ReLFA:Resist Link Flooding Attacks via Renyi Entropy and Deep Reinforcement Learning in SDN-IoT

Link flooding attack(LFA)is a fresh distributed denial of service attack(DDoS).Attackers can cut off the critical links,making the services in the target area unavailable.LFA manipulates legal lowspeed flow to flood critical links,so traditional technologies are difficult to resist such attack.Meanwhile,LFA is also one of the most important threats to Internet of things(IoT)devices.The introduction of software defined network(SDN)effectively solves the security problem of the IoT.Aiming at the LFA in the software defined Internet of things(SDN-IoT),this paper proposes a new LFA mitigation scheme ReLFA.Renyi entropy is to locate the congested link in the data plane in our scheme,and determines the target links according to the alarm threshold.When LFA is detected on the target links,the control plane uses the method based on deep reinforcement learning(DRL)to carry out traffic engineering.Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT.In addition,the rerouting time of ReLFA is superior to other latest schemes.

Infrared image segmentation method based on 2D histogram shape modification and optimal objective function

In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

Quantum Entanglement of Parallel-Coupled Double Quantum Dots:a Theoretical Study Using the Hierarchical Equations of Motion Approach

Quantum dots comprise a type of quantum impurity system. The entanglement and co- herence of quantum states are significantly influenced by the strong electron-electron interactions among impurities and their dissipative coupling with the surrounding environment. Competition between many-body effects and transfer couplings plays an important role in determining the entanglement among localized impurity spins. In this work, we employ the hierarchical-equations-of-rnotion approach to explore the entanglement of a strongly correlated double quantum dots system. The relation between the total system entropy and those of subsystems is also investigated.

Incremental Learning Framework for Mining Big Data Stream

At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

Fault detection for centrifugal chillers using a Kernel Entropy Component Analysis (KECA) method

Fault detection is beneficial for chiller routine operation management in building automation systems.Considering the limitations of traditional principal component analysis(PCA)algorithm for chiller fault detection,a so-called kernel entropy component analysis(KECA)method has been developed and the development results are reported in this paper.Unlike traditional PCA,in KECA,the feature extraction or dimensionality reduction is implemented in a new space,called kernel feature space.The new space is nonlinearly related to the input space.The data set in the kernel feature space is projected onto a principal component subspace constructed by the feature space principal axes determined by the maximum Rényi entropy rather than the top eigenvalues.The proposed KECA is more suitable to deal with nonlinear process without Gaussian assumption.Using the available experimental data from ASHRAE RP-1043,seven typical chiller faults were tested by the proposed KECA method,and the results were compared to that of PCA.Two statistics,i.e.T2 and squared prediction error(SPE),were employed for fault detection monitoring.The fault detection results showed that the proposed KECA method had a better performance in terms of a higher detection accuracy in comparison to the traditional PCA.For the seven typical faults,the fault detection ratios were over 55%,even at their corresponding least severity level when using the proposed KECA based chiller fault detection method.

Note on non-vacuum conformal family contributions to Rényi entropy in two-dimensional CFT

We calculate the contributions of a general non-vacuum conformal family to R′enyi entropy in twodimensional conformal field theory（CFT）. The primary operator of the conformal family can be either non-chiral or chiral, and we denote its scaling dimension by ？. For the case of two short intervals on a complex plane, we expand the R′enyi mutual information by the cross ratio x to order x^（2△＋2）. For the case of one interval on a torus with low temperature, we expand the R′enyi entropy by q = exp（-2πβ/L）, with β being the inverse temperature and L being the spatial period, to order q^（△＋2）. To make the result meaningful, we require that the scaling dimension ？ cannot be too small. For two intervals on a complex plane we need △ 〉 1, and for one interval on a torus we need △ 〉 2.We work in the small Newton constant limit on the gravity side and so a large central charge limit on the CFT side,and find matches of gravity and CFT results.

On Renyi entropies of order statistics

In this paper we consider a generalize dynamic entropy measure and prove that this mea- sure characterizes the distribution function uniquely. Also we propose cumulative resi- dual R^nyi entropy of order statistics and prove that it also determines the distribution function uniquely. Applications of entropy concepts to DNA sequence analysis, the ulti- mate support for the biological systems, have been widely explored by researchers. The entropy measures discussed here can be applied for analysis of ordered DNA sequences.

Uncertainty and Certainty Relations for Pauli Observables in Terms of R′enyi Entropies of Order α ∈(0;1]

We obtain uncertainty and certainty relations of state-independent form for the three Paufi observables with use of the Renyi entropies of order α∈ （0; 1]. It is shown that these entropic bounds are tight in the sense that they are always reached with certain pure states. A new result is the condition for equality in Renyi-entropy uncertainty relations for the Pauli observables. Upper entropic bounds in the pure-state case are also novel. Combining the presented bounds leads to a band, in which the rescaled average Renyi a-entropy ranges for a pure measured state. A width of this band is compared with the Tsallis formulation derived previously.

Generalized Conditional Entropies of Partitions on Quantum Logic

For partitions on quantum logic, the Rdnyi and Tsallis conditional entropies are introduced. Several relations between the conditional entropies of such partitions are derived.

Quantum uncertainty relations of two generalized quantum relative entropies of coherence

In this paper, we consider the quantum uncertainty relations of two generalized relative entropies of coherence based on two measurement bases. First, we give quantum uncertainty relations for pure states in a d-dimensional quantum system by making use of the majorization technique; these uncertainty relations are then generalized to mixed states. We find that the lower bounds are always nonnegative for pure states but may be negative for some mixed states. Second, the quantum uncertainty relations for single qubit states are obtained by the analytical method. We show that the lower bounds obtained by this technique are always positive for single qubit states. Third, the lower bounds obtained by the two methods described above are compared for single qubit states.