Privacy‐preserving multisource transfer learning in intrusion detection system

Abstract

The increasing scale of the network and the demand for data privacy‐preserving have brought several challenges for existing intrusion detection schemes, which presents three issues: large computational overhead, long training period, and different feature distribution which leads low model performance. The emergence of transfer learning has solved the above problems. However, the existing transfer learning‐based schemes can only operate in plaintext when different domains and clouds are untrusted entities, the privacy during data processing cannot be preserved. Therefore, this paper designs a privacy‐preserving multi‐source transfer learning intrusion detection system (IDS). Firstly, we used the Paillier homomorphic to encrypt models which trained from different source domains and uploaded to the cloud. Then, based on privacy‐preserving scheme, we first proposed a multisource transfer learning IDS based on encrypted XGBoost (E‐XGBoost). The experimental results show that the proposed scheme can successfully transfer the encryption models from multiple source domains to the target domain, and the accuracy rate can reach 93.01% in ciphertext, with no significant decrease in detection performance compared with works in plaintext. The training time of the model is significantly reduced from the traditional hour‐level to the minute‐level.

An Efficient Intrusion Detection Method Based on Federated Transfer Learning and an Extreme Learning Machine with Privacy Preservation

“…e benchmark algorithms selected in the experiment are ELM [32], TrAdaBoost [13,14,39], and SVM, among which SVM is implemented using the LIBSVM [37] toolkit. e 10-fold cross-validation method is a standard method for evaluating machine learning algorithms, and this study uses the intrusion detection model proposed by its evaluation.…”

“…e experimental results show that compared with the existing methods, the detection accuracy of our model scheme is improved by at least 23%. Xu et al [13] designed a multisource transfer learning intrusion detection system for privacy protection. First, the system uses Paillier homomorphism to encrypt the models that are trained from different source domains and uploaded to the cloud and then proposes a multisource transfer learning intrusion detection system based on encryption XGBoost (e-XGBoost) based on a privacy protection scheme.…”

“…e algorithms proposed in [10,11] do not use transfer learning and cannot use relevant data to help target learning tasks create an intrusion detection classifier. Moreover, references [12,13] are intrusion detection models based on migration learning, but the application scenario of the former is a vehicle network, and they cannot solve the problem of data islands. Although the literature [14] has the advantages of federated learning and transfer learning, it uses convolutional neural networks and reinforcement learning.…”

Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

“…23,24 Network administrators have applied intrusion detection systems (IDS) to supply essential network security. 17,25 An intrusion is a kind of attack on information assets in which the instigator attempts to gain entry into a system or disturb its usual operations. 26 Intrusion detection is the process of monitoring networks for unauthorized access, activity, or file modification.…”

Bipolar fully recurrent deep structured neural learning based attack detection for securing industrial sensor networks

“…A privacy‐preserving multisource transfer learning intrusion detection scheme was introduced in Reference 18 for security analysis. However, the minimization of data loss rate was not sufficient.…”