Image conversion of malicious binaries, or binary visualisation, is a relevant approach in the security community. Recently, it has exceeded the role of a single-file malware analysis tool and has become a part of Intrusion Detection Systems (IDSs) thanks to the adoption of Convolutional Neural Networks (CNNs). However, there has been little effort toward image segmentation for the converted images. In this study, we propose a novel method that serves a dual purpose: (a) it enhances colour and pattern segmentation, and (b) it achieves a sparse representation of the images. According to this, we considered the R, G, and B colour values of each pixel as respective fuzzy sets. We then performed α-cuts as a defuzzification method across all pixels of the image, which converted them to sparse matrices of 0s and 1s. Our method was tested on a variety of dataset sizes and evaluated according to the detection rates of hyperparameterised ResNet50 models. Our findings demonstrated that for larger datasets, sparse representations of intelligently coloured binary images can exceed the model performance of unprocessed ones, with 93.60% accuracy, 94.48% precision, 92.60% recall, and 93.53% f-score. This is the first time that α-cuts were used in image processing and according to our results, we believe that they provide an important contribution to image processing for challenging datasets. Overall, it shows that it can become an integrated component of image-based IDS operations and other demanding real-time practices.

/pdf/image-based-malware-detection-using-a-cuts-and-binary-2q9xr4vj.pdf

Image-Based Malware Detection Using α-Cuts and Binary Visualisation

Abstract Software Defect Detection (SDD) has always been critical to the development life cycle. A stable defect detection system can not only alleviate the workload of software testers but also enhance the overall efficiency of software development. Researchers have recently proposed various artificial intelligence-based SDD methods and achieved significant advancements. However, these methods still exhibit limitations in terms of reliability and usability. Therefore, we introduce MSDD-(IA) 3 , a novel framework leveraging the pre-trained CodeT5+ and (IA) 3 for parameter-efficient multi-classification SDD. This framework constructs a detection model based on pre-trained CodeT5+ to generate code representations while capturing defect-prone features. Considering the high overhead of pre-trained LLMs, we injects (IA) 3 vectors into specific layers, where only these injected parameters are updated to reduce the training cost. Furthermore, leveraging the properties of the pre-trained CodeT5+, we design a novel feature sequence that enriches the input data through the combination of source code with Natural Language (NL)-based expert metrics. Our experimental results on 64K real-world Python snippets show that MSDD-(IA) 3 demonstrates superior performance compared to state-of-the-art SDD methods, including PM2-CNN, in terms of F1-weighted, Recall-weighted, Precision-weighted, and Matthews Correlation Coefficient. Notably, the training parameters of MSDD-(IA) 3 are only 0.04% of those of the original CodeT5+. Our experimental data and code can be available at ( https://gitee.com/wxyzjp123/msdd-ia3/ ). 

pdf/parameter-efficient-multi-classification-software-defect-1gx4bc3muf.pdf

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Exploring malware complexities: a behavior and characteristic analysis approach for robust and accurate cybersecurity

The classification of encrypted traffic is critical for network security, yet it faces a significant “few-shot” challenge as novel applications with scarce labeled data continuously emerge. This complexity arises from the high-dimensional, noisy nature of traffic data, making it difficult for models to generalize from few examples. Existing paradigms, such as meta-learning from scratch or standard pre-train/fine-tune methods, often fail in this scenario. To address this gap, we propose Contrastive Learning Meta-Flow (CL-MetaFlow), a novel two-stage learning framework that uniquely synergizes the strengths of contrastive representation learning and meta-learning adaptation. In the first stage, a robust feature encoder is pre-trained using supervised contrastive learning on known traffic classes, shaping a highly discriminative and metric-friendly embedding space. In the second stage, this pre-trained encoder initializes a Prototypical Network, enabling rapid and effective adaptation to new, unseen classes from only a few samples. Extensive experiments on a benchmark dataset (ISCX-VPN-2016 &amp; ISCX-Tor-2017) demonstrate the superiority of our approach. Notably, in a five-way five-shot setting, CL-MetaFlow achieves a Macro F1-Score of 0.620, significantly outperforming from-scratch ProtoNet (0.384), a standard fine-tuning baseline (0.160), and strong pre-training counterparts like SimCLR+ProtoNet (0.545) and a re-implemented T-Sanitation (0.591). Our work validates that a high-quality, domain-adapted feature prior is the key to unlocking high-performance few-shot learning in complex network environments, providing a practical and powerful solution for real-world traffic analysis. 

Unlocking Few-Shot Encrypted Traffic Classification: A Contrastive-Driven Meta-Learning Approach

The increasing volume and variants of malware are posing substantial risks to the security of personal privacy and property, which gradually makes malware classification a hot research topic in machine learning. Compared with traditional malware classification methods requiring much prior knowledge to explicitly construct feature representation, CNN-based methods have more strength for implicitly exploring deep representation and achieved better performance. However, there is an obvious long-tailed distribution problem in existing malware datasets, i.e., a small fraction of families occupies most of the samples, while the sample distribution of most families is relatively sparse. The long-tailed distribution will lead to poor generalization performance on families with fewer sample numbers in conventional CNN-based malware classification. In this paper, we propose a malware classification method based on representation learning, called RL4Mal, which utilizes metric learning for better generalization on the tail families while keeping performance on the head families. Besides designing a weighted metric loss, we also introduce data augmentation and a memory bank to expand negative samples for the loss optimization, further promoting a better construction of the representation space for malware classification. We conduct sufficient experiments on the Malimg dataset, proving that RL4Mal effectively solves the long-tailed problem with 99.47% accuracy in tail families while keeping 99.78% accuracy on average.

RL4Mal: Representation learning-based malware classification under long-tailed distribution

Among the signaling pathways that control the stem cell self-renewal and maintenance vs. acquisition of differentiated cell fates, those mediated by receptor tyrosine kinase (RTK) activation are well established as key players. CBL family ubiquitin ligases are negative regulators of RTKs but their physiological roles in regulating stem cell behaviors are unclear. While hematopoietic Cbl/Cblb knockout (KO) leads to a myeloproliferative disease due to expansion and reduced quiescence of hematopoietic stem cells, mammary epithelial KO led to stunted mammary gland development due to mammary stem cell depletion. Here, we examined the impact of inducible Cbl/Cblb double-KO (iDKO) selectively in the Lgr5-defined intestinal stem cell (ISC) compartment. Cbl/Cblb iDKO led to rapid loss of the Lgr5Hi ISC pool with a concomitant transient expansion of the Lgr5Lo transit amplifying population. LacZ reporter-based lineage tracing showed increased ISC commitment to differentiation, with propensity towards enterocyte and goblet cell fate at the expense of Paneth cells. Functionally, Cbl/Cblb iDKO impaired the recovery from radiation-induced intestinal epithelial injury. In vitro, Cbl/Cblb iDKO led to inability to maintain intestinal organoids. Single cell RNAseq analysis of organoids revealed Akt-mTOR pathway hyperactivation in iDKO ISCs and progeny cells, and pharmacological inhibition of the Akt-mTOR axis rescued the organoid maintenance and propagation defects. Our results demonstrate a requirement for Cbl/Cblb in the maintenance of ISCs by fine tuning the Akt-mTOR axis to balance stem cell maintenance vs. commitment to differentiation.

/pdf/cbl-and-cbl-b-ubiquitin-ligases-are-essential-for-intestinal-tvc3o97v.pdf

Cbl and Cbl-b Ubiquitin Ligases are Essential for Intestinal Epithelial Stem Cell Maintenance

PAFE: A lightweight visualization-based fast malware classification method

A massive proliferation of malware variants has posed serious and evolving threats to cybersecurity. Developing intelligent methods to cope with the situation is highly necessary due to the inefficiency of traditional methods. In this paper, a highly efficient, intelligent vision-based malware variants detection method was proposed. Firstly, a bilinear interpolation algorithm was utilized for malware image normalization, and data augmentation was used to resolve the issue of imbalanced malware data sets. Moreover, the paper improved the convolutional neural network (CNN) model by combining multi-scale feature fusion (MFF) and channel attention mechanism for more discriminative and robust feature extraction. Finally, we proposed a hyperparameter optimization algorithm based on the bat algorithm, referred to as HDBA, in order to overcome the disadvantage of the traditional hyperparameter optimization method based on manual adjustment. Experimental results indicated that our model can effectively and efficiently identify malware variants from real and daily networks, with better performance than state-of-the-art solutions.

/pdf/malware-variants-detection-model-based-on-mff-hdba-2ckdgowh.pdf

Malware Variants Detection Model Based on MFF–HDBA

Abstract With the advancement of adversarial techniques for malicious code, malevolent attackers have propagated numerous malicious code variants through shell coding and code obfuscation. Addressing the current issues of insufficient accuracy and efficiency in malicious code classification methods based on deep learning, this paper introduces a detection strategy for malicious code, uniting Convolutional Neural Networks (CNNs) and Transformers. This approach utilizes deep neural architecture, incorporating a novel fusion module to reparametrize the structure, which mitigates memory access costs by eliminating residual connections within the network. Simultaneously, overparametrization during linear training time and significant kernel convolution techniques are employed to enhance network precision. In the data preprocessing stage, a pixel-based image size normalization algorithm and data augmentation techniques are utilized to remedy the loss of texture information in the malicious code image scaling process and class imbalance in the dataset, thereby enhancing essential feature expression and alleviating model overfitting. Empirical evidence substantiates this method has improved accuracy and the most recent malicious code detection technologies. 

A Lightweight Model for Malicious Code Classification Based on Structural Reparameterisation and Large Convolutional Kernels

Correction to: Tri-channel visualised malicious code classification based on improved ResNet

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