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Modeling and Study of Defense Outsourcing Against Advanced Persistent Threat Through Impulsive Differential Game Approach

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SSRN 2024年

作者： Qin, Yang Yang, Xiaofan Yang, Lu-Xing Huang, Kaifan School of Big Data & Software Engineering Chongqing University Chongqing400044 China School of Information Technology Deakin University MelbourneVIC3125 Australia

Advanced persistent threat (APT) poses serious threat to organizations with rich digital assets. APT detection programs designed for quickly finding possibly hijacked hosts are now commercially available. This greatly reduces the workload of APT defense. In practice, the identification and repair of APT-hijacked hosts are out of a system administrator’s capability and have to be outsourced to an established cybersecurity firm. Owing to the limited security budget, the APT defense can be outsourced only in a small number of maintenance periods. We refer to the sequence of outsourcing costs paid in these maintenance periods as an impulsive defense (ID) strategy. On the other hand, APT is time-continuous. We refer to the growth rate function of the attack cost over time as a continuous attack (CA) strategy. In the context that the APT actor is strategic and pursues a cost-effective CA strategy, the organization faces the problem of finding a cost-effective ID strategy (the single-impulsive defense (SID) problem). This paper addresses the SID problem through game-theoretic modeling. Based on an impulsive state evolutionary model, the SID problem is boiled down to a single-impulsive differential game model (the SID model). By applying single-impulsive differential game theory, an iterative algorithm of solving the SID problem is presented. The ID strategy obtained by running the algorithm is corroborated to be cost-effective under the Nash equilibrium solution concept. Therefore, we recommend the ID strategy. This work takes the first step toward the theoretic study of APT defense outsourcing in the presence of strategic attacker. © 2024, The Authors. All rights reserved.

关键词： Outsourcing

A Novel Distributed K-Means Clustering Algorithm for big Text data 23

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A Novel Distributed K-Means Clustering Algorithm for Big Tex...

7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Li, Min Li, Meijing Cheng, Yonglong Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Software Development Center Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399883

With the advancement of diverse industries, text clustering has garnered significant attention in fields such as data mining, document retrieval, and pattern classification. The K-Means algorithm, a simple, efficient, and widely adopted clustering approach, has been extensively studied for its applicability in text data analysis. However, the exponential growth of information presents a formidable challenge when clustering large-scale data. The traditional K-Means algorithm proves ineffective in handling high-dimensional, massive text data, and the initial centroid selection significantly impacts the clustering outcomes. To address these limitations, this research introduces a clustering algorithm based on the virtual centroid and nearest centroid strategy. Furthermore, we incorporate the MapReduce parallelization framework to tackle the clustering problem associated with vast text data. Experimental results demonstrate the algorithm's commendable scalability and effective handling of large datasets. © 2023 ACM.

关键词： MapReduce

Akane: Perplexity-Guided Time Series data Cleaning

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Proceedings of the ACM on Management of data 2024年第3期2卷 1-26页

作者： Xiaoyu Han Haoran Xiong Zhenying He Peng Wang Chen Wang X. Sean Wang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China National Engineering Research Center for Big Data Software EIRI Tsinghua University Beijing China

Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

关键词： data cleaning perplexity recurrent patterns time series

Log Parsing: How Far Can ChatGPT Go?

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arXiv 2023年

作者： Le, Van-Hoang Zhang, Hongyu School of Information and Physical Sciences The University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing. © 2023, CC BY.

关键词： software reliability

multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information

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Computers in Biology and Medicine 2025年 191卷 110148-110148页

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. © 2025

关键词： Decoding

Optimal Control of Malware Propagation in IoT Networks

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arXiv 2024年

作者： Jafar, Mousa Tayseer Yang, Lu-Xing Li, Gang Yang, Xiaofan The School of Information Technology Deakin University MelbourneVIC3125 Australia The School of Big Data and Software Engineering Chongqing University Chongqing400044 China

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete (SIHILRFRC) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

Log Parsing with Prompt-based Few-shot Learning

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arXiv 2023年

作者： Le, Van-Hoang Zhang, Hongyu School of Information and Physical Sciences The University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing. © 2023, CC BY.

关键词： Semantics

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

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arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

Intelligent Deposit Product Recommendation for Flexible Employees of Housing Provident Fund Based on Random Forest 23

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Intelligent Deposit Product Recommendation for Flexible Empl...

Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning

作者： Xue Chen Yuxing Hu Xiaoxia Hong Zhaohan Zhu Yang Song Juan Liu Peng Song Wei Xiong Meng Yan Chongqing Housing Provident Fund Management Center China School of Big Data and Software Engineering Chongqing University China School of Eletrical Engineering Southwest Jiaotong University China

ISBN: (纸本)9798400707544

With the rapid development of big data and cloud computing, digital technology has been widely used to develop provident funds. We innovatively apply data mining and machine learning methods to analyze the impact factors of provident fund deposits and the recommendation model of the deposit products for flexible employees. After experiments, our model performs well in the performance evaluation index (such as Accuracy, Recall, and F1-score). Through our model and research analysis, the housing provident fund center can comprehensively analyze and mine the real data, promote more popular customer policies, and provide better customer service.

关键词： big data