FMHADP: Design of an Efficient Pre-Forensic Layer for Mitigating Hybrid Attacks via Deep Learning Pattern Analysis

作     者：Meesala Sravani B Kiran Kumar M Rekha Sundari D Tejaswi 

作者机构：Department of Computer Science and Engineering， GMR Institute of Technology Information Technology， Anil Neerukonda Institute of Technology & Science(Autonomous) 

出 版 物：《Journal of Harbin Institute of Technology(New series)》 (哈尔滨工业大学学报(英文版))

年 卷 期：2024年

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

摘      要：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.