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检索条件"主题词=Graph Pattern Matching"

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IPMES: A Tool for Incremental TTP Detection over the System Audit Event Stream 54

IPMES: A Tool for Incremental TTP Detection over the System ...

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54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)

作者： Li, Hong-Wei Liu, Ping-Ting Lin, Bo-Wei Liao, Yi-Chun Huang, Yennun Acad Sinica CITI Taipei Taiwan Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9798350341065;9798350341058

Advanced persistent threat (APT) cyberattacks are serious threats to corporations and governments. The prolong dwell time associated with APTs significantly increase the difficulty on detecting them in provenance graphs. To reduce the detection complexity, some works have demonstrated the effectiveness of employing pattern matching on provenance graphs in conjunction with APT lifecycle models to pinpoint short-duration attack steps, also known as "tactics, techniques, and procedures" (TTPs). However, when dealing with more complex TTPs, particularly those involving graph-based and partial ordering, few tools can incrementally and efficiently handle them. In this paper, we present IPMES1, a tool that has been publicly released to address this gap. By leveraging specific optimizations, it provides efficient incremental matching for those TTPs, and can handle practical system audit event streams. Experiments conducted on synthetic and real-world data demonstrated the practical feasibility of IPMES in TTP detection.

关键词： TTP detection graph pattern matching incremental pattern matching system audit event

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Scaling Topology pattern matching: A Distributed Approach 18

Scaling Topology Pattern Matching: A Distributed Approach

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33rd Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Stein, Michael Froemmgen, Alexander Kluge, Roland Wang, Lin Wilberg, Augustin Koldehofe, Boris Muehlhaeuser, Max Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9781450351911

graph pattern matching in network topologies is a building block of many distributed algorithms. Based on a limited local view of the topology, pattern-based algorithms substantiate the decision-making of each device on the occurrence of graph patterns in its surrounding topology. Existing pattern-based algorithms require that each device has a sufficiently large local view to match patterns without support of other devices. In practical environments, the local view is often restricted to one hop. Thus, algorithms matching non-trivial patterns are locked out from such environments today. This paper presents the first algorithm for distributed topology pattern matching, enabling pattern matching beyond the local view. Outgoing from initiating devices, our pattern matcher delegates the matching procedure to further devices in the network. Exploring major contextual parameters of our algorithm, we show that the optimal local view size depends on scenario-specific conditions. Our pattern matcher provides the flexibility for adaptations of the local view size at runtime. Making use of this flexibility, we optimize the execution of an established pattern-based algorithm and evaluate our pattern matcher in two topology control case studies for the Internet of Things. By scaling the view size of each device in a distributed way, our adaptive approach achieves significant communication cost savings in face of dynamic conditions.

关键词： graph pattern matching locality control topology control

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Answering graph pattern Query using Incremental Views

Answering Graph Pattern Query using Incremental Views

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Singh, Komal Singh, Vikram Natl Inst Technol Dept Comp Sci Kurukshetra 136119 Haryana India

ISBN: (纸本)9781509016662

In recent years, modeling data in graph structure became evident and effective for processing in some of the prominent application areas like social analytics, health care analytics, scientific analytics etc. The key sources of massively scaled data are petascale simulations, experimental devices, the internet and scientific applications. Hence, there is a demand for adapt graph querying techniques on such large graph data. graphs are pervasive in large scale analytics, facing the new challenge such as data size, heterogeneity, uncertainty and data quality. Traditional graph pattern matching approaches are based on inherent isomorphism and simulation. In real life applications, many of them either fail to capture structural or semantic or both similarities. Moreover, in real life applications data graphs constantly bear modifications with small updates. In response to these challenges, we propose a notion that revises traditional notions to characterize graph pattern matching using graph views. Based on this characterization, we outline an approach that efficiently solve graph pattern queries problem over both static and dynamic real life data graphs.

关键词： graph pattern matching Subgraph isomorphism graph simulation Incremental pattern matching Incremental views Query Answering

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SISA: Set-Centric Instruction Set Architecture for graph Mining on Processing-in-Memory Systems 21

SISA: Set-Centric Instruction Set Architecture for Graph Min...

引用

54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Besta, Maciej Kanakagiri, Raghavendra Kwasniewski, Grzegorz Ausavarungnirun, Rachata Beranek, Jakub Kanellopoulos, Konstantinos Janda, Kacper Vonarburg-Shmaria, Zur Gianinazzi, Lukas Stefan, Ioana Gomez-Luna, Juan Copik, Marcin Kapp-Schwoerer, Lukas Di Girolamo, Salvatore Blach, Nils Konieczny, Marek Mutlu, Onur Hoefler, Torsten Swiss Fed Inst Technol Zurich Switzerland IIT Tirupati Tirupati Andhra Pradesh India King Mongkuts Univ Technol North Bangkok Bangkok Thailand Tech Univ Ostrava Ostrava Czech Republic AGH Univ Sci & Technol Lublin Poland

ISBN: (纸本)9781450385572

Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-bound and thus could be accelerated by hardware techniques such as Processing-in-Memory (PIM). However, they also come with non-straightforward parallelism and complicated memory access patterns. In this work, we address this problem with a simple yet surprisingly powerful observation: operations on sets of vertices, such as intersection or union, form a large part of many complex graph mining algorithms, and can offer rich and simple parallelism at multiple levels. This observation drives our cross-layer design, in which we (1) expose set operations using a novel programming paradigm, (2) express and execute these operations efficiently with carefully designed set-centric ISA extensions called SISA, and (3) use PIM to accelerate SISA instructions. The key design idea is to alleviate the bandwidth needs of SISA instructions by mapping set operations to two types of PIM: in-DRAM bulk bitwise computing for bitvectors representing high-degree vertices, and near-memory logic layers for integer arrays representing low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and outperform hand-tuned baselines, offering more than 10x speedup over the established Bron-Kerbosch algorithm for listing maximal cliques. We deliver more than 10 SISA set-centric algorithm formulations, illustrating SISA's wide applicability.

关键词： graph Mining graph pattern matching graph Learning Clique Mining Clique Listing Clique Enumeration Subgraph Isomorphism Parallel graph Algorithms Processing In Memory Processing Near Memory graph Accelerators Instruction Set Architecture

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AutoMine: Harmonizing High-Level Abstraction and High Performance for graph Mining 19

AutoMine: Harmonizing High-Level Abstraction and High Perfor...

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27th ACM Symposium on Operating Systems Principles (SOSP)

作者： Mawhirter, Daniel Wu, Bo Colorado Sch Mines Golden CO 80401 USA

ISBN: (纸本)9781450368735

graph mining algorithms that aim at identifying structural patterns of graphs are typically more complex than graph computation algorithms such as breadth first search. Researchers have implemented several systems with high-level and flexible interfaces customized for tackling graph mining problems. However, we find that for triangle counting, one of the simplest graph mining problems, such systems can be several times slower than a single-threaded implementation of a straightforward algorithm. In this paper, we reveal the root causes of the severe inefficiencies of state-of-the-art graph mining systems and the challenges to address the performance problems. We build AutoMine, a single-machine system to provide both highlevel interfaces and high performance for large-scale graph mining applications. The novelty of AutoMine comes from 1) a new representation of subgraph patterns and 2) compilation techniques that automatically generate efficient mining code with minimized memory consumption from a highlevel abstraction. We have extensively evaluated AutoMine against 3 graph mining systems on 8 real-world graphs of different scales. Our experimental results show that AutoMine often produces several orders of magnitude better performance and can process very large graphs existing systems cannot handle.

关键词： graph mining graph pattern matching compiler

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Scalable pattern matching over Compressed graphs via Dedensification 16

Scalable Pattern Matching over Compressed Graphs via Dedensi...

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22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Maccioni, Antonio Abadi, Daniel J. Roma Tre Univ Rome Italy Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450342322

One of the most common operations on graph databases is graph pattern matching (e.g., graph isomorphism and more general types of "subgraph pattern matching"). In fact, in some graph query languages every single query is expressed as a graph matching operation. Consequently, there has been a significant amount of research effort in optimizing graph matching operations in graph database systems. As graph databases have scaled in recent years, so too has recent work on scaling graph matching operations. However, the performance of recent proposals for scaling graph pattern matching is limited by the presence of high-degree nodes. These high-degree nodes result in an explosion of intermediate result sizes during query execution, and therefore significant performance bottlenecks. In this paper we present a dedensification technique that losslessly compresses the neighborhood around high-degree nodes. Furthermore, we introduce a query processing technique that enables direct operation of graph query processing operations over the compressed data, without ever having to decompress the data. For pattern matching operations, we show how this technique can be implemented as a layer above existing graph database systems, so that the end-user can benefit from this technique without requiring modifications to the core graph database engine code. Our technique reduces the size of the intermediate result sets during query processing, and thereby improves query performance.

关键词： power-law graph databases big graphs graph pattern matching scale-free graphs rdf sparql

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Finding risk patterns in cloud system models 12

Finding risk patterns in cloud system models

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12th IEEE International Conference on Cloud Computing (IEEE CLOUD) held as Part of IEEE World Congress on Services (IEEE SERVICES)

作者： Kunz, Florian Mann, Zoltan Adam Univ Duisburg Essen Paluno Ruhr Inst Software Technol Essen Germany

ISBN: (纸本)9781728127057

The risk of unauthorized access to confidential data is a major problem in cloud computing. In previous work, the notion of risk patterns was introduced to capture configurations of cloud systems that are prone to data protection issues. In this paper, we devise a program for the automatic detection of risk patterns in cloud system models. Our program makes use of the Eclipse Modeling Framework, the model transformation library Henshin, and the modeling workbench Sirius to (i) enable security experts to describe cloud risk patterns in a compact way, (ii) enable the efficient automatic detection of risk patterns in the model of a cloud system, and (iii) support cloud experts in experimenting with the security implications of different cloud configurations. A case study and experiments demonstrate the applicability and scalability of the proposed approach.

关键词： cloud computing security risk management data protection graph pattern matching run-time model

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Towards the Scheduling of Vertex-constrained Multi Subgraph matching Query 20

Towards the Scheduling of Vertex-constrained Multi Subgraph ...

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Hao, Kongzhang Lai, Longbin UNSW Sydney NSW Australia Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450367356

Subgraph matching is one of the most fundamental problems in graph database, which is associated with a wide spectrum of applications. Researchers have primarily devoted their efforts to improving performance for individual query, while we often need to compute multiple queries all at once in practice. In this paper, we study the problem of Vertex-constrained Multi Subgraph matching Query (vMSQ), where we propose a novel scheduling algorithm for processing multiple queries in parallel, while taking into considerations of load balance and maximum possible sharing of computation.

关键词： graph pattern matching query planning query optimization graph database

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Anomaly Subgraph Mining in Large-Scale Social Networks 17

Anomaly Subgraph Mining in Large-Scale Social Networks

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IEEE Int Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Chen, Shengnan Qian, Jianmin Chen, Haopeng Liu, Si Shanghai Jiao Tong Univ Shanghai Peoples R China Boc Financial Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781728143286

Anomaly detection is a relevant research field in online social networks (OSNs), which detects the abnormal individuals and activities to protect other regular activities of the whole network. Current detection approaches can be divided into two types: structure-based automatic detection and experience-based manual detection. However, the first approach may have inaccurate detection results and neglect some abnormal nodes;The second one needs a great deal of experts' experience and needs such high labor costs. In this paper, we design a training-based graph pattern matching systems (T-GPM) to detect abnormal individuals in large-scale social networks. T-GPM first mines the feature patterns by training some existed abnormal samples. Then, by applying the feature patterns to the original social networks, T-GPM can find out all the similar abnormal nodes or subgraphs. Our experiments show that T-GPM can get essential features of the abnormal sample without extra experts' experience. Meanwhile, the accuracy of T-GPM increases with the number of iterations, and it achieves the final accuracy rate of more than 90%. Finally, the entire system has only a small amount of labor cost for subgraph filtering in the predicting phase.

关键词： anomaly detection social network subgraph mining graph pattern matching

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POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting 19

POIROT: Aligning Attack Behavior with Kernel Audit Records f...

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ACM SIGSAC Conference on Computer and Communications Security (CCS)

作者： Milajerdi, Sadegh M. Eshete, Birhanu Gjomemo, Rigel Venkatakrishnan, V. N. Univ Illinois Chicago IL 60680 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450367479

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.

关键词： Cyber Threat Hunting Cyber Threat Intelligence Indicator of Compromise graph Alignment graph pattern matching

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