As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method’s efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy.

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.

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to address the issues caused by the manual identification of plant diseases, which is time-consuming and has low detection accuracy. This paper proposes the development of a robust Deep Ensemble Convolutional Neural Network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative analysis was then conducted on the enlarged dataset of sugarcane leaf diseases, which was divided into six categories and 4800 images. The application of data augmentation, along with the addition of dense layers, batch normalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 scores for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom-weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, which outperformed the individual performance of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases.

With the increasingly severe challenge of Software Supply Chain (SSC) security, the rising trend in guarding against security risks has attracted widespread attention. Existing techniques still face challenges in both accuracy and efficiency when detecting malware in SSC. To meet this challenge, this paper introduces two novel models, named the Bayesian Optimization-based Support Vector Machine (BO-SVM) and the Long Short-Term Memory–BO-SVM (LSTM-BO-SVM). The BO-SVM model is constructed on an SVM foundation, with its hyperparameters optimized by Bayesian Optimization. To further enhance its accuracy and efficiency, the LSTM-BO-SVM model is proposed, building upon BO-SVM and employing LSTM networks for pre-classification. Extensive experiments were conducted on two datasets: the balanced ClaMP dataset and the unbalanced CICMalDroid-2020 dataset. The experimental results indicate that the BO-SVM model is superior to other models in terms of accuracy; the accuracy of the LSTM-BO-SVM model on the two datasets is 98.2% and 98.6%, respectively, which is 2.9% and 2.2% higher than that of the BO-SVM on these two datasets.

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.