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Automatic APT Attack Reconstruction Supporting Lateral Movement 2nd

Automatic APT Attack Reconstruction Supporting Lateral Movem...

2nd International Conference on Security and Information Technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Fan, Chun-I Wu, Wei-Chen Shie, Cheng-Han Kuo, Hsin-Nan Lee, Bo-Yi Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan National Center for High-Performance Computing Hsinchu Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9789819777853

This work proposes a framework for generating datasets that allows users to adjust the APT attack techniques within it. The framework utilizes the MITRE ATT&CK framework to label the attack traffic based on the Tactics, Techniques, and Procedures (TTP), which facilitating researchers in using the datasets for model training. Meanwhile, this work uses the framework to reconstruct the APT29 attack process and collect a dataset to validate the framework’s feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Advanced Persistent Threat (APT) Dataset generation The APT29 attack Threat intelligence

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CoChain: high Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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Mobility-Aware Deep Reinforcement Learning with Seq2seq Mobility Prediction for Offloading and Allocation in Edge computing

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IEEE Transactions on Mobile computing 2024年第6期23卷 6803-6819页

作者： Wu, Chao-Lun Chiu, Te-Chuan Wang, Chih-Yu Pang, Ai-Chun Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan National Taiwan University Institute of Networking and Multimedia Taipei10617 Taiwan National Taiwan University High Performance and Scientific Computing Center Taipei10617 Taiwan

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts: glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a 'glimpse' of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements. © 2002-2012 IEEE.

关键词： Reinforcement learning

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer 29

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Qi Li, Yanlong Du, Wei Ren, Zenghui Liu, Yue Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%. © 2023 IEEE.

关键词： adaptive heterogeneous algorithm MPI+Athread Sunway supercomputer three-dimensional heat conduction problem tridiagonal linear systems

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Toward to Real Low-Resolution Person Re-identification: A New Dataset and Baseline

Toward to Real Low-Resolution Person Re-identification: A Ne...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Sun, Dongting Yang, Lulu Lan, Long Luo, Zhigang College of Computer Science and Technology National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Person re-identification(re-id) aims at querying and identifying the same target pedestrian in multiple non-overlapping cameras. However, in real scenarios, many person images captured by surveillance cameras tend to have low resolution due to camera hardware, shooting distance, viewing angle, etc. Many existing re-id methods mainly address the crossresolution person re-id problem, i.e., the high-low resolution mismatch problem. In contrast, the problem of matching low-resolution (LR) gallery images and query images with each other has been less studied. In this paper, we address this problem by developing a new gun-ball camera-based person re-id dataset and designing a LR re-id baseline model for this dataset to tackle the LR person matching problem. Extensive experiments validate the effectiveness of the proposed LR baseline model. © 2022 IEEE.

关键词： Cameras

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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DISCRETE ENERGY ANALYSIS OF THE THIRD-ORDER VARIABLE-STEP BDF TIME-STEPPING FOR DIFFUSION EQUATIONS

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Journal of Computational Mathematics 2023年第2期41卷 325-344页

作者： Hong-lin Liao Tao Tang Tao Zhou Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing 211106China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

This is one of our series works on discrete energy analysis of the variable-step BDF *** this part,we present stability and convergence analysis of the third-order BDF(BDF3)schemes with variable steps for linear diffusion equations,see,e.g.,[SIAM ***.,58:2294-2314]and[***.,90:1207-1226]for our previous works on the BDF2 *** this aim,we first build up a discrete gradient structure of the variable-step BDF3 formula under the condition that the adjacent step ratios are less than 1.4877,by which we can establish a discrete energy dissipation ***-robust stability and convergence analysis in the L^(2) norm are then *** the mesh robustness means that the solution errors are well controlled by the maximum time-step size but independent of the adjacent time-step *** also present numerical tests to support our theoretical results.

关键词： Diffusion equations Variable-step third-order BDF scheme Discrete gradient structure Discrete orthogonal convolution kernels Stability and convergence

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Accurate Detection of Superspreaders on Multi-Pipeline Switches 10

Accurate Detection of Superspreaders on Multi-Pipeline Switc...

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10th International Conference on computer and Communications, ICCC 2024

作者： Wang, Cenman Zheng, Bowen Yang, Fan Tian, Ye School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory on High Performance Computing Hefei China

In various network monitoring tasks, the detection of superspreaders has become increasingly crucial. However, latest superspreader detection algorithms and data structures assume the target switch with a single pipeline, while state-of-the-art programmable switches actually comprise multiple independent pipelines. In current method for deploying super-spreader detection applications on multi-pipeline switches, a separate measurement data structure instance is employed for each pipeline. However, under multi-path routing, this simplistic approach leads to lower detection accuracy due to hash collisions. To address this issue, in this paper, we present PipelineAlloc, a HyperLogLog-based superspreader detection system designed for multi-pipeline switches. In order to reduce hash collisions in measurement data structures, PipelineAlloc assigns an egress pipeline as the monitor pipeline for each source IP address. PipelineAlloc introduces novel measurement data structures in the ingress pipelines for temporarily storing statistics of the source and destination IP addresses of packets, and uses packets to carry these measurement data to egress pipelines, which serve as the monitor pipelines. The HyperLogLog-based measurement data structures in egress pipelines are updated using data carried by packets. Experiments driven by real-world traffic traces show that PipelineAlloc outperforms existing solutions in terms of detection accuracy. © 2024 IEEE.

关键词： multi-path routing multi-pipeline switch network monitoring su-perspreader

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DNS Service Optimization Through In-Network Caching on Programmable Switches

DNS Service Optimization Through In-Network Caching on Progr...

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International Conference on computer and Communications (ICCC)

作者： Fan Yang Cenman Wang Bowen Zheng Ye Tian Anhui Key Laboratory on High Performance Computing School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The Domain Name System (DNS) plays a crucial role in contemporary internet infrastructure. Despite the geographical distance of public DNS servers posing a challenge in minimizing domain name resolution latency, there is an increasing reliance on these servers for resolving domain names. One effective approach to mitigate this challenge involves caching DNS query results at network nodes. The storage of cached results commonly employs hash tables due to the variable lengths of domain names. However, within network nodes, hash tables encounter limitations in capacity and susceptibility to hash collisions, potentially leading to inaccuracies in cached results. Current conventional caching strategies have not produced satisfactory storage outcomes. This research introduces a hash-based DNS caching mechanism to tackle these issues. The proposed mechanism presents a unique DNS storage structure on routers and suggests two implementation strategies that incorporate fingerprint verification and Bloom filters. Through analysis and extensive simulations utilizing real-world network topology, the results demonstrate that the hash-based DNS caching mechanism introduced in this study outperforms existing methods in cache hit rate and diminishes query latency.

关键词： Analytical models Filters Network topology Computational modeling Fingerprint recognition Cache storage Topology Domain Name System Servers Optimization

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