With the open-source development of cyber-attack technologies, attackers’ ability to modify existing strategies and exploit vulnerabilities has increased, leading to numerous unknown cyber-attacks. Traditional detection methods face two main challenges: (a) requiring abundant labeled attack samples, which deep learning-based detection methods find difficult to obtain in practice, and (b) struggling to effectively detect novel and previously unseen attacks, especially those that are unknown. In this paper, we propose Exploratory Detection of Unknown Cyber-Attacks via Evolutionary Strategy and Machine Learning. Specifically, firstly, we train kernel-based Ramp-OCSVM models on full features of known attacks to derive class-specific thresholds, while inferring unknown attack thresholds via Gaussian distribution. Next, we define known sample features as “genes” and generate evolutionary feature representations through multi-strategy evolution. Subsequently, these features are processed by the trained Ramp-OCSVM and the thresholds to separate known-attack variants from unknown samples. Finally, we iteratively train a RF classifier using evolved features, selecting the optimal iteration-trained model based on detection performance. We conducted extensive experiments on authoritative datasets. The results achieves F1 scores of 82.70% and 87.64% for detecting unknown attack under different configurations. The mean F1 scores improve to 99.84% and 95.80% for detecting known and unknown attacks in the few-shot learning scenario. Compared to SOTA methods, our proposed method achieves an increase of 2.19% in the F1 score, while demonstrating 53.99% higher F1-score than detection methods via GAN and VAE.

The integration of multi-omics data has emerged as a powerful approach to elucidate interactions across different biological levels. However, throughput limitations and high costs of sequencing technologies often result in sparse multi-omics datasets, where only a subset of samples contains complete omics profiles - a challenge known as the “block missing”. To address this challenge, we propose OmiImp, a novel computational framework based on an improved generative adversarial network (GAN) for cross-omics data imputation. Through performance evaluation on independent datasets, we demonstrate that OmiImp outperforms existing state-of-the-art imputation methods while maintaining stable performance across different missing rates. In addition, we perform enrichment analysis and the results demonstrate that differential expressed features are uniformly distributed across pathways, and the synthetic data retains utility in diverse prognostic analyses. Collectively, this methodological advancement facilitates more reliable multi-omics integration studies, particularly when handling incomplete datasets.

MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is urgent to develop efficient computational methods for predicting potential miRNA-disease associations to reduce the cost and time associated with biological wet experiments. In addition, high-quality feature representation remains a challenge for miRNA-disease association prediction using graph neural network methods. In this paper, we propose a method named ESGC-MDA, which employs an enhanced Simple Graph Convolution Network to identify miRNA-disease associations. We first construct a bipartite attributed graph for miRNAs and diseases by computing multi-source similarity. Then, we enhance the feature representations of miRNA and disease nodes by applying two strategies in the simple convolution network, which include randomly dropping messages during propagation to ensure the model learns more reliable feature representations, and using adaptive weighting to aggregate features from different layers. Finally, we calculate the prediction scores of miRNA-disease pairs by using a fully connected neural network decoder. We conduct 5-fold cross-validation and 10-fold cross-validation on HDMM v2.0 and HMDD v3.2, respectively, and ESGC-MDA achieves better performance than state-of-the-art baseline methods. The case studies for cardiovascular disease, lung cancer and colon cancer also further confirm the effectiveness of ESGC-MDA.

With the continuous development of cyber-attack technologies, attackers increasingly exploit zero-day vulnerabilities or leverage emerging techniques to launch sophisticated attacks, resulting in the persistent emergence of unknown cyber-attacks. However, traditional DL-based cyber-attack detection methods heavily rely on large-scale labeled training data. In practice, obtaining sufficient samples of unknown attacks is challenging, which makes it difficult for these methods to effectively defend against unknown cyber-attacks. In this paper, we propose a method for discovering unknown cyber threats empowered by genetic evolution without prior knowledge. Specifically, We, first mapped the network feature space into a gene framework, and divided the attack genes into a static gene region (SGZ) and a dynamic gene region (DGZ) according to the importance of the cyber-attack genes. Subsequently, leveraging the known attack genes, we utilized different gene evolution strategies and a Convolutional Autoencoder (CAE) to generate attack variants and potential unknown attack genes. Finally, we constructed a cyber-attack detection model incorporating both the global attention mechanism (GAM) and the local attention mechanism (LAM). The generated attack variants and unknown attack genes are the used to enhance the detection ability of the detection model for variants and unknown cyber-attacks. We conducted a large number of experiments on six real and authoritative network datasets. The experimental results show that in different scenario settings, the F1 scores of our proposed method for detecting unknown attacks are 84.64% and 95.77% respectively. The F1 score for detecting unknown attacks on the UNSW-NB15 dataset exceeds that of the baseline classifier. The F1 score for detecting unknown attacks on the CSE-CIC-IDS2018 dataset is 98.85%. In comparison with SOTA methods, the average F1 score is improved by 3.14%. In the evaluation of variant detection performance, the generation method we proposed improves the detection of variants by approximately 11.2%, surpassing generation methods such as the Conditional Generative Adversarial Network (CGAN) and the Variational Autoencoder (VAE). Meanwhile, we also comprehensively evaluated the generalization ability of our proposed method and the evolution ability of different evolution strategies on different datasets and through ablation experiments.

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity, proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times faster than the latest zk-SNARK-based non-interactive method vCNN, and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approaches can be used to verify integrity, they are difficult to use for larger CNN models like LeNet-5 and VGG16, due to the large cost (in terms of time and storage) of generating a proof. This paper proposes ValidCNN, which can efficiently generate integrity proofs based zk-SNARK. At the heart of ValidCNN, it is a novel usage of Freivald's concepts for circuit construction, and a more efficient way for verifying matrix multiplication. Our experimental results demonstrate that VaildCNN significantly outperforms the state-of-the-art approaches that are based on zk-SNARK. For example, compared with ZEN, VaildCNN achieves a 12-fold improvement in time and a 31-fold improvement in storage. Compared with vCNN, VaildCNN achieves a 195-fold and 279-fold improvement in time and storage respectively.

Android malware and its variants are a major challenge for mobile platforms. However, there are two main problems in the existing detection methods:a) The detection method lacks the evolution ability for Android malware, which leads to the low detection rate of the detection model for malware and its variants.b) Traditional detection methods require centralized data for model training, however, the aggregation of training samples is limited due to the infectivity of malware and growing data privacy concerns, centralized detection methods are difficult to be applied in actual detection scenarios. In this paper, we propose FEDriod, a comprehensive Android malware detection method based on federated learning architecture that protects against growing Android malware or emerging Android malware variants. Specifically, we employ genetic evolution strategy to simulate the evolution of Android malware and develop potential malware variants from typical Android malware. Then, we customize the Android malware detection model based on residual neural network to achieve high detection accuracy. Finally, to achieve the protection sensitive data, we develope a federated learning framework to allows multiple Android malware detection agencies to jointly build a comprehensive Android malware detection model. We comprehensively evaluate the performance of FEDriod on the CIC, Drebin, and Contagio authoritative datasets. Experimental results show that our local model outperforms all baseline classifiers. In the federal scenario, our proposed method is superior to the state-of-the-art detection methods, especially in the cross-dataset evaluation, the F1 of FEDriod is 98.53%. More important, we performed genetic evolution experiments on the Drebin dataset, and the results showed that our proposed method has the ability to detect Android malware variants.