Secured image steganography based on Catalan transform

Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

Section: Resultsmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

“…Mukhopadhyay et al 39 utilized Catalan transform (CT) for steganography in the transform domain. The steganography image is formed by consecutive embedding operations that take place over the entire image.…”

Section: Review Of Related Workmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

“…The rapid development of the Internet has brought many benefits to people, and at every time an instant huge amount of multimedia information is created and transmitted on the Internet 1 . However, the hackers' attack on computer systems on the Internet challenges the cyberspace security 2,3 . So how to prevent the hackers' network intrusion is one of the most important issues in cyberspace security 4‐6 .…”

Section: Introductionmentioning

confidence: 99%

“…1 However, the hackers' attack on computer systems on the Internet challenges the cyberspace security. 2,3 So how to prevent the hackers' network intrusion is one of the most important issues in cyberspace security. [4][5][6] To solve this problem, intrusion detection technique has been developed and attracts continuous attention.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection: A model based on the improved vision transformer

Yang

Gao

et al. 2022

We propose an intrusion detection model based on an improved vision transformer (ViT). More specifically, the model uses an attention mechanism to process data, which overcomes the flaw of the short-term memory in recurrent neural network (RNN) and the difficulty of learning remote dependency in convolutional neural network. It supports parallelization and has a faster computing speed than RNN. A sliding window mechanism is presented to improve the capability of modeling local features for ViT. The hierarchical focal loss function is used to improve the classification effect, and solve the issue of the data imbalance. The public intrusion detection dataset NSL-KDD is used for experimental simulations. By experimental simulations, the accuracy is up to 99.68%, the false-positive rate is 0.22%, and the recall rate is 99.57%, which show that the improved ViT has better accuracy, false positive rate, and recall rate than existing intrusion detection models.