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With the development of information technology, network security has increasingly become a focal point for users and organizations, and encrypted data transmission has gradually become mainstream. This trend has driven the proportion of encrypted traffic on the Internet to rise continuously. However, data encryption, while ensuring privacy and security, has also become a means for illegal content to evade network supervision. To achieve the detection and analysis of encrypted traffic, it has become necessary to efficiently identify encrypted traffic. However, the presence of compressed traffic has significantly interfered with the identification of encrypted traffic. To address this issue, an encrypted traffic identification scheme based on sliding windows and randomness features was designed to efficiently and accurately identify encrypted traffic. Specifically, the scheme involved sampling the payloads of data packets in sessions using a sliding window mechanism to obtain data block sequences that reflect the information patterns of the original traffic. For each data block, randomness measurement algorithms were utilized to extract sample features and construct randomness features for the original payload. Additionally, a decision tree model based on the CART algorithm was designed, which significantly improved the accuracy of identifying encrypted and compressed traffic and greatly reduced the false negative rate for encrypted traffic identification. A balanced dataset was constructed by randomly sampling data from several authoritative websites, and experiments demonstrated the feasibility and efficiency of the proposed scheme.

Conservation monitoring is paramount for the gathering of information on species and populations in need of conservation actions. However, monitoring of the mountain bongo Tragelaphus eurycerus isaaci, a Critically Endangered antelope endemic to Kenya, has hitherto been limited to surveillance (i.e. focused on persistence of the species in particular areas), thus limiting the information that could be retrieved on the status and trends of these populations. Using a newly developed identification system, we implemented robust design mark–recapture using existing camera-trap records of four wild subpopulations of the bongo. We provide data on sex and age structure and the first estimates of population size in the wild. Males and calves seem to be suffering higher mortality than females, and only two of the four monitored populations include adults of both sexes and calves. The combined size of these two breeding populations is estimated to be 29–50. Our findings confirm the critical situation of the mountain bongo in the wild and highlight the need for conservation actions to reinforce the wild populations for the long-term conservation of this antelope.

The current methods for identifying network traffic are generally designed and tested for specific network environments or datasets, making it difficult to generalize and apply to complex and ever-changing actual network environments. A robust traffic recognition algorithm based on graph neural networks was proposed for achieving accurate traffic recognition in practical network scenarios. Firstly, in response to the current algorithm’s neglect of network environment fluctuations and the decrease in accuracy caused by pattern changes, network flows were clustered and filtered by selecting high-level protocol features to reduce the impact of network bandwidth fluctuations on website access traffic behavior. Secondly, due to the fact that most current algorithms only perform single stream recognition and ignore the interrelationships between flows, the various types of feature information and their correlations of network flows were considered, and spatiotemporal correlation features between network flows were extracted through graph neural networks to fully learn network traffic characteristics. By complementing multiple flows and features, the robustness of the algorithm was improved. Finally, a Transformer model that could capture global data information was used as a classifier to analyze the multi type features of network data flow, achieving robust network traffic recognition. Approximately 1 500 and 1 400 visits to 21 target websites in different network environments were collected as datasets for training and testing, achieving an accuracy of 90.7%. Compared with the latest ProGraph algorithm, the accuracy is improved by 7.3%, and the experimental results verify the effectiveness of the proposed method.

Abstract A fault properties identification scheme for a hybrid multi‐terminal DC (MTDC) system is proposed to improve the power supply reliability in the system recovery stage. Firstly, a current signal injection strategy based on line commutated converter (LCC) is proposed using the fault control capability. Secondly, the difference between the calculated value (CV) and the measured value (MV) of the current signal under permanent and temporary faults is analyzed for the distributed parameter line model. By introducing the dynamic time warping (DWT) algorithm to represent the difference between CV and MV, a fault properties identification scheme for permanent fault identification is constructed. Finally, the proposed scheme for permanent fault location with the distributed parameter model is obtained by introducing the trust region reflection (TRR) algorithm. The simulation results show that the proposed fault properties identification scheme has the function of permanent fault identification and location. The proposed scheme fully considers the effects of fault impedance, sampling rate, noise, and data loss. It can be applied to high voltage DC long‐distance transmission lines with LCC.

To ensure their sound and continuous operation to the greatest extent, VSC-based DC girds have extremely stringent requirements for transmission line relay protection. In terms of guaranteeing their reliability, accurate identification of lightning strikes on DC transmission lines is one of the urgent key problems to be solved. An effective ultra-high-speed identification scheme of lightning strikes suitable for the VSC-based DC grid is proposed in this paper. First, an 1-mode reverse voltage traveling wave (RVTW) is constructed applying the pole-mode transformation theory. Next, fault traveling wave propagation characteristics along the DC transmission line are analyzed in depth utilizing Peterson's law. Then, differences of time-frequency electromagnetic transient characteristics of 1-mode RVTWs between disturbances and faults caused by lightning strikes are distinguished in detail by means of the classical wavelet transformation multi-resolution analysis theory. Finally, extensive simulations are carried out to evaluate the performance of the proposed identification scheme, and by which its excellent rapidity, reliability and robustness are validated.

Any faults in power electronic switches must be detected and located promptly to certify the unchanging operation and reliability of multilevel inverter (MLI) systems. Machine learning (ML) techniques are increasingly being utilized to diagnose faults in MLIs due to their advantages, such as high precision, less calculation time, and reduced complexity. A faulty switch identification and phase identification method based on different ML classifiers is presented in this paper, explicitly targeting open-circuit faults in switches in a PV-fed 3-phase three-level Neutral Point Clamped (NPC) inverter. The NPC MLI output voltage and current signals are input signals to classify faults. The information or essential feature from the raw voltage and current data is extracted using a recursive discrete Fourier (RDF) followed by a discrete wavelet transform (DWT). Finally, the standard deviations of the approximate and detailed coefficients are used as input for ML algorithms. To do the comparative analysis, a variety of ML techniques are then applied to these features, including logistic regression (LR), random forest (RF), quadratic discriminate analysis (QDA), K-nearest neighbour (K-NN), and support vector machines (SVM). The experiment results indicate that the RF algorithms achieve the highest classification accuracy of 99.59 % for various switch fault conditions.

Abstract Cryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification accuracy of ECB and CBC encryption modes in block cryptosystem is significantly higher than that of the other two classification models by 35.2% and 36.1%. In single division, the identification accuracy is higher than HGBDTLR with a maximum of 9.8%, and higher than HKNNRF with a maximum of 7.5%. At the same time, the scheme proposed in this paper has significantly improved the identification effect compared with the single division identification accuracy of 17 cryptosystem directly and the 17 classification accuracy of 5.9% compared with random classification, which indicates that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has significant advantages in the accuracy of identifying multiple cryptographic algorithms.

Abstract A fault identification scheme for protection and adaptive reclosing is proposed for a hybrid multi-terminal HVDC system to increase the reliability of fault isolation and reclosing. By analyzing the "zero passing" characteristic of current at the local end during the converter capacitor discharge stage, the fault identification scheme is proposed. The distributed parameter-based fault location equation, which incorporates fault distance and fault impedance, is developed with the injection signal and the distributed parameter model during the adaptive reclosing stage. The fault distance is determined using a trust region reflection algorithm to identify the permanent fault, and a fault identification scheme for adaptive reclosing is developed. Simulation results show that the proposed scheme is suitable for long-distance transmission lines with strong anti-fault impedance and anti-interference performance. Also, it is less affected by communication delay and DC boundary strength than existing methods.

In order to solve the problem of worse recognition performance under the multi-classification scenarios of existing cryptosystem identification, this paper proposes a block cryptosystem recognition scheme based on Hamming weight distribution. A feature extraction method based on Hamming weight distribution is designed to extract the cipher text features, and the cryptosystem is further described. XGBoost-RFE feature selection algorithm is proposed to filter invalid features. In order to better adapt to the characteristics of complex ciphertext data and difficult fitting in multi classification scenarios, XGB-LGBM ensemble learning model with multi-layer fusion structure is designed to further improve the accuracy and generalization of recognition. The experiment carried out mixed identification of 10 common block cipher algorithm, and the overall recognition accuracy reached 89.65%.

The identification protocol is a type of zero-knowledge proof. One party (the prover) needs to prove his identity to another party (the verifier) without revealing the secret key to the verifier. One can apply the Fiat–Shamir transformation to convert an identification scheme into a signature scheme which can be used for achieving security purposes and cryptographic purposes, especially for authentication. In this paper, we recall an identification protocol, namely the RankID scheme, and show that the scheme is incorrect and insecure. Then, we proposed a more natural approach to construct the rank version of the AGS identification protocol and show that our construction overcomes the security flaws in the RankID scheme. Our proposal achieves better results when comparing the public key size, secret key size, and signature size with the existing identification schemes, such as Rank RVDC and Rank CVE schemes. Our proposal also achieves 90%, 50%, and 96% reduction for the signature size, secret key size, and public key size when compared to the Rank CVE signature scheme.

Conservation monitoring is paramount for the gathering of information on species and populations in need of conservation actions. However, monitoring of the mountain bongo Tragelaphus eurycerus isaaci , a Critically Endangered antelope endemic to Kenya, has hitherto been limited to surveillance (i.e. focused on persistence of the species in particular areas), thus limiting the information that could be retrieved on the status and trends of these populations. Using a newly developed identification system, we implemented robust design mark–recapture using existing camera-trap records of four wild subpopulations of the bongo. We provide data on sex and age structure and the first estimates of population size in the wild. Males and calves seem to be suffering higher mortality than females, and only two of the four monitored populations include adults of both sexes and calves. The combined size of these two breeding populations is estimated to be 29–50. Our findings confirm the critical situation of the mountain bongo in the wild and highlight the need for conservation actions to reinforce the wild populations for the long-term conservation of this antelope. [ABSTRACT FROM AUTHOR]