these experiments showed the stability and resilience of the homomorphic picture encryption method we proposed, which also improved the security of the connected encrypted photos. Photos that utilise our homomorphic image encryption technique and are incredibly safe.

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Cloud Intrusion Detection Model Based on Deep Belief Network and Grasshopper Optimization

Cited by 4 publications

References 39 publications

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

DDoS attacks detection using machine learning and deep learning techniques: analysis and comparison

Efficient and Secure Optimized Homomorphic Encryption (OHE) Securing Sensitive Images Data in the Age of Cloud Computing

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