Optimal decision threshold for soft decision cooperative spectrum sensing

In order to achieve higher spectrum efficiency in cognitive radio （CR） systems, a closed-form expression of the optimal decision threshold for soft decision cooperative spectrum sensing based on the minimum total error probability criterion is derived. With the analytical expression of the optimal decision threshold, the impact of different sensing parameters on the threshold value is studied. Theoretical analyses show that the optimal threshold achieves an efficient trade-off between the missed detection probability and the false alarm probability. Simulation results illustrate that the average signal-to-noise ratio （SNR） and the soft combination schemes have a great influence on the optimal threshold value, whereas the number of samples has a weak impact on the optimal threshold value. Furthermore, for the maximal ratio combing （MRC） and the modified deflection coefficient （MDC） schemes, the optimal decision threshold value increases and approaches a corresponding individual limit value while the number of CR users increases. But the number of CR users has a weak influence on the optimal decision threshold for the equal gain combining （EGC） scheme.

Integration Interval Determination and Decision Threshold Optimization for Improved TRPC-UWB Communication Systems

Integration interval and decision threshold issues were investigated for improved transmitted reference pulse cluster （iTRPC-） ultra-wideband （UWB） systems. Our analysis shows that the bit error rate （BER） performance of iTRPC-UWB systems can be significantly improved via integration interval determination （IID） and decision threshold optimization. For this purpose, two modifications can be made at the autocorrelation receiver as follows. Firstly, the liD processing is performed for autocorrelation operation to capture multi-path energy as much as possible. Secondly, adaptive decision threshold （ADT） instead of zero decision threshold （ZDT）, is used as estimated optimal decision threshold for symbol detection. Performance of iTRPCUWB systems using liD and ADT was evaluated in realistic IEEE 802.15.4a UWB channel models and the simulation results demonstrated our theoretical analysis.

A Nonparametric Approach to Foreground Detection in Dynamic Backgrounds

Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.

标准模型下的抗选择密文攻击基于身份门限解密方案

提出了两个有效的抗选择密文攻击的基于身份门限解密方案.在标准的决定性双线性Diffie-Hellman假设下,它们被证明可以抗选择密文攻击.第一个方案基于Boneh和Boyen提出的一个Selective IBE方案,另外一个方案基于Waters提出的Full IBE方案.与所基于的方案相同,前者的挑战身份要在公共参数选取之前确定,而后者的挑战身份可以由攻击者适应性地选取.据我们所知,以前的相关方案的安全性只能在随机预言机模型中得以证明.

标准模型下可证安全的基于身份门限签密方案

提出了一种有效的基于身份的门限签密方案.采用双线性对映射技术,基于Waters和Paterson提出的签名方案,设计了标准模型下可证安全的门限签密方案,并利用决策双线性Diffie-Hellman问题的困难性证明了方案的语义安全性,同时利用计算Diffie-Hellman问题的困难性证明了方案的不可伪造性.相对随机预言模型下的同类方案,本文方案更具有实际意义.

标准模型下可证安全的基于身份的门限环签密方案

签密是一个能够同时提供认证性和保密性的密码学术语,而它却比分别签名和加密具有更低的计算成本。环签密除具有签密的一般属性外,还具有匿名性。为了设计基于身份的门限环签密方案,利用秘密共享和双线性对技术,提出了一种标准模型下基于身份的门限环签密方案,并对方案的安全性进行了分析。最后,通过计算Diffie-Hell-man问题和判定性Diffie-Hellman问题的困难性,证明了该方案在适应性选择消息和身份攻击下的不可伪造性以及在适应性选择密文攻击下的不可区分性。

一种可证安全的基于身份门限代理签密方案

代理签名可以实现签名权利的委托,具有可认证性和不可否认性,然而却不能提供保密性。签密是一种将加密和签名结合在一起的技术,它同时具有两者的优点。利用双线性对技术,提出了一种在标准模型下有效的基于身份门限代理签密方案,并对方案的安全性进行了分析。最后,通过DBDH问题的困难性证明了方案的语义安全性,同时利用CDH问题的困难性证明了方案的不可伪造性。

基于身份的门限环签密方案

把基于身份的环签密和门限签名结合起来,提出了一个基于身份的门限环签密方案。此方案不但具有签密的一般性质:机密性和认证性,还具有签密者的匿名性和门限性质。在CDHP、DBDHP困难性假设下,分别证明了方案在适应性选择消息攻击下的不可伪造性以及在适应性选择密文攻击下的不可区分性。

一种有效基于身份的门限环签密方案

为了设计基于身份的门限环签密方案,利用秘密共享和双线性对技术,提出了一种在标准模型下基于身份的门限环签密方案,并对方案的安全性进行了分析。最后,利用CDH问题和DBDH问题的困难性,证明了方案在适应性选择消息和身份攻击下的不可伪造性以及在适应性选择密文攻击下的不可区分性。

Developing a Secure Framework Using Feature Selection and Attack Detection Technique

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.

A Certificateless Threshold Public Key Encryption Scheme

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.

一种安全有效的基于身份的门限签密方案

签密是一种将加密和数字签名技术结合在一起的思想,它比采用先签名后加密的方法具有更高的效率.基于双线性对技术,提出了一种新的基于身份的门限签密方案.它利用决策双线性Diffie-Hellman(DBDH)问题的困难性在随机预言模型下给出了方案的语义安全性证明,并利用计算Diffie-Hellman(CDH)问题的困难性证明了方案的不可伪造性,同时指出方案具有较高的效率.

The Research of ST-OFDM in Physical Layer of IEEE 802.16a

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