To address the limitations of existing clean-label backdoor attacks, particularly concerning feature space heterogeneity, we propose a novel feature-distance-guided clean-label backdoor attack method. Specifically, we first introduce a sample selection strategy based on feature discrepancy and recognizability constraints. This strategy involves calculating the mean feature vector for each class within the dataset and subsequently employing the Fréchet Inception Distance to quantify the deviation of individual sample feature vectors from their respective class mean, thereby identifying samples significantly diverging from the class distribution. Subsequently, we present an innovative noise generation technique termed Feature Displacement-Driven Noise Iteration. For each selected training sample, we iteratively adjust the intensity of the added noise to effectively amplify its feature distribution divergence while rigorously preserving the recognizability of the sample’s original label, ultimately yielding highly optimized adversarial noise. Finally, this iterated noise, along with a pre-defined trigger, is embedded into the chosen training samples to construct poisoned samples, which are then utilized for model training. Experimental results unequivocally demonstrate that, compared to existing techniques, our proposed method significantly enhances the attack success rate by 0.88% to 13.99%, while maintaining nearly identical classification accuracy on benign samples. This conclusively validates the superior performance of our approach.

In response to the limitations of traditional fuzzing approaches that rely on static mutators and fail to dynamically adjust their test case mutations for deeper testing, resulting in the inability to generate targeted inputs to trigger vulnerabilities, this paper proposes a directed fuzzing methodology termed DocFuzz, which is predicated on a feedback mechanism mutator. Initially, a sanitizer is used to target the source code of the tested program and stake in code blocks that may have vulnerabilities. After this, a taint tracking module is used to associate the target code block with the bytes in the test case, forming a high‐value byte set. Then, the reinforcement learning mutator of DocFuzz is used to mutate the high‐value byte set, generating well‐structured inputs that can cover the target code blocks. Finally, utilizing the feedback mechanism of DocFuzz, when the reinforcement learning mutator converges and ceases to optimize, the fuzzer is rebooted to continue mutating toward directions that are more likely to trigger vulnerabilities. Comparative experiments are conducted on multiple test sets, including LAVA‐M, and the experimental results demonstrate that the proposed DocFuzz methodology surpasses other fuzzing techniques, offering a more precise, rapid, and effective means of detecting vulnerabilities in source code.

Aiming at the problems that the sample-based category imbalance methods were prone to the loss of important data and the current software defect prediction methods did not identify defect potential risks based on the dichotomous classification results, a defect risk identification method on the premise of the sample category perception was proposed. Firstly, noise samples, feature distributions, high-dimensional features, and category imbalance of multi-source sample sets were processed to obtain cleaned samples. Then, the category perception and perception coefficients of the cleaned samples were calculated to obtain the category perception product, and the defect risk identification (DRI) model was constructed by the category perception product. Finally, the risk identification probabilities were calculated using the DRI model to identify potential risks. The experimental results show that the proposed method performs well with accuracy, F1-score, and Matthews correlation coefficient. The obtained risk identification probabilities and defect risk levels are consistent with the actual situation of real samples, which can accurately reflect the severity of defects and identify potential risks.

To solve the problems that existing network security situation assessment (NSSA) methods are difficult to extract features and have poor timeliness, an NSSA method with network attack behavior classification (NABC) is proposed. First, an NABC model is designed. The model combines features and advantages of a parallel feature extraction network (PFEN), a bidirectional gate recurrent unit (BiGRU), and the attention mechanism (ATT). The PFEN module is composed of parallel sparse autoencoders which extract key data from different network attack behaviors. The BiGRU module gets the time-series relationship from the state of three different time periods, finds potential representation rules from network attack behaviors. The ATT module pays more attention to the network traffic key information and improves the NABC accuracy. Second, the NABC detects and classifies attacks from network behaviors, the occurrence number of each attack behavior, and the error probability matrix are counted. Finally, the occurrence number of each attack behavior is corrected according to the error probability matrix, and the network security situation value is calculated through combining the severity factor of each attack behavior. The experimental results show that the precision and recall of the NABC model are improved by 5.28% and 5.65%, respectively, compared with the conventional method. The comparison experiment with the classical situation assessment method also proves that the proposed method can assess the overall situation of network security more effectively and comprehensively.

Based on the research of business continuity and information security of the Internet of Things (IoT), a key business node identification model for the Internet of Things security is proposed. First, the business nodes are obtained based on the business process, and the importance decision matrix of business nodes is constructed by quantifying the evaluation attributes of nodes. Second, the attribute weights are improved by the analytic hierarchy process (AHP) and entropy weighting method from subjective and objective dimensions to form the combination weight decision matrix, and the analytic hierarchy process and entropy weighting VIKOR (AE-VIKOR) method are used to calculate the business node importance coefficient to identify the key nodes. Finally, according to the NSL-KDD dataset, the network security events of IoT network intrusion detection based on machine learning are monitored purposefully, and after the information security event occurs in the smart mobile phone, which impacts through IoT on the business system, the impact of the key business node on business continuity is analyzed, and the business continuity risk value is calculated to evaluate the business risk to prove the effectiveness of the model. The experimental results of the civil aviation departure business show that the AE-VIKOR method can effectively identify key business node, and the impact of the key business node on business continuity is analyzed, which further proves the efficiency and accuracy of the model in identifying the key business node.

The accuracy of the existing security situation assessment model of information system for smart mobile devices is affected by expert evaluation preferences. This paper proposes an information system security situation assessment model for smart mobile devices, which is based on the modified interval matrix-entropy weight-based cloud (MIMEC). According to the security situation assessment index system, the interval judgment matrix reflecting the relative importance of different indexes is modified to improve the objectivity of the index layer weight vector. Then, the entropy weight-based cloud is used to quantify the criterion layer and the target layer security situation index, and the security level of the system is graded. The evaluation experiment on the departure control system for smart mobile devices not only verify the validity of this model but also demonstrate that this model has higher stability and reliability than other models.