Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
The decryption participant＇s private key share for decryption is delegated by key generation center in the threshold IBE scheme.However,a key generation center which is absolutely trustworthy does not exist.So the au...The decryption participant＇s private key share for decryption is delegated by key generation center in the threshold IBE scheme.However,a key generation center which is absolutely trustworthy does not exist.So the author presents a certificateless threshold public key encryption scheme.Collaborating with an administrator,the decryption participant generates his whole private key share for decryption in the scheme.The administrator does not know the decryption participant＇s private key share for decryption.Making use of q-SDH assumption,the author constructs a certificateless threshold public key encryption scheme.The security of the scheme is eventually reduced to the solving of Decisional Bilinear Diffie-Hellman problem.Moreover,the scheme is secure under the chosen ciphertext attack in the standard model.展开更多
This paper briefly introduces one of the three physical layer implementations of IEEE 802.16a~[1],WirelessMAN-OFDM PHY.Based on the implementation,the combination of Orthogonal Frequency Division Multiplexing(OFDM)and...This paper briefly introduces one of the three physical layer implementations of IEEE 802.16a~[1],WirelessMAN-OFDM PHY.Based on the implementation,the combination of Orthogonal Frequency Division Multiplexing(OFDM)and Space-Time Coding(STC)which is briefly called ST-OFDM in IEEE 802.16a,is investigated under thechannel provided in Ref.[2].Especially,this paper is focused on the influence of the optimal decision threshold on the sys-tern Bit-Error-Rate(BER)performance based on unequal probabilities of sources.The simulations show that when Signal-Noise-Ratio(SNR)is low the optimal decision threshold is obviously superior to the usual one;when SNR is high to someextent,such as 10 dB for 4QAM and 16 dB for 16QAM,we can use the usual decision threshold instead of the optimal展开更多
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
基金Supported by the National Natural Science Foundation of China(60903175,60703048)the Natural Science Foundation of Hubei Province (2009CBD307,2008CDB352)
文摘The decryption participant＇s private key share for decryption is delegated by key generation center in the threshold IBE scheme.However,a key generation center which is absolutely trustworthy does not exist.So the author presents a certificateless threshold public key encryption scheme.Collaborating with an administrator,the decryption participant generates his whole private key share for decryption in the scheme.The administrator does not know the decryption participant＇s private key share for decryption.Making use of q-SDH assumption,the author constructs a certificateless threshold public key encryption scheme.The security of the scheme is eventually reduced to the solving of Decisional Bilinear Diffie-Hellman problem.Moreover,the scheme is secure under the chosen ciphertext attack in the standard model.
文摘This paper briefly introduces one of the three physical layer implementations of IEEE 802.16a~[1],WirelessMAN-OFDM PHY.Based on the implementation,the combination of Orthogonal Frequency Division Multiplexing(OFDM)and Space-Time Coding(STC)which is briefly called ST-OFDM in IEEE 802.16a,is investigated under thechannel provided in Ref.[2].Especially,this paper is focused on the influence of the optimal decision threshold on the sys-tern Bit-Error-Rate(BER)performance based on unequal probabilities of sources.The simulations show that when Signal-Noise-Ratio(SNR)is low the optimal decision threshold is obviously superior to the usual one;when SNR is high to someextent,such as 10 dB for 4QAM and 16 dB for 16QAM,we can use the usual decision threshold instead of the optimal
