Adversarial attacks meticulously generate minuscule, imperceptible perturbations that add to images to deceive neural networks . Adversarial purification methods seek to remove perturbations using generative models to achieve defense. However, residual perturbations lead to less-than-ideal results. Under the premise that perturbations are difficult to remove completely, we are the first to quantify the hazards of residual perturbations and explore how to achieve more robust defenses by reducing perturbations and resisting the impact of residual perturbations. Motivated by this, we propose a novel adversarial purification approach named Information Mask Purification (IMPure). Our method utilizes informative masks and a regional intersection reconstruction to generate images to reduce the perturbation residues. During training, we use the combination module to guide the generative model in recovering feature representations. Finally, we establish a combined constraint of pixel loss and perceptual loss to augment the model’s reconstruction adaptability. Extensive experiments on the complex dataset ImageNet with classifier models demonstrate that our approach achieves state-of-the-art results in defending against adversarial attack methods. Implementation code and pre-trained weights can be accessed at https://github.com/NoWindButRain/IMPure .

DDoS attacks websites and servers by disrupting network services in an attempt to drain the application's resources. With the explosive growth of electric vehicles (EVs), DDoS attacks have threatened the security of EVs and charging stations. In this work, we developed a novel machine learning model named FCDForest to detect DDoS attacks on the CICEV 2023 dataset. FCDForest employs feature construction and deep forest to detect DDoS attacks on the CICEV 2023 dataset. The feature construction is used to construct features, and the deep forest is used as the classification model to detect DDoS attacks. This study selected six existing models as comparison models of FCDForest. In light of our experiments, FCDForest achieved the highest accuracy of 0.94 on the CICEV 2023 dataset. Our experiments indicated that FCDForest is feasible for DDoS attack detection, and feature construction method can improve models’ performance on DDoS attack detection.

When deep learning models are employed in Network Intrusion Detection Systems (NIDSs) to cope with a variety of rising attacks from network, the interpretability of these applications are not studied adequately, which result in the uncertainty of their classification basis and also can not give the warning for how to improve model decisions. In this paper, a new framework is designed to provide a NIDS with visual and quantitative analysis, including a modified ensemble Convolutional Neural Network (CNN) model and a novel explainable method. The ensemble model is used as a feature extractor and aims to make classification. The explainable method in combination with Gradient-weighted Class Activation Mapping (Grad-CAM) is made to calculate feature importance of network traffic from the aspect of spatial relations, and find out the key features for improving model performance. The results of the experiments on NSL-KDD and UNSW-NB15 datasets demonstrate that the new framework, which has a high accuracy comparing with the existing models, can explain the feature importance effectively, and also improve model performance.

With the advancement of big data, microproces-sors, and other applications, the Internet of Things (IoT) has seen significant development. Owing to the lack of necessary security defense mechanisms, IoT devices are susceptible to being targeted and controlled by attackers. They can manipu-late a vast array of IoT devices to launch DDoS attacks on the network infrastructure of a country or region, leading to serious economic losses and social security risks. Intrusion detection methods based on deep learning generally rely on numerous high-quality training instances, making it difficult to apply them to network traffic lacking sufficient labeled data. Traditional machine learning (ML) methods have limited capabilities in extracting and representing features from high-dimensional data, making it challenging to discover underlying structures and patterns in the data. To address the above issues, this paper proposes an intrusion detection system (IDS) that integrates the Fast Independent Component Analysis (FastICA) module and the multi-Grained Cascade Forest (GcForest). By utilizing FastICA to preprocess the raw data and extract significant features, as well as improving the model's feature extraction capabilities with the aid of cascade modules. Experiments have indicated that FastICA-GcF achieves the binary classification accuracy of 99.95% on CIC-DDoS-2019, and the multiclass classification accuracy of 98.77%, outperforming existing DDoS attack detection models.

In recent years, electric vehicles (EV) have developed greatly and have begun to gradually replace traditional cars. With this development, the potential security threats faced by electric vehicle network systems are also increasing. To cope with these threats in the EV network, in the paper we propose an auxiliary attention filtering module (AAFM) that cooperates with an ensemble convolutional neural network (CNN). AAFM combines the attention of features from different dimensions, filters irrelevant features, and compensates for the output of the model. The latest CICEV2023 dataset is used for training and evaluation. Experiments demonstrate that AAFM can effectively improve model performance and deal with attacks in extreme situations compared to 8 classic and effective intrusion detection models. AAFM-Net achieves over 90% accuracy.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.

MOTIVATION: Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. RESULTS: We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. AVAILABILITY AND IMPLEMENTATION: MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.