Cyber attack campaigns are becoming increasingly complex and severe, causing significant impacts on institutions and individuals. Cyber Threat Intelligence (CTI) provides important evidential knowledge about attackers...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Cyber attack campaigns are becoming increasingly complex and severe, causing significant impacts on institutions and individuals. Cyber Threat Intelligence (CTI) provides important evidential knowledge about attackers and is critical to the shift from reactive to proactive defense against cyber attacks. Attack detection based on Indicators of Compromise (IOCs), a type of CTI, is vulnerable to the limitation of insufficient context of attack scenarios. In contrast, attack behavior intelligence is associated with information on attackers’ techniques, targets, and intentions, providing a solid foundation for security practitioners to conduct attack investigations or other applications. Many current CTI mining systems are limited to extracting CTI from a single source, leading to challenges such as fragmented attack behavior view and low-value density. To address these issues, we propose an unsupervised fusion framework named CTIFuser, which includes a comprehensive pipeline of four subtasks aimed at mining and fusing multi-source attack behaviors at the attack technique level. In our evaluation of 739 real-world CTI reports from 542 sources, experimental results demonstrate that CTIFuser can obtain a complete view of the attack behaviors at the attack technique level.
Microprocessor performance has improved at about 55% per year for the past three decades. To maintain this performance growth rate, next generation processors must achieve higher levels of instruction level parallelis...
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Microprocessor performance has improved at about 55% per year for the past three decades. To maintain this performance growth rate, next generation processors must achieve higher levels of instruction level parallelism. However, it is known that a conditional branch poses serious performance problems in modern processors. In addition, as an instruction pipeline becomes deep and the issue width becomes wide, this problem becomes worse. The goal of this study is to develop a novel processor architecture which mitigates the performance degradation caused by branch instructions. In order to solve this problem, we propose a super instruction-flow architecture. The concept of the architecture is described. This architecture has a mechanism which processes multiple instruction-flows efficiently and tries to mitigate the performance degradation. Preliminary evaluation results with small benchmark programs show that the first generation super instruction-flow processor efficiently mitigates branch overhead
Connectionist and computationalist theories are contrasted in many ways in the literature. Extremely common among these contrasts are distinctions that in various ways involve rules. For example, connectionist models ...
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Connectionist and computationalist theories are contrasted in many ways in the literature. Extremely common among these contrasts are distinctions that in various ways involve rules. For example, connectionist models are not supposed to use explicit rules where computationalist models do. Connectionist models are said to use soft rules, or soft constraints, where computationalist models do not. The authors believe that, while the distinctions that have been offered in the literature make sense, they do not serve to contrast computationalist models as a class from connectionist models as a class. In other words, for each of the distinctions that has been offered, they believe there to exist both computationalist and connectionist models that fall on either side of the distinctions. Thus, both connectionist and computationalist models may be said to use explicit rules, to use soft rules, and so forth.< >
The past five years have seen tremendous development in quantum computing technology, remarked by the demonstration of quantum supremacy. Although many quantum algorithms declare exponential speedups over their classi...
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ISBN:
(数字)9781665497473
ISBN:
(纸本)9781665497480
The past five years have seen tremendous development in quantum computing technology, remarked by the demonstration of quantum supremacy. Although many quantum algorithms declare exponential speedups over their classical counterparts, today's quantum devices in the noisy-intermediate scale quantum (NISQ) era are very susceptible to environmental noise, internal interference, manufacturing imperfection, and technology limitation. Consequently, quantum algorithms that are more robust to noise, or can be effectively decomposed into small pieces for incremental or parallel quantum execution become promising. The purpose of this workshop is to explore innovative ways of quantum-classical cooperative computing (QCCC) to make quantum computing more effective and scalable in NISQ platforms. The workshop will focus heavily on how classical computing can improve NISQ device execution efficiency, scalability, or compensate for noise impact or technology deficiency, with particular emphasis on demonstrable approaches on existing NISQ platforms, such as IBM-Q, IonQ and Rigetti.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
ISBN:
(纸本)9781538655566;9781538655559
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Traditional approaches to reliability and performance analysis become intractable when dealing with complex parallel and distributed processing systems, computer networks, and software for such systems. New approaches...
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Traditional approaches to reliability and performance analysis become intractable when dealing with complex parallel and distributed processing systems, computer networks, and software for such systems. New approaches based on Petri nets, dataflow graphs, simulations and approximations are now used in such cases. In order to extend the utility of Petri nets and dataflow graphs, the authors present a decomposition technique that can be used to partition a large system into smaller subsystems, where performance indexes of the total system can be obtained (at least approximately) from the subsystem analyses. The decomposition reduces the computational complexity of analysis significantly. The approach (using marked graph components) is similar to the concept of 'near-completely decomposable' stochastic processes.< >
Based on an analysis of related work about time exception in workflow, an algorithm for time exception handling of temporal workflow is presented in the paper. The algorithm is to meet the overall deadline of the case...
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Based on an analysis of related work about time exception in workflow, an algorithm for time exception handling of temporal workflow is presented in the paper. The algorithm is to meet the overall deadline of the case via an approach of cutting down the slack time of remaining activities, when there is a time exception. Meanwhile, details about the adjusting strategy for various kinds of routing constructions are also discussed. Finally, contrast experiment is used to show the effectiveness of the algorithm.
The concept of Network Function Virtualization (NFV) enables the realization of services as a Service Function Chain (SFC) that is composed of multiple Virtual Network Functions (VNFs), allowing for flexible deploymen...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
The concept of Network Function Virtualization (NFV) enables the realization of services as a Service Function Chain (SFC) that is composed of multiple Virtual Network Functions (VNFs), allowing for flexible deployment across the network. For wireless networks comprised of diverse domains which means be managed by different network providers, each domain has different resource and transmission costs. Due to commercial confidentiality reasons, the internal information of these domains is not interconnected, which increases the difficulty of SFC deployment. How to deploy SFCs in multi-domains networks at the lowest cost is becoming a challenge. This paper proposes a novel strategy for the scenario that information of sub-domain is invisible. The strategy firstly splits the SFC based on the processing dependency of its VNFs to reduce intermediate data transmission. Then it utilizes a Graph Matching Network (GMN) to evaluate the matching degree between sub-domain and SFC fragment. This matching degree acts as a reference for optimizing SFC allocation, thereby avoiding the direct acquisition of internal sub-domain information. Simulation results demonstrate that the proposed strategy not only significantly reduces resource, but also maintains a higher request acceptance ratio.
Feature (gene) selection is a frequently used preprocessing technology for successful cancer classification task in microarray gene expression data analysis. Widely used gene selection approaches are mainly focused on...
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Feature (gene) selection is a frequently used preprocessing technology for successful cancer classification task in microarray gene expression data analysis. Widely used gene selection approaches are mainly focused on the filter methods. Filter methods are usually considered to be very effective and efficient for high-dimensional data. This paper reviews the existing filter methods, and shows the performance of the representative algorithms on microarray data by extensive experimental study. Surprisingly, the experimental results show that filter methods are not very effective on microarray data. We analyze the cause of the result and provide the basic ideas for potential solutions.
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