Design of optimal metaheuristics based pixel selection with homomorphic encryption technique for video steganography

Sharath, M N; Rajesh, T.; Patil, Mallanagouda

doi:10.1007/s41870-022-01005-9

Cited by 13 publications

(1 citation statement)

References 24 publications

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“…Physical and digital attacks may be directed toward CPS. While cyber-physical links are broken by cyber-attacks, which also weaken CPS, physical attacks result in the immediate suspension of dynamic response [34]. The goal of the study CPS, or cyber-physical systems, integrate components from the digital and physical worlds to enhance functionality.…”

Section: Proposed Systemmentioning

confidence: 99%

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

Swathi,

Narsimha

2023

IJRITCC

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The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.

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Section: Proposed Systemmentioning

confidence: 99%