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.

Multiple-input–multiple-output (MIMO) array near-field imaging technology, due to its high resolution, has great potential for development in the fields of nondestructive testing, medical imaging, and security inspection. However, currently available imaging algorithms present challenges, as they either require excessive hardware processor memory capacity or lead to prolonged imaging processing time. In this article, a fast Fourier-based near-field 3-D imaging algorithm for the MIMO array is proposed, and it requires less hardware processor memory capacity. Based on the feature that the target image can be obtained by integrating the data corresponding to all transceiver arrays, the data corresponding to all transceiver elements are split into the data corresponding to individual transmitting elements in a targeted manner and then imaged. After that, the final 3-D image is obtained by integrating the images corresponding to all transmitting elements. It effectively reduces the requirement for hardware processor memory capacity. The horizontal and vertical dimensional Fourier transforms (FTs) of all receiving element data corresponding to a single transmitting element are performed, which converts the spatial-domain data to the wavenumber domain. It effectively improves the computational efficiency. At the same time, the time-domain data are converted to the frequency domain by FT, and the phase compensation can be performed only on the peak data corresponding to the target. Therefore, it also effectively improves the computational efficiency. Simulation and experimental results show that compared with the commonly used algorithms, the proposed algorithm can not only obtain similar image quality but also has higher computational efficiency.

Two-dimensional (2D) large-plane multiple-input-multiple-output (MIMO) array antennas with wide coverage can obtain high-resolution three-dimensional (3D) images, which have great potential for applications in security surveillance, medical diagnostics, and nondestructive evaluation. Cross MIMO arrays with low hardware cost and complexity are one of the effective choices for 2D large-plane MIMO arrays. However, the current imaging algorithms corresponding to the cross MIMO arrays are computationally intensive. In this paper, a fast wavenumber domain 3D near-field imaging algorithm for cross MIMO array is proposed. According to signal characteristics of the cross MIMO array, the signal is effectively expressed in the convolution form. The convolution signal is converted to wavenumber domain to achieve horizontal and pitch direction imaging, and wavenumber domain operation can effectively improve computational efficiency. Meanwhile, this algorithm uses distance approximation reasonably according to the distance characteristics of the actual scene, greatly improving the computational efficiency. Simulation and experimental results show that, featuring faster computational efficiency, the proposed algorithm can obtain images that have the similar quality as the images obtained by the commonly used algorithm.

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.