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9th International Symposium on Digital Forensics and Security (ISDFS)

作者： Gulmez, Sibel Sogukpinar, Ibrahim Gebze Tech Univ Comp Engn Kocaeli Turkey

ISBN: (纸本)9781665444811

The impact of malware grows for IT (information technology) systems day by day. The number, the complexity, and the cost of them increase rapidly. While researchers are developing new and better detection algorithms, attackers are also evolving malware to fail the current detection techniques. Therefore malware detection becomes one of the most challenging tasks in cyber security. To increase the performance of the detection techniques, researchers benefit from different approaches. But some of them might cost a lot both in time and hardware resources. This situation puts forward fast and cheap detection methods. In this context, static analysis provides these utilities but it is important to keep detection accuracy high while reducing resource consumption. Opcodes (operational codes) are commonly used in static analysis but sometimes feature extraction from opcodes might be difficult since an opcode sequence might have a great length. Furthermore, most of the malware developers use obfuscation and encryption techniques to avoid detection methods based on static analysis. This kind of malware is called packed malware and according to common belief, packed malware should be either unpacked or analyzed dynamically in order to detect them. In this study, a graph-based malware detection method has been proposed to overcome these problems. The proposed method relies on obtaining the opcode graph of every executable file in the dataset and using them for future extraction. In this way, the proposed method reaches up to 98% detection accuracy. In addition to the accuracy rate, the proposed method makes it possible to detect packed malware without the need for unpacking or dynamic analysis.

关键词： malware detection static analysis opcode analysis graph-based detection packed malware

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GUARD: graph-based Unknown Attack Recognition and detection 16

GUARD: Graph-based Unknown Attack Recognition and Detection

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2024 International Workshop on Information Forensics and Security

作者： Dirksen, Shane Korah, John Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA

ISBN: (纸本)9798350364439;9798350364422

The escalating threats posed by cyber-attacks, particularly zero-day attacks, are projected to push the global annual cost of cybercrime to $10.5 trillion in 2025, with an anticipated 33 billion accounts breached [1]. Detecting these zero-day attacks, which exploit unknown vulnerabilities, is critical. This paper introduces the graph-based Unknown Attack Recognition and detection (GUARD) system, which combines autoencoders and graph theory to detect zero-day threats. GUARD translates latent representations and reconstruction errors from autoencoders into graphs, with edges weighted by similarity. Our novel technique, Hierarchical Quartet Loss, builds on the established triplet loss by using four elements and a hierarchical structure to enhance graph modularity by distinguishing both attacks and attack families. By analyzing patterns such as reconstruction error and cosine similarity, GUARD identifies unknown samples that deviate significantly from known patterns. Additionally, GUARD can discern multiple subclasses within the unknown dataset and represents them in a hierarchical family tree, offering a comprehensive visualization of network behaviors. Initial validation shows that GUARD improves zero-day attack detection precision and provides valuable insights into emerging threat characteristics and categorization.

关键词： zero-day attack detection autoencoder latent representation cosine similarity reconstruction error graph-based detection quartet loss hierarchical family tree user explainability

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Lattice reduction aided belief propagation for massive MIMO detection

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Science China(Information Sciences) 2019年第4期62卷 127-139页

作者： Senjie ZHANG Zhiqiang HE Kai NIU Shi JIN Hong CHENG Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications National Mobile Communications Research Laboratory Southeast University Intel Labs China

Efficient massive MIMO detection for practical deployment, which is with spatially correlated channel and high-order modulation, is a challenging topic for the fifth generation mobile communication(5G). In this paper, lattice reduction aided belief propagation(LRA-BP) is proposed for massive MIMO detection. LRA-BP applies the message updating rules of Markov random field based belief propagation(MRF-BP) in lattice reduced MIMO system. With the lattice reduced, well-conditioned MIMO channel,LRA-BP obtains better message updating and detection performance in spatially correlated channel than MRF-BP. Log-domain arithmetic is used in LRA-BP for computational complexity reduction. Simulation result shows that LRA-BP outperforms MRF-BP with 3–10 dB in terms of required SNR for 1% packet error rate in spatially correlated channel for 256-QAM. We also show that LRA-BP requires much lower complexity compared with MRF-BP.

关键词： massive MIMO MIMO detection belief propagation graph-based detection lattice reduction Markov random field

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Low-Complexity MIMO detection based on Belief Propagation Over Pairwise graphs

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2014年第5期63卷 2363-2377页

作者： Yoon, Seokhyun Chae, Chan-Byoung Dankook Univ Dept Elect Engn Yongin 448701 South Korea Yonsei Univ Sch Integrated Technol Seoul 120749 South Korea

This paper considers a belief propagation algorithm over pairwise graphical models to develop low-complexity iterative multiple-input multiple-output (MIMO) detectors. The pairwise graphical model is a bipartite graph where a pair of variable nodes are related by an observation node represented by the bivariate Gaussian function obtained by marginalizing the posterior joint probability density under the Gaussian input assumption. Specifically, we consider two types of pairwise models: the fully connected and ring-type. The pairwise graphs are sparse, compared with the conventional graphical model introduced by Bickson et al., insofar as the number of edges connected to an observation node (edge degree) is only two. Consequently, the computations are much easier than those of maximum likelihood (ML) detection, which are similar to the belief propagation (BP) that is run over the fully connected bipartite graph. The link level performance for non-Gaussian input is evaluated via simulations, and the results show the validity of the proposed algorithms. We also customize the algorithm with Gaussian input assumption to obtain the Gaussian BP run over the two pairwise graphical models, and for the ring-type, we prove its convergence to the linear minimum mean square error (MMSE) estimates. Since the maximum a posterior (MAP) estimator for Gaussian input is equivalent to the linear MMSE estimator, it shows the optimality of the scheme for Gaussian input.

关键词： Belief propagation (BP) forward-backward recursion graph-based detection low-complexity multi-input and multi-output (MIMO) detection Markov random field (MRF) sum-product algorithm

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On linear predictive detection for communications with phase noise and frequency offset

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2007年第4期56卷 2073-2085页

作者： Ferrari, Gianluigi Colavolpe, Giulio Raheli, Riccardo Univ Parma Dept Informat Engn I-43100 Parma Italy

In this paper, by applying the concept of linear prediction, which is widely used for fading channels, to phase-uncertain communications, we generalize existing linear predictive detection algorithms for transmission over channels with phase noise and frequency offset. This approach leads to the derivation of detection algorithms, which are referred to as phasor linear predictive (pLP), for trellis-based maximum a posteriori (MAP) sequence detection (based on the Viterbi algorithm) and MAP symbol detection: trellis-based (using the forward-backward algorithm) and graph-based (using the sum-product algorithm). The effectiveness of the, proposed pLP detection algorithms is evaluated for several communication schemes. The derived algorithms outperform previously appeared finite-memory detection solutions in terms of robustness against fast channel dynamics. Moreover, the proposed detection strategy lends itself to attractive extensions to adaptive schemes.

关键词： finite-memory detection forward-backward (FB) algorithm graph-based detection iterative detection linear prediction maximum a posteriori (MAP) sequence/symbol detection minimum mean square error (MMSE) estimation sum-product (SP) algorithm Viterbi algorithm (VA)

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A unified framework for finite-memory detection

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2005年第9期23卷 1697-1706页

作者： Ferrari, G Colavolpe, G Raheli, R Univ Parma Dept Informat Engn I-43100 Parma Italy

In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm, forward-backward, and sum-product algorithms, shows that a basic metric emerges naturally under very general causality and finite-memory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-memory condition. This conditional Markov property, although seldom met exactly in practice, is shown to represent a reasonable and useful approximation in all considered cases. We consider, as examples, linear predictive and noncoherent detection schemes. While good performance for increasing complexity can often be achieved with a finite-memory detection strategy, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity.

关键词： adaptive detection finite-memory detection forward-backward (FB) algorithm graph-based detection iterative detection maximum a posteriori (MAP) sequence/symbol detection sum-product (SP) algorithm Viterbi algorithm (VA)

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Fast dual-graph-based hotspot filtering

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2008年第9期27卷 1635-1642页

作者： Kahng, Andrew B. Park, Chul-Hong Xu, Xu Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Blaze DFM Inc San Diego CA 92121 USA

As advanced technologies in wafer manufacturing push patterning processes toward lower k(1) subwavelength printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in the generation of many hotspots, which are actual device patterns with relatively large critical-dimension and image errors with respect to on-wafer targets. Hotspots can be formed under a variety of conditions such as the original design being unfriendly to the resolution enhancement technique that is applied, unanticipated pattern combinations in rule-based optical proximity correction (OPC), or inaccuracies in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of a device, device performance and parametric yield can, be significantly degraded. The golden verification signoff tool using a simulation-based approach has occupied the mainstream and has been able to accurately detect hotspots. However, this approach represents a runtime-quality tradeoff point that is high in quality but also high in runtime. There is also little point in trying to replace the golden signoff tool. We are motivated to develop a low-runtime "prefilter" that reduces the amount of layout area to be analyzed by the golden tool, without compromising the overall quality of hotspot finding. In this paper, we first describe a novel detection algorithm for hotspots induced by lithographic uncertainty. Our goal is to rapidly detect all lithographic hotspots without significant accuracy degradation. In other words, we propose a filtering method: as long as there are no "false negatives," i.e., we reliably obtain a superset of actual hotspots, then our method can dramatically reduce the layout area processed by golden hotspot analysis. Our hotspot detection algorithm includes layout graph construction, graph planarization, three-level bridging hotspot detection, and necking hotspot detection. We have tested our flow on several indu

关键词： bridging and necking defocus graph-based detection hotspot lithography

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Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第9期15卷 6487-6501页

作者： Liu, Lei Yuen, Chau Guan, Yong Liang Li, Ying Su, Yuping Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-MIMO) system, in which a base station with M antennas serves K Gaussian sources simultaneously. Both K and M are very large numbers, and we consider the cases that K < M. The GMPID is a message passing algorithm operating on a fully connected loopy graph, which is well understood to be non-convergent in some cases. As it is hard to analyze the GMPID directly, the large-scale property of the massive MU-MIMO is used to simplify the analysis. First, we prove that the variances of the GMPID definitely converge to the mean square error of minimum mean square error (mmse) detection. Second, we derive two sufficient conditions that make the means of the GMPID converge to those of the mmse detection. However, the means of GMPID may not converge when K/M >= (root 2- 1)(2). Therefore, a modified GMPID called scale-and-add GMPID, which converges to the mmse detection in mean and variance for any K < M, and has a faster convergence speed than the GMPID, but has no higher complexity than the GMPID, is proposed. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results.

关键词： Convergence analysis Gaussian message passing Gaussian belief propagation graph-based detection loopy factor graph low-complexity MIMO detection

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Convergence of Gaussian Belief Propagation over a Ring-type graphical Model

Convergence of Gaussian Belief Propagation over a Ring-type ...

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18th Asia-Pacific Conference on Communications (APCC) - Green and Smart Communications for IT Innovation

作者： Yoon, Seokhyun Dankook Univ Dept Elect & Elect Engn Yongin Gyeonggi Do South Korea

ISBN: (纸本)9781467347280

We consider the joint MIMO detection algorithm based on ring-type Pair-wise Markov random fields(MRF) in [1] for Gaussian input to obtain the Gaussian BP that is run over the ring-type MRF and proves its convergence in mean to the linear MMSE estimates. The result lies on the same line of those in [2] and [3],but with differences in its graphical model and the message passing rule.

关键词： Markov random field (MRF) graph-based detection Belief propagation (BP)

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