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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA"

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2015 ieee symposium on computational intelligence for security and defense applications, cisda 2015 - Proceedings

2015 IEEE Symposium on Computational Intelligence for Securi...

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ieee symposium on computational intelligence for security and defense applications, cisda 2015

ISBN: (纸本)9781467375573

The proceedings contain 28 papers. The topics discussed include: using computational swarm intelligence for real-time asset allocation;rigorous sensor resource management: methodology and evolutionary optimization;dynamic asset allocation for counter-smuggling operations under disconnected, intermittent and low-bandwidth environment;design and analysis of neuromemristive echo state networks with limited-precision synapses;memristive computational architecture of an echo state network for real-time speech-emotion recognition;spiking-based matrix computation by leveraging memristor crossbar array;design of experiments based empirical models to support cognitive radio decision making;detecting push to talk radios using two tone intermodulation distortion;and evolving spiking neural networks: a novel growth algorithm corrects the teacher.

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Proceedings of the 2014 7th ieee symposium on computational intelligence for security and defense applications, cisda 2014

Proceedings of the 2014 7th IEEE Symposium on Computational ...

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7th ieee symposium on computational intelligence for security and defense applications, cisda 2014

ISBN: (纸本)9781479954315

The proceedings contain 24 papers. The topics discussed include: effects of the cookie cutter function shapes on Monte Carlo simulations of weapon effectiveness;malware detection using genetic programming;intrusion detection system using discrete Fourier transform;a simple braking model for detecting incidents locations by smartphones;a hybrid framework for enhancing correlation to solve cold-start problem in recommender systems;an improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals;automated generation of ham rules for Vietnamese spam filtering;design of takagi-sugeno fuzzy controller for automatic stabilization system of missiles with blended aerodynamic and lateral impulsive reaction-jet;and behavior-driven video analytics system for critical infrastructure protection.

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Proceedings of the 2013 ieee symposium on computational intelligence for security and defense applications, cisda 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Computational Inte...

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2013 ieee symposium on computational intelligence for security and defense applications, cisda 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

ISBN: (纸本)9781467359115

The proceedings contain 15 papers. The topics discussed include: comparing heuristic search methods for finding effective real-time strategy game plans;evolving team tactics using potential fields;positioning of military combat units through weight-based terrain analysis using NASA world wind;testing a distributed denial of service defense mechanism using red teaming;multiple UAV area decomposition and coverage;cooperative path planning for UAVs with UAV loss considerations;neural adaptive flight controller for ducted-fan UAV performing nonlinear maneuver;a novel obstacle avoidance control algorithm in a dynamic environment;design of a nonlinear dynamic inversion controller for trajectory following and maneuvering for fixed wing aircraft;and investigating application behavior in network traffic traces.

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

Optimizing Batched Small Matrix Multiplication on Multi-core...

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22nd ieee International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x. © 2024 ieee.

关键词： Network security

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Advancing security of Mobile and IOT Devices with AI Threat Detection Strategies

Advancing Security of Mobile and IOT Devices with AI Threat ...

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ieee/ACM International symposium on Big Data Computing (BDC)

作者： Ali Shikai Marymount University USA

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Because of their ubiquitous connectivity and major influence on many sectors, mobile devices and the Internet of Things (IoT) are becoming even more vital. Through the app economy and mobile commerce, mobile devices stimulate economic growth and innovation; IoT improves efficiency and intelligence in fields including smart homes, healthcare, and industrial automation. These technologies' confluence guarantees flawless, data-driven experiences and promotes ongoing technological innovation, therefore transforming our way of life and employment. Data privacy issues, vulnerability to cyberattacks, and vulnerabilities resulting from insufficient security policies in many IoT devices define the security difficulties in the areas of mobile devices and IoT. Constant connectivity increases the possibility of network breaches; hence, maintaining compliance with data security rules is difficult. To reduce these threats, strong encryption, frequent upgrades, and strict access limits—all part of effective security strategies—are vital. By means of machine learning to recognize and react to threats in real-time, artificial intelligence-based threat detection improves security for mobile devices and IoT. Providing a proactive defense against changing cyber threats, it detects anomalies, forecasts breaches, and automates reactions. Based on their assessment criteria including accuracy, precision, recall, and f1-score, this study contrasts several machine learning and deep learning models for the most effective intrusion detection approach on mobiles devices and IOT. Comparative analysis and evaluation made use of many datasets

关键词： Deep learning Technological innovation Accuracy computational modeling Intrusion detection Mobile handsets Threat assessment Internet of Things security Unsupervised learning

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A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data

A Spiking Neural Network Based Auto-encoder for Anomaly Dete...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Stratton, Peter Wabnitz, Andrew Hamilton, Tara Julia Univ Technol Sch Elect & Data Eng Sydney NSW 2007 Australia Def Sci & Technol Grp Cyber & Elect Warfare Div Edinburgh SA 5111 Australia

ISBN: (纸本)9781728125473

Anomaly Detection (AD) is useful for a range of applications including cyber security, health analytics, robotics, defense and big data. Automating the detection of anomalies is necessary to deal with large volumes of data and to satisfy real time processing constraints. Current Machine Learning (ML) methods have had some success in the automated detection of anomalies, but no ideal ML solutions have been found for any domain. Spiking Neural Networks (SNNs), an emerging ML technique, have the potential to do AD well, especially for Edge applications where it needs to be low power, readily adaptable, autonomous and reliable. Here we investigate SNNs doing anomaly detection on streams of text. We show that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.

关键词： anomaly detection spiking neural networks edge applications cyber security

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Artificial intelligence And Machine Learning

Artificial Intelligence And Machine Learning

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2022 ieee Future Networks World Forum, FNWF 2022

作者： Kataria, Deepak Walid, Anwar Daneshmand, Mahmoud Dutta, Ashutosh Enright, Michael A. Gu, Rentao Lackpour, Alex Ramachandran, Prakash Wang, Honggang Chen, Chi-Ming Chng, Baw Darema, Frederica Ip Junction United States Nokia Bell Labs United States Stevens Institute of Technology United States Johns Hopkins University Applied Physics Lab United States Quantum Dimension Inc. United States Bupt China Drexel University United States Dell Technologies United States EOTF India UMass Dartmouth United States United States Bawman Llc United States Info Symbiotic Systems Society United States

ISBN: (纸本)9781665462501

In the evolution of artificial intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives. © 2022 ieee.

关键词： Cloud computing

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computational intelligence in Maritime security and defense: Challenges and Opportunities 8

Computational Intelligence in Maritime Security and Defense:...

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8th ieee symposium Series on computational intelligence (ieee SSCI)

作者： Cococcioni, Marco Univ Pisa Dept Informat Engn Largo Lucio Lazzarino 1 I-56122 Pisa Italy

ISBN: (纸本)9781538692769

computational intelligence (CI) has a great potential in security & defense (S&D) applications. Nevertheless, such potential seems to be still under exploited. In this work we first review CI applications in the maritime domain, done in the past decades by NATO Nations. Then we discuss challenges and opportunities for CI in S&D. Finally we argue that a review of the academic training of military officers is highly recommendable, in order to allow them to understand, model and solve new problems, using CI techniques.

关键词： Artificial intelligence Training security computational intelligence Oceans Simulated annealing

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Memristive computational Architecture of an Echo State Network for Real-Time Speech-Emotion Recognition

Memristive Computational Architecture of an Echo State Netwo...

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ieee symposium on computational intelligence for security and defense applications (cisda)

作者： Sakti, Qutaiba Merkel, Cory Kudithipudi, Dhireesha Wysocki, Bryant Rochester Inst Technol NanoComputing Res lab Rochester NY 14623 USA US Air Force Res Lab I-13441 Rome NY Italy

ISBN: (纸本)9781467375573

Echo state neural networks (ESNs) provide an efficient classification technique for spatiotemporal signals. The feedback connections in the ESN topology enable feature extraction of both spatial and temporal components in time series data. This property has been used in several application domains such as image and video analysis, anomaly detection, and speech recognition. In this research, we explore a hardware architecture for realizing ESN efficiently in power-constrained devices. Specifically, we propose a scalable computational architecture applied to speech-emotion recognition. Two different topologies are explored, with memristive synapses. The simulation results are promising with a classification accuracy of approximate to 96% for two distinct emotion statuses.

关键词： Reservoir Computing Echo State Networks Memristors Speech Emotion Recognition

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A Trace Abstraction Approach for Host-based Anomaly Detection

A Trace Abstraction Approach for Host-based Anomaly Detectio...

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ieee symposium on computational intelligence for security and defense applications (cisda)

作者： Murtaza, Syed Shariyar Khreich, Wael Hamou-Lhadj, Abdelwahab Gagnon, Stephane Concordia Univ ECE SBA Res Lab Montreal PQ Canada Univ Quebec Outaouais Dept Sci Adm Gatineau PQ Canada

ISBN: (纸本)9781467375573

High false alarm rates and execution times are among the key issues in host-based anomaly detection systems. In this paper, we investigate the use of trace abstraction techniques for reducing the execution time of anomaly detectors while keeping the same accuracy. The key idea is to represent system call traces as traces of kernel module interactions and use the resulting abstract traces as input to known anomaly detection techniques, such as STIDE (the Sequence Time-Delay Embedding) and HMM (Hidden Markov Models). We performed experiments on three datasets, namely, the traditional UNM dataset as well as two modern datasets, Firefox and ADFA-LD. The results show that kernel module traces can lead to similar or fewer false alarms and considerably smaller execution times compared to raw system call traces for host-based anomaly detection systems.

关键词： Host-based Anomaly Detection System Trace Analysis and Abstraction System Call Traces Software security Software Dependability

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