Bo Li

The increasing volume of time series signals and the scarcity of labels make time series anomaly detection (TSAD) a natural fit for self-supervised deep learning. However, existing normality-based approaches face two key limitations: relying on a single assumption often fails to capture the whole normality, leading to biased representations; and they typically presume clean training data, which is unrealistic in practice and undermines model robustness. In this article, we propose RoCA, a unified and robust anomaly detection (AD) framework that simultaneously addresses assumption incompleteness and data contamination. The key insight is that normal samples tend to satisfy multiple normality assumptions, whereas anomalous or contaminated samples should violate at least one. RoCA employs a composite loss function consisting of a multinormality alignment term, a dynamic anomaly-aware term, and a variance term to maintain training stability. This design enables RoCA to dynamically discover and push away latent anomalies during training to refine the boundary, eliminating the dependence on precisely labeled, high-purity training data. Extensive experiments on both univariate and multivariate time series datasets demonstrate that RoCA consistently outperforms state-of-the-art methods, achieving up to 7.3% improvement under real-world contamination. Our theoretical analysis further reveals the intrinsic synergy between contrastive learning (CL) and one-class classification (OC) under the RoCA framework. The source code is available at the anonymous repository https://github.com/ruiking04/RoCA.

Graph Neural Networks (GNNs) have found widespread application in malware detection tasks in recent years, aiming to uncover the malicious nature of target processes by aggregating neighborhood information via different relations. However, current GNN-based malware detection models are primarily designed for static graphs or fixedwindow dynamic graphs; they often overlook the correlation between distinct continuous behaviors and specific attacks (e.g., code injection), leading to a disruption in the attack's continuity and incurring substantial computing overhead. This paper introduces a novel model dubbed DynMD, designed to enhance the efficiency of malware detection on streaming behavioral data via energy-based dynamic graph representation learning. Concretely, DynMD investigates an energy function to adaptively partition windows, facilitating the construction of a lossless malware dynamic heterogeneous graph. More importantly, DynMD proposes a unique time-aware dynamic graph learning method to capture energy-concordant dynamic neighborhoods. This approach involves both intra-graph and inter-graph message propagation, promoting dynamic graph learning while avoiding time-consuming and repetitive computations. Experimental results on three real-world malware datasets show that DynMD improves detection accuracy by up to 3.99% and achieves detection 3.81× to 5.33× faster than the MG-DVD framework, identifying malware within an average of 40 seconds of execution.

Malicious traffic detection is one of the main challenges in the field of cybersecurity. Although modern deep learning methods have made progress in identifying malicious traffic, they often overlook the persistent nature of attack behaviors, making it difficult to distinguish between malicious and normal traffic at a single observation point. To address this issue, we propose MalDetectFormer, which aims to accurately capture the spatiotemporal dynamics of malicious traffic. By incorporating a sparse attention mechanism, MalDetectFormer can efficiently focus on key characteristics of traffic nodes while overcoming the challenges faced by traditional long-sequence processing. Additionally, by adopting a time-cyclic attention mechanism, the model can identify and capture persistent attack patterns of malicious traffic. Experiments conducted on benchmark datasets demonstrate the advantages of the proposed MalDetectFormer in both malicious traffic detection and malicious attack recognition tasks.

Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by the interchange of gradients. To achieve privacy-preserving federated learning, integrating secure aggregation mechanisms is essential. Unfortunately, existing solutions are vulnerable to recently demonstrated inference attacks such as the disaggregation attack. This paper proposes TAPFed , an approach for achieving privacy-preserving federated learning in the context of multiple decentralized aggregators with malicious actors. TAPFed uses a proposed threshold functional encryption scheme and allows for a certain number of malicious aggregators while maintaining security and privacy. We provide formal security and privacy analyses of TAPFed and compare it to various baselines through experimental evaluation. Our results show that TAPFed offers equivalent performance in terms of model quality compared to state-of-the-art approaches while reducing transmission overhead by 29%-45% across different model training scenarios. Most importantly, TAPFed can defend against recently demonstrated inference attacks caused by curious aggregators, which the majority of existing approaches are susceptible to.

Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integra

Malware attacks, especially “few-shot” malware, have profoundly harmed the cyber ecosystem. Recently, malware detection models based on graph neural networks have achieved remarkable success. However, these efforts over-rely on sufficient labeled data for model training and thus may be brittle in few-shot malware detection because of the label scarcity. To this end, we propose a self-supervised malware detection framework based on graph contrastive learning and adversarial augmentation, termed A2-CLM, to address the challenge of few-shot malware detection. Particularly, A2-CLM first depicts the malware execution context with a sensitivity heterogeneous graph by assessing the security semantic of each behavior. Afterwards, A2-CLM designs multiple adversarial attacks to generate more practical contrastive pairs, including the PGD attack, attribute masking attack, meta-graph-guide sampling attack, direct system calls attack, and obfuscation attack, which is beneficial to strengthening the model’s effectiveness and robustness. To alleviate the training workload of contrastive learning, we introduce a momentum strategy to train the multiple graph encoders in A2-CLM. Especially on 1-shot detection tasks, A2-CLM achieves performance gains of up to 24.63% and 4.58% against supervised and self-supervised detection methods, respectively.

Foretelling ongoing malware attacks in real time is challenging due to the stealthy and polymorphic nature of their executive behavior patterns. In this paper, we present MalAF, a novel Mal ware A ttack F oretelling framework that utilizes run-time behavior (i.e., sequences of API events) of malware to foretell the attack that has not yet executed. MalAF first samples suspicious API events by assessing the sensitivity of the parameters of each API event and dividing them into multiple attack time slots by calculating the strong correlation. Following that, MalAF employs dynamic heterogeneous graph sequences to incrementally model contextual semantics for each attack time slot, generating malware state sequences in real time. Moreover, MalAF proposes a greedy adaptive dictionary (GAD)-optimized IRL preference learning method to automate the capture of families' intrinsic attack preferences, which achieves higher performance than the existing inverse reinforcement learning (IRL). Additionally, with the guidance of families' attack preferences, MalAF trains an LSTM to foretell the future path of the target malware. Finally, MalAF matches the identified APIs' paths with a malicious capability base and reports the comprehensible attacks to an analyst. The experiments on real-world datasets demonstrate that our proposed MalAF outperforms the state-of-the-art methods, which improves the baseline by 3.01% $\sim$ 4.73% of accuracy in terms of path foretell.