CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing.

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC's capabilities in terms of security and robustness.

Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection.

Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.

Construction of Structured Random Measurement Matrices in Semi-Tensor Product Compressed Sensing Based on Combinatorial Designs.

A random matrix needs large storage space and is difficult to be implemented in hardware, and a deterministic matrix has large reconstruction error. Aiming at these shortcomings, the objective of this paper is to find an effective method to balance these performances. Combining the advantages of the incidence matrix of combinatorial designs and a random matrix, this paper constructs a structured random matrix by the embedding operation of two seed matrices in which one is the incidence matrix of combinatorial designs, and the other is obtained by Gram-Schmidt orthonormalization of the random matrix. Meanwhile, we provide a new model that applies the structured random matrices to semi-tensor product compressed sensing. Finally, compared with the reconstruction effect of several famous matrices, our matrices are more suitable for the reconstruction of one-dimensional signals and two-dimensional images by experimental methods.

A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs.

Body to body networks (BBNs) are a kind of large-scaled sensor network that are composed of several wireless body area networks (WBANs) in the distributed structure, and in recent decades, BBNs have played a key role in medical, aerospace, and military applications. Compared with the traditional WBANs, BBNs have larger scales and longer transmission distances. The sensors within BBNs not only transmit the data they collect, but also forward the data sent by other nodes as relay nodes. Therefore, BBNs have high requirements in energy efficiency, data security, and privacy protection. In this paper, we propose a secure and efficient data transmission method for sensor nodes within BBNs that is based on the perception of chaotic compressive sensing. This method can simultaneously accomplish data compression, encryption, and critical information concealment during the data sampling process and provide various levels of reconstruction qualities according to the authorization level of receivers. Simulation and experimental results demonstrate that the proposed method could realize data compression, encryption, and critical information concealment for images that are transmitted within BBNs. Specifically, the proposed method could enhance the security level of data transmission by breaking the statistical patterns of original data, providing large key space and sensitivity of the initial values, etc.

Low Energy Consumption Compressed Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Radio Network.

For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.

Chaos-order transition in foraging behavior of ants.

The study of the foraging behavior of group animals (especially ants) is of practical ecological importance, but it also contributes to the development of widely applicable optimization problem-solving techniques. Biologists have discovered that single ants exhibit low-dimensional deterministic-chaotic activities. However, the influences of the nest, ants' physical abilities, and ants' knowledge (or experience) on foraging behavior have received relatively little attention in studies of the collective behavior of ants. This paper provides new insights into basic mechanisms of effective foraging for social insects or group animals that have a home. We propose that the whole foraging process of ants is controlled by three successive strategies: hunting, homing, and path building. A mathematical model is developed to study this complex scheme. We show that the transition from chaotic to periodic regimes observed in our model results from an optimization scheme for group animals with a home. According to our investigation, the behavior of such insects is not represented by random but rather deterministic walks (as generated by deterministic dynamical systems, e.g., by maps) in a random environment: the animals use their intelligence and experience to guide them. The more knowledge an ant has, the higher its foraging efficiency is. When young insects join the collective to forage with old and middle-aged ants, it benefits the whole colony in the long run. The resulting strategy can even be optimal.

The effect of randomness for dependency map on the robustness of interdependent lattices.

The percolation for interdependent networks with identical dependency map follows a second-order phase transition which is exactly the same with percolation on a single network, while percolation for random dependency follows a first-order phase transition. In real networks, the dependency relations between networks are neither identical nor completely random. Thus in this paper, we study the influence of randomness for dependency maps on the robustness of interdependent lattice networks. We introduce approximate entropy(ApEn) as the measure of randomness of the dependency maps. We find that there is critical ApEnc below which the percolation is continuous, but for larger ApEn, it is a first-order transition. With the increment of ApEn, the pc increases until ApEn reaching ApEnc (') and then remains almost constant. The time scale of the system shows rich properties as ApEn increases. Our results uncover that randomness is one of the important factors that lead to cascading failures of spatially interdependent networks.

An Energy Efficient Mutual Authentication and Key Agreement Scheme Preserving Anonymity for Wireless Sensor Networks.

WSNs (Wireless sensor networks) are nowadays viewed as a vital portion of the IoTs (Internet of Things). Security is a significant issue in WSNs, especially in resource-constrained environments. AKA (Authentication and key agreement) enhances the security of WSNs against adversaries attempting to get sensitive sensor data. Various AKA schemes have been developed for verifying the legitimate users of a WSN. Firstly, we scrutinize Amin-Biswas's currently scheme and demonstrate the major security loopholes in their works. Next, we propose a lightweight AKA scheme, using symmetric key cryptography based on smart card, which is resilient against all well known security attacks. Furthermore, we prove the scheme accomplishes mutual handshake and session key agreement property securely between the participates involved under BAN (Burrows, Abadi and Needham) logic. Moreover, formal security analysis and simulations are also conducted using AVISPA(Automated Validation of Internet Security Protocols and Applications) to show that our scheme is secure against active and passive attacks. Additionally, performance analysis shows that our proposed scheme is secure and efficient to apply for resource-constrained WSNs.

Robust and efficient biometrics based password authentication scheme for telecare medicine information systems using extended chaotic maps.

The Telecare Medicine Information Systems (TMISs) provide an efficient communicating platform supporting the patients access health-care delivery services via internet or mobile networks. Authentication becomes an essential need when a remote patient logins into the telecare server. Recently, many extended chaotic maps based authentication schemes using smart cards for TMISs have been proposed. Li et al. proposed a secure smart cards based authentication scheme for TMISs using extended chaotic maps based on Lee's and Jiang et al.'s scheme. In this study, we show that Li et al.'s scheme has still some weaknesses such as violation the session key security, vulnerability to user impersonation attack and lack of local verification. To conquer these flaws, we propose a chaotic maps and smart cards based password authentication scheme by applying biometrics technique and hash function operations. Through the informal and formal security analyses, we demonstrate that our scheme is resilient possible known attacks including the attacks found in Li et al.'s scheme. As compared with the previous authentication schemes, the proposed scheme is more secure and efficient and hence more practical for telemedical environments.

An enhanced biometric-based authentication scheme for telecare medicine information systems using elliptic curve cryptosystem.

The telecare medical information systems (TMISs) enable patients to conveniently enjoy telecare services at home. The protection of patient's privacy is a key issue due to the openness of communication environment. Authentication as a typical approach is adopted to guarantee confidential and authorized interaction between the patient and remote server. In order to achieve the goals, numerous remote authentication schemes based on cryptography have been presented. Recently, Arshad et al. (J Med Syst 38(12): 2014) presented a secure and efficient three-factor authenticated key exchange scheme to remedy the weaknesses of Tan et al.'s scheme (J Med Syst 38(3): 2014). In this paper, we found that once a successful off-line password attack that results in an adversary could impersonate any user of the system in Arshad et al.'s scheme. In order to thwart these security attacks, an enhanced biometric and smart card based remote authentication scheme for TMISs is proposed. In addition, the BAN logic is applied to demonstrate the completeness of the enhanced scheme. Security and performance analyses show that our enhanced scheme satisfies more security properties and less computational cost compared with previously proposed schemes.

A new fixed-time stability theorem and its application to the fixed-time synchronization of neural networks.

In this paper, we derive a new fixed-time stability theorem based on definite integral, variable substitution and some inequality techniques. The fixed-time stability criterion and the upper bound estimate formula for the settling time are different from those in the existing fixed-time stability theorems. Based on the new fixed-time stability theorem, the fixed-time synchronization of neural networks is investigated by designing feedback controller, and sufficient conditions are derived to guarantee the fixed-time synchronization of neural networks. To show the usability and superiority of the obtained theoretical results, we propose a secure communication scheme based on the fixed-time synchronization of neural networks. Numerical simulations illustrate that the new upper bound estimate formula for the settling time is much tighter than those in the existing fixed-time stability theorems. Moreover, the plaintext signals can be recovered according to the new fixed-time stability theorem, while the plaintext signals cannot be recovered according to the existing fixed-time stability theorems.

Propagation analysis and prediction of the COVID-19.

Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is quite small. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.

Parameter estimation of nonlinear dynamical systems based on integrator theory.

In this paper, a novel unknown parameter identifier of nonlinear dynamical systems is designed through the integrator theory, and the corresponding sufficient conditions for the existence of unknown parameter identifiers are presented. In order to illustrate the effectiveness of the proposed method, simulation results are given. The effects of system noise and measurement noise for the proposed method are discussed in detail. The comparative analysis between the proposed method based on integrator theory and the approach based on adaptive synchronization is also given.

Parameter identification and projective synchronization between different chaotic systems.

In this paper, adaptive generalized projective synchronization and parameter identification in different chaotic systems are carefully revisited. We use a concrete counterexample to demonstrate that the result in a previous work [R. Li, W. Xu, and S. Li, Phys. Lett. A 367, 199 (2007)] is imperfect, where a scheme of generalized projective synchronization is proposed for parameter identification with some drawbacks on ignoring the conditions which ensure the parameter convergence. We further discuss the two conditions of parameter convergence, which are linear independence and persistent excitation. A special relationship between them is addressed to estimate unknown model parameters effectively.

Fixed-time synchronization of inertial memristor-based neural networks with discrete delay.

This paper is concerned with the fixed-time synchronization control of inertial memristor-based neural networks with discrete delay. We design four different kinds of feedback controllers, under which the considered inertial memristor-based neural networks can realize fixed-time synchronization perfectly. Moreover, the obtained fixed-time synchronization criteria can be verified by algebraic operations. For any initial synchronization error, the settling time of fixed-time synchronization is bounded by a fixed constant, which can be calculated beforehand based on system parameters and controller parameters. Numerical simulations are given to illustrate the effectiveness of our theoretical results.

Harnessing piecewise-linear systems to construct dynamic logic architecture.

This paper explores piecewise-linear systems to construct dynamic logic architecture. We present three schemes to obtain various basic logic gates, adders, and memory by using piecewise-linear systems. These schemes can switch easily among different operational roles by changing parameters. The proposed schemes are computationally efficient and easy to use. It is convenient for us to study and analyze them with the theory of linear systems.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.

This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results.

A Secure and Efficient Scalable Secret Image Sharing Scheme with Flexible Shadow Sizes.

In a general (k, n) scalable secret image sharing (SSIS) scheme, the secret image is shared by n participants and any k or more than k participants have the ability to reconstruct it. The scalability means that the amount of information in the reconstructed image scales in proportion to the number of the participants. In most existing SSIS schemes, the size of each image shadow is relatively large and the dealer does not has a flexible control strategy to adjust it to meet the demand of differen applications. Besides, almost all existing SSIS schemes are not applicable under noise circumstances. To address these deficiencies, in this paper we present a novel SSIS scheme based on a brand-new technique, called compressed sensing, which has been widely used in many fields such as image processing, wireless communication and medical imaging. Our scheme has the property of flexibility, which means that the dealer can achieve a compromise between the size of each shadow and the quality of the reconstructed image. In addition, our scheme has many other advantages, including smooth scalability, noise-resilient capability, and high security. The experimental results and the comparison with similar works demonstrate the feasibility and superiority of our scheme.