| Related citation: | Meesala Sravani,B Kiran Kumar,M Rekha Sundari,D Tejaswi.FMHADP: Design of an Efficient Pre-Forensic Layer for Mitigating Hybrid Attacks via Deep Learning Pattern Analysis[J].Journal of Harbin Institute Of Technology(New Series),2024,31(5):55-67.DOI:10.11916/j.issn.1005-9113.2023098. |
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| Author Name | Affiliation | | Meesala Sravani | Department of Computer Science and Engineering, GMR Institute of Technology, Rajam 532127, Andhra Pradesh, India | | B Kiran Kumar | Information Technology, Anil Neerukonda Institute of Technology & ScienceAutonomous, Sangivalasa 531162, Bheemunipatnam, India | | M Rekha Sundari | Information Technology, Anil Neerukonda Institute of Technology & ScienceAutonomous, Sangivalasa 531162, Bheemunipatnam, India | | D Tejaswi | Information Technology, Anil Neerukonda Institute of Technology & ScienceAutonomous, Sangivalasa 531162, Bheemunipatnam, India |
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| Abstract: |
| Network attack detection and mitigation require packet collection, pre-processing, feature analysis, classification, and post-processing. Models for these tasks sometimes become complex or inefficient when applied to real-time data samples. To mitigate hybrid assaults, this study designs an efficient forensic layer employing deep learning pattern analysis and multidomain feature extraction. In this paper, we provide a novel multidomain feature extraction method using Fourier, Z, Laplace, Discrete Cosine Transform (DCT), 1D Haar Wavelet, Gabor, and Convolutional Operations. Evolutionary method dragon fly optimisation reduces feature dimensionality and improves feature selection accuracy. The selected features are fed into VGGNet and GoogLeNet models using binary cascaded neural networks to analyse network traffic patterns, detect anomalies, and warn network administrators. The suggested model tackles the inadequacies of existing approaches to hybrid threats, which are growing more common and challenge conventional security measures. Our model integrates multidomain feature extraction, deep learning pattern analysis, and the forensic layer to improve intrusion detection and prevention systems. In diverse attack scenarios, our technique has 3.5% higher accuracy, 4.3% higher precision, 8.5% higher recall, and 2.9% lower delay than previous models. |
| Key words: digital replay attack perceptual hashing content authentication content identification Differential Luminance Block Means (DLBM) normalization shifts |
| DOI：10.11916/j.issn.1005-9113.2023098 |
| Clc Number:TP309 |
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