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.

The malicious domains generated by domain generation algorithm (DGA) are a threat to network security and the existing DGA domain detection methods commonly represent domain features by scalars, resulting in damage to the feature structure. To cope with the above issues, an improved capsule network for DGA domain detection was proposed. Firstly, the original samples were numerically processed and converted to the domain word vectors. Secondly, we built a n-grams feature extraction network based on residual network to extract domain features. Thirdly, we designed an improved capsule network to classify the domains according to the domain features. The domain features were converted to primary capsules. Finally, an improved dynamic routing algorithm was used to generate high-level capsules, whose lengths were used as auxiliary information for detecting domains. The experimental results show that compared with state-of-the-art methods, our method has remarkable detection performance.

Machine learning technology has achieved excellent results in Android malware detection, however, existing detection methods ignore the frequent changes of API in malware, resulting in their detection performance continuing to decline over time. In this paper, we propose an evolved Android malware detection method (EAMDM). Two components comprise EAMDM: API clustering and malware detection. Before malware detection, we perform API clustering to obtain cluster centers representing the function of each API. we employ Bert to comprehensively extract the semantic information contained in API features such as method name, exception, and permission. Bert generates feature vectors for clustering that represent the similarity of API functions. In malware detection, EAMDM abstracts the API into cluster centers in order to maintain resilience against the frequent changes of API in both malware and Android framework. We evaluate the effectiveness of EAMDM on a dataset of 85K apps developed over seven years. The experimental results show that EAMDM greatly outperforms the existing classic methods and has a significantly slower aging speed.

The domain name features used in the existing domain name detection methods about domain generation algorithm (DGA) are generally easy to evade, which results in some common DGA domain name detection methods failing to effectively detect the DGA domain name. To solve the issues, we propose a DGA domain name detection method based on two-stage feature reinforcement. Firstly, we encode the domain name to obtain the domain name word vector. Secondly, the slice pyramid network (SPN) is used to process the word vector to extract the domain name feature. Thirdly, we reinforce the domain name feature by using the two-stage reinforcement method we proposed. The two-stage reinforcement method reinforces the domain name feature by adding domain name semantic information to the extracted features and reducing feature information redundancy to improve the stability of the domain name feature, meanwhile, we convert the reinforced domain name feature to the primary capsules to reduce feature loss. Finally, we use the dynamic routing algorithm to process the primary capsules to generate digital capsules, and then the digital capsules are used to detect domain names. Experimental results on domain name detection and domain name family classification both show that compared with the state-of-the-art methods, our method has better detection performances.

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.

Aiming at the fact that the existing source code vulnerability detection methods did not explicitly maintain the semantic information related to the vulnerability in the source code, which made it difficult for the vulnerability detection model to extract the vulnerability sentence features and had a high detection false positive rate, a source code vulnerability detection method based on the vulnerability dependency graph is proposed. Firstly, the candidate vulnerability sentences of the function were matched, and the vulnerability dependency representation graph corresponding to the function was generated by analyzing the multi-layer control dependencies and data dependencies of the candidate vulnerability sentences. Secondly, abstracted the function name and variable name of the code sentences node and generated the initial representation vector of the code sentence nodes in the vulnerability dependency representation graph. Finally, the source code vulnerability detection model based on the heterogeneous graph transformer was used to learn the context information of the code sentence nodes in the vulnerability dependency representation graph. In this paper, the proposed method was verified on three datasets. The experimental results show that the proposed method have better performance in source code vulnerability detection, and the recall rate is increased by 1.50%~22.27%, and the F1 score is increased by 1.86%~16.69%, which is better than the existing methods.

This paper introduces a malware detection method based on the reorganisation of API instruction sequence and image representation in an effort to address the challenges posed by current methods of malware detection in terms of feature extraction and detection accuracy. In the first step, APIs of the same type are grouped into an API block. Each API block is reorganised according to the first invocation order of each type of API. As a measure of the API's devotion to the software sample, the number of API block entries is recorded. Second, the API codes, API devotions, and API sequential indexes are extracted based on the reorganised API instruction sequence to generate the feature image. The feature image is then fed into the self-built lightweight malware feature image convolution neural network. The experimental results indicate that the detection accuracy of this method is 98.66% and that it has high performance indicators and detection speed for malware detection.

The existing botnet detection methods have the problems of uneven sampling, poor feature selection, and weak generalization ability, resulting in low detection and classification results and poor adaptability to the internet of things (IoT) environment with limited computing and storage resources. This paper proposes an IoT botnet detection method using feature reconstruction and interval optimization to solve the above problems. Through the designed address triple and time window-based IP aggregation and feature reconstruction method (ATTW-IP-FR), the network traffic samples obtained from the IoT gateway are integrated, and the flow features are reconstructed to attain the reconstructed sample set. The proposed self-corrected hybrid weighted sampling algorithm balances the normal and botnet flow samples in the reconstructed sample set to get the resampling sample set. The introduced multiattribute decision-making and adjacency relation chain-based sequential forward selection algorithm is applied to eliminate the redundant features in the resampling sample set, and the optimal feature subset is obtained. The resampling sample set filtered by the optimal feature subset is detected and classified through the designed two-stage hybrid heterogeneous model optimized by the intermittent chaos and bald eagle search algorithm-based interval optimization algorithm. The experimental results show that the proposed method effectively detects the botnet in two real IoT scenarios. The detection accuracy is 99.17 % $ \% $ , the Matthews correlation coefficient is 98.35 % $ \% $ , the false positive rate is 0.25 % $ \% $ , and the false negative rate is 1.27 % $ \% $ , which are better than the existing methods. This method can effectively reduce sampling and feature selection time and space overhead and better adapt to the resource-constrained IoT environment.

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.

In this letter, novel models of coherently distributed wideband sources in both time domain and fractional Fourier domain are proposed. For wideband linear frequency-modulated (LFM) source, the one-to-one relationship between the location vector in the Dechirping domain and the spatial parameters is given. Then, the Multiple Signal Classification (MUSIC) type algorithm for distributed narrowband source localization is exploited to estimate the central angle and the extension width of multiple distributed LFM sources in the Dechirping domain. The simulations demonstrate the proposed algorithm can determine the number of incident sources which in this algorithm is allowed to exceed the number of sensors in the array and obtain high performance in location position accuracy and anti-noise property.