Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using c...Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using conventional security techniques challenging.To address this issue,we propose a Lebesgue-2 regularized multilayer perceptron(L2R-MLP)algorithm for detecting DNS tunneling attacks.The DNS dataset was carefully curated from a publicly available repository,and relevant features,such as packet size and count,were selected using the recusive feature elimination technique.L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training,effectively countering the risk of overfitting.When evaluated against a benchmark MLP model,L2R-MLP demonstrated superior performance with 99.46%accuracy,97.00%precision,97.00%F1-score,99.95%recall,and an AUC of 89.00%.In comparison,the benchmark MLP achieved 92.53%accuracy,96.00%precision,97.00%F1-score,99.95%recall,and an AUC of 87.00%.This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.展开更多
In this paper, we built upon the estimating primaries by sparse inversion （EPSI） method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and Ll-norm r...In this paper, we built upon the estimating primaries by sparse inversion （EPSI） method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and Ll-norm regularization, and use alternating optimization to directly estimate the primary reflection coefficients and source wavelet. The 3D curvelet transform is used as a sparseness constraint when inverting the primary reflection coefficients, which results in avoiding the prediction subtraction process in the surface-related multiples elimination （SRME） method. The proposed method not only reduces the damage to the effective waves but also improves the elimination of multiples. It is also a wave equation- based method for elimination of surface multiple reflections, which effectively removes surface multiples under complex submarine conditions.展开更多
文摘Domain name system(DNS)tunneling attacks can bypass firewalls,which typically“trust”DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones,thereby making detection using conventional security techniques challenging.To address this issue,we propose a Lebesgue-2 regularized multilayer perceptron(L2R-MLP)algorithm for detecting DNS tunneling attacks.The DNS dataset was carefully curated from a publicly available repository,and relevant features,such as packet size and count,were selected using the recusive feature elimination technique.L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training,effectively countering the risk of overfitting.When evaluated against a benchmark MLP model,L2R-MLP demonstrated superior performance with 99.46%accuracy,97.00%precision,97.00%F1-score,99.95%recall,and an AUC of 89.00%.In comparison,the benchmark MLP achieved 92.53%accuracy,96.00%precision,97.00%F1-score,99.95%recall,and an AUC of 87.00%.This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.
基金supported by the National Science and Technology Major Project (No.2011ZX05023-005-008)
文摘In this paper, we built upon the estimating primaries by sparse inversion （EPSI） method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and Ll-norm regularization, and use alternating optimization to directly estimate the primary reflection coefficients and source wavelet. The 3D curvelet transform is used as a sparseness constraint when inverting the primary reflection coefficients, which results in avoiding the prediction subtraction process in the surface-related multiples elimination （SRME） method. The proposed method not only reduces the damage to the effective waves but also improves the elimination of multiples. It is also a wave equation- based method for elimination of surface multiple reflections, which effectively removes surface multiples under complex submarine conditions.
