RAE-SVM based Webpage Tamper-Resistant Detection Algorithm
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Cybersecurity has become a key component of national strategy in recent years. Traditional cybersecuritytechnology such as network traffic-based intrusion detection and threatening intelligence sensing are designedto focus on the traffic features of network, which are no doubt effective defense technologies. However, thesemethods required decent amount of domain knowledge and massive training data, which brought a significantbarrier for cybersecurity research. In this work, we propose a novel residual autoencoder and support vectormachine combined approach (RAE-SVM) for webpage tamper-resistant detection using high-level webpageimage features. This method, inspired by the Chinese proverb “mend the fold after the sheep have been stolen.”The web crawler technology is used for website screenshot within limited domain names, and input them intoautoencoder architecture and SVM for feature extraction and invaded webpage detection. This method combinesthe advantages of deep residual network, convolutional autoencoder and SVM, and the interdisciplinaryintersection between cybersecurity and high-level image features. The experimental results demonstrate thatthe proposed method achieves an accuracy of 95%, significantly higher than other models, which proves thevalidity of the proposed method.
Abstract
Cybersecurity has become a key component of national strategy in recent years. Traditional cybersecuritytechnology such as network traffic-based intrusion detection and threatening intelligence sensing are designedto focus on the traffic features of network, which are no doubt effective defense technologies. However, thesemethods required decent amount of domain knowledge and massive training data, which brought a significantbarrier for cybersecurity research. In this work, we propose a novel residual autoencoder and support vectormachine combined approach (RAE-SVM) for webpage tamper-resistant detection using high-level webpageimage features. This method, inspired by the Chinese proverb “mend the fold after the sheep have been stolen.”The web crawler technology is used for website screenshot within limited domain names, and input them intoautoencoder architecture and SVM for feature extraction and invaded webpage detection. This method combinesthe advantages of deep residual network, convolutional autoencoder and SVM, and the interdisciplinaryintersection between cybersecurity and high-level image features. The experimental results demonstrate thatthe proposed method achieves an accuracy of 95%, significantly higher than other models, which proves thevalidity of the proposed method.