As unmanned aerial vehicle (UAV) technology develops rapidly and is widely used, illegal intrusion into UAV networks occurs frequently, posing a serious threat to public safety. To enhance the feature extraction ability of UAV network and effectively monitor the operation status of UAVs, we propose a lightweight Transformer network based on rotary position encoding, local aggregation attention unit, and lightweight feed-forward neural network for UAV traffic intrusion detection. Firstly, rotary position encoding is employed to encode the network traffic. To enhance the local and global information extraction of the network traffic, we propose a local aggregation attention unit, which can effectively aggregate and extract the local features by using group linear transformation, and realize the perception and enhancement of the global information by self-attention mechanism. Then, the enhanced features are extracted using a lightweight feed-forward neural network. Finally, the experimental results show that the proposed UAV traffic intrusion detection method has a more superior detection performance and provides a new lightweight solution for the security protection of UAV.

With the development of network technology, there is a large amount of traffic data in the network at all times. In order to maintain network order and prevent attackers from damaging it, it is necessary to study methods for real-time monitoring of network traffic anomalies. The current machine learning based network traffic anomaly detection methods mostly rely on complex feature dimensionality reduction and feature selection techniques. The model relies on labeled data and is prone to getting stuck in local optima during use. A self supervised patch Transformer(SSPT) based network intrusion detection method is proposed. To solve the problem of wide distribution and strong discreteness of raw attack data, the data is first subjected to single hot encoding and normalization processing, and then segmented into subsets as inputs to the Transformer. Then, training and classification are achieved by sharing the same weights through independent channels. The paper conducted experiments on the CIC-Bell-DNS-EXF-2021 dataset, and the results showed that the proposed method achieved an accuracy of 99.34% for the aforementioned dataset, which is 2.80% higher than the same type of LSTM.

As malicious code attacks continue to evolve, attackers leverage techniques like packing and code obfuscation to generate numerous variants, challenging traditional detection methods. Addressing the limitations of current deep learning-based malicious code classification approaches in feature extraction and accuracy, this paper introduces an innovative RGB visualization detection method based on a hybrid multi-head attention mechanism. Initially, a feature representation method utilizing RGB images is introduced. This approach focuses on semantic relationships between a malware’s binary information, assembly details, and API data, generating images with richer textural information. This technique effectively uncovers the deep dependencies between the original and variant versions of malicious code, providing stronger support for subsequent classification tasks. Furthermore, to tackle the issues of malware encryption and obfuscation, a deep neural network framework is adopted, incorporating a modular design philosophy and integrating a multi-head attention mechanism. This design not only enhances the expressiveness of critical features but also helps the model better focus on key aspects of the malicious code, thereby improving classification accuracy. Through comparative experiments and in-depth analysis, the effectiveness and superiority of the proposed RGB visualization method and MSA-ResNet model in the field of malicious code variant classification are validated. The accuracy rates achieved on the Kaggle and DataCon datasets are 99.49% and 97.70%, respectively, representing significant improvements over other methods. This approach demonstrates strong generalization capabilities and resistance to obfuscation, offering a new and effective tool for malicious code detection.

Assessment of the contribution rates of weapon system-of-systems is an extremely uncertain and complex problem that should be addressed by domain experts. Addressing these problems, which is often hesitant, fuzzy, and linguistic, requires evaluation of multiple indices of different types. Hesitant Pythagorean fuzzy sets combined with linguistic term sets are powerful tools to handle this kind of problem. In this paper, recent studies on hesitant Pythagorean fuzzy sets (HPFSs) are first reviewed and linguistic HPFSs (LHPFSs) are proposed. To efficiently solve the evaluation problem, the axiomatic properties of entropies for HPFSs and LHPFSs are defined. According to these properties, novel entropy measures for HPFSs and LHPFSs are introduced. A novel method that can better reveal the characteristics of HPFSs and LHPFS to represent the HPF and LHPF numbers is also introduced. On this foundation, the definitions of distance measures for HPFSs and LPFSs are proposed, and a set of distance measures for HPFSs and LHPFSs are developed. Moreover, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is modified through hierarchical thought by using the presented entropy and distance measures for LHPFSs; the improved TOPSIS method is applied preferably to the case of contribution of weapon system-of-systems and shows better validity and distinguishability than other methods under the LHPFS environment in the examples. The hierarchical TOPSIS method can better solve the problem of weight allocation in case of a lot of indices.

Attention mechanisms have recently shown strong potential in improving the performance of convolutional neural networks (CNNs). This letter proposes a fully convolutional attention block (FCAB) that can be combined with a CNN to refine important features and suppress unnecessary ones in synthetic aperture radar (SAR) images. The FCAB consists of a channel attention module and a spatial attention module. For the channel attention module, we use average-pooling and max-pooling to learn complementary features, and apply group convolution to aggregate the information of the two types of channels. Global average-pooling is then used to encode the channel-wise importance. For the spatial attention module, the average-pooling and max-pooling along the channel axis are used to generate two spatial feature maps, and then two very lightweight convolutional layers are used to encode the spatial weight map. Experimental results on SAR images demonstrate that our FCAB can focus on important channels and object regions. It uses relatively few parameters and is computationally efficient, while bringing about significant performance gain for SAR recognition.

High-resolution range profile (HRRP) has obtained intensive attention in radar target recognition and convolutional neural networks (CNNs) are among predominant approaches to deal with HRRP recognition problems. However, most CNNs are designed by the rule-of-thumb and suffer from much more computational complexity. Aiming at enhancing the channels of one-dimensional CNN (1D-CNN) for extracting efficient structural information oftargets form HRRP and reducing the computation complexity, we propose a novel framework for HRRP-based target recognition based on 1D-CNN with channel attention and channel pruning. By introducing an aggregation-perception-recalibration (APR) block for channel attention to the 1D-CNN backbone, channels in each 1D convolutional layer can adaptively learn to recalibrate the extracted features for enhancing the structural information captured from HRRP. To avoid rule-of-thumb design and reduce the computation complexity of 1D-CNN, we proposed a new method incorporated withthe global best leading artificial bee colony (GBL-ABC) to prune the original network based on the lottery ticket hypothesis in an automatic and heuristic manner. The extensive experimental results on the measured data illustrate that the proposed algorithm achievesthe superiorrecognition rate by combing APR and GBL-ABC simultaneously.

It is of great significance to measure the knowledge amount conveyed by Atanassov’s intuitionistic fuzzy sets (AIFSs). Many efforts have been done to define a suitable knowledge measure for AIFSs, or uncertainty measure, named as a dual measure of knowledge measure. However, many of these measures are developed from the view of point of intuitionistic fuzzy entropy, which cannot well reflect the knowledge amount associated with an AIFS. Other knowledge measures developed based on the difference between an AIFS and its complement may lead to information loss in the scenario of decision making. This paper proposed a new knowledge measure for AIFSs. The axiomatic definition of knowledge measure is extended to a more general level. The properties of the new developed knowledge measure are investigated through mathematical analysis and numerical examples. Further discussion on the relation between knowledge measure and entropy measure is proposed to clear up the relation and distinction between them.

Abstract When training base classifier by ternary Error Correcting Output Codes (ECOC), it is well know that some classes are ignored. On this account, a non-competent classifier emerges when it classify an instance whose real label does not belong to the meta-subclasses. Meanwhile, the classic ECOC dichotomizers can only produce binary outputs and have no capability of rejection for classification. To overcome the non-competence problem and better model the multi-class problem for reducing the classification cost, we embed reject option to ECOC and present a new variant of ECOC algorithm called as Reject-Option-based Re-encoding ECOC (ROECOC). The cost-sensitive classification model and cost-loss function based on Receiver Operating Characteristic (ROC) curve are built respectively. The optimal reject threshold values are obtained by combing the condition to be met for minimizing the loss function and the ROC convex hull. In so doing, reject option (t1, t2) provides a three-symbol output to make dichotomizers more competent and ROECOC more universal and practical for cost-sensitive classification issue. Experimental results on two kinds of datasets show that our scheme with low-degree freedom of initialized ECOC can effectively enhance accuracy and reduce cost.