With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with Android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect Android malware and improve the detection accuracy, this paper proposes a dynamic detection model for Android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of Android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models.

Vulnerability detection in software source code is crucial in ensuring software security. Existing models face challenges with dataset class imbalance and long training times. To address these issues, this paper introduces a multi-feature screening and integrated sampling model (MFISM) to enhance vulnerability detection efficiency and accuracy. The key innovations include (i) utilizing abstract syntax tree (AST) representation of source code to extract potential vulnerability-related features through multiple feature screening techniques; (ii) conducting analysis of variance (ANOVA) and evaluating feature selection techniques to identify representative and discriminative features; (iii) addressing class imbalance by applying an integrated over-sampling strategy to create synthetic samples from vulnerable code to expand the minority class sample size; (iv) employing outlier detection technology to filter out abnormal synthetic samples, ensuring high-quality synthesized samples. The model employs a bidirectional long short-term memory network (Bi-LSTM) to accurately identify vulnerabilities in the source code. Experimental results demonstrate that MFISM improves the F1 score performance by approximately 10% compared to existing DeepBalance methods and reduces the training time to 2–3 h. These results confirm the effectiveness and superiority of MFISM in source code vulnerability detection tasks.

In the highly interconnected digital ecosystem, cyberspace has become the main battlefield for complex attacks such as Advanced Persistent Threat (APT). The complexity and concealment of APT attacks are increasing, posing unprecedented challenges to network security. Current APT detection methods largely depend on general datasets, making it challenging to capture the stages and complexity of APT attacks. Moreover, existing detection methods often suffer from suboptimal accuracy, high false alarm rates, and a lack of real-time capabilities. In this paper, we introduce LDR-RFECV, a novel feature selection (FS) algorithm that uses LightGBM, Decision Trees (DTs), and Random Forest (RF) as integrated feature evaluators instead of single evaluators in recursive feature elimination algorithms. This approach helps select the optimal feature subset, thereby significantly enhancing detection efficiency. In addition, a novel optimization algorithm called LWHO was proposed, which integrates the Levy flight mechanism with the Wild Horse Optimizer (WHO) to optimize the hyperparameters of the LightGBM model, ultimately enhancing performance in APT attack detection. More importantly, this optimization strategy significantly boosts the detection rate during the lateral movement phase of APT attacks, a pivotal stage where attackers infiltrate key resources. Timely identification is essential for disrupting the attack chain and achieving precise defense. Experimental results demonstrate that the proposed method achieves 97.31% and 98.32% accuracy on two typical APT attack datasets, DAPT2020 and Unraveled, respectively, which is 2.86% and 4.02% higher than the current research methods, respectively.