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Fractal Based cognitive Neural Network to Detect Obfuscated and Indistinguishable Internet Threats 16

Fractal Based Cognitive Neural Network to Detect Obfuscated ...

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ieee 16th international conference on cognitive Informatics and cognitive computing (ICCI*CC)

作者： Siddiqui, Sana Khan, Muhammad Salman Ferens, Ken Kinsner, Witold Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781538607718

State of the art network intrusion detection systems are heavily influenced by signature based techniques for detecting threats which are extracted from raw packet captures and firewall logs. With the recent emergence of cloud computing and big data analytics, supervised machine learning is also being used to detect deviations of the network traffic patterns from already-known normal patterns. Subsequently, these anomalies are analyzed by human experts to differentiate legitimate anomalies, also known as true positives, from enormous false anomalies and reconfigure the machine learning system accordingly. Using machine learning for cyber security is relatively a difficult topic compared to other application domains primarily because of the dynamically fast changing threat landscape which is also extremely complex. Our main claim is that the proposed methodology significantly improves the classification performance of neural networks by detecting obfuscated malicious samples that masquerade the behavior of normal samples and thus are indistinguishable on Platonic Euclidean feature space. It is achieved by transforming a traditional single-scale (Euclidean scale) based error curve to information fractal dimension based multiscale error curve and subsequent design changes in the backpropagation algorithm. The performance comparison is provided by incorporating our proposed methodology in a fundamental gradient descent based neural network and shows promising results. Our claims are supported by experimental results and the subsequent analyses.

关键词： Machine learning Artificial Neural Network Classification Cyber threats Cyber security complexity Fractal Gradient Descent Backpropagation Multiscale Advanced indistinguishable threats Obfuscated cyber threats cognitive cyber security cognitive Intelligence Dimensionality

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cognitive Acoustic Analytics Service for Internet of Things 1

Cognitive Acoustic Analytics Service for Internet of Things

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1st ieee international conference on cognitive computing (ICCC)

作者： Pan, Zhaotai Ge, Yi Zhou, Yu Chen Huang, Jing Chang Zheng, Yu Ling Zhang, Ning Liang, Xiao Xing Gao, Peng Zhang, Guan Qun Wang, Qingyan Shi, Shaw-ben IBM Res China Beijing Peoples R China IBM Internet Things Littleton MA USA IBM Internet Things Austin TX USA

ISBN: (纸本)9781538620083

The rapid development of the Internet of Things (IoT) has brought great changes for non-contact and non-destructive sensing and diagnosis. For every inanimate object can tell us something by the sound it makes, acoustic sensor demonstrates great advantages comparing to conventional electronic and mechanic sensors in such cases: overcoming environmental obstacles, mapping to existing use cases of detecting problems with human ears, low cost for deployment, etc. It could be widely applied to various domains, such as predictive maintenance of machinery, robot sensory, elderly and baby care in smart home, etc. Whether we can use the acoustic sensor data to understand what is happening and to predict what will happen relies heavily on the analytics capabilities we apply to the acoustic data, which has to overcome the obstacles of noise, disturbance and errors, and has to meet the requirement of real-time processing of high volume signals with large number of sensors. In this paper, we propose a scalable cognitive acoustics analytics service for IoT that provides the user an incremental learning approach to evolve their analytics capability on non-intuitive and unstructured acoustic data through the combination of acoustic signal processing and machine learning technology. It first performs acoustic signal processing and denoising, enables acoustic signal based abnormal detection based on sound intensity, spectral centroid, etc. Then based on the accumulated abnormal data, a supervised learning method is performed as baseline and a neural network based classifier is used to recognize acoustic events in different scenarios with various volume of sample data and requirement of accuracy. In addition, acoustic sensor arrays processing is supported for localization of moving acoustic source in more complex scenario. In this paper, we designed a hybrid computing structure. Finally, we conduct experiments on acoustic event recognition for machinery diagnosis, and show that the p

关键词： acoustic analytics internet of things machine learning acoustic signal processing

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cognitive Domain Ontologies on the TrueNorth Neurosynaptic System

Cognitive Domain Ontologies on the TrueNorth Neurosynaptic S...

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international Joint conference on Neural Networks (IJCNN)

作者： Rahman, Nayim Atahary, Tanvir Taha, Tarek Douglass, Scott Univ Dayton Elect & Comp Engn Dayton OH 45469 USA US Air Force Res Lab Human Effectiveness Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781509061822

cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus there is a strong need for a real time agent running on a low power computing platform. This paper examines how some of the components of a cognitive agent can be mapped onto the IBM TrueNorth neurosynaptic system to achieve real time performance at low power. The agent examined is the cognitively Enhanced complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a cognitive Domain Ontology (CDO). This paper looks at how CDOs can be mapped into neural form for realtime applications, and then examines their implementation on the IBM TrueNorth processor. The results show that the TrueNorth implementation generated correct answers for all the CDOs examined.

关键词： Knowledge Mining TrueNorth cognitive Agents Autonomous Decision Making

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Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples 17

Knowledge will Propel Machine Understanding of Content: Extr...

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ieee/WIC/ACM international conference on Web Intelligence (WI)

作者： Sheth, Amit Perera, Sujan Wijeratne, Sanjaya Thirunarayan, Krishnaprasad Wright State Univ Kno E Sis Ctr Dayton OH 45435 USA

ISBN: (纸本)9781450349512

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

关键词： Machine Intelligence Multimodal Exploitation Understanding complex Text Knowledge-enhanced Machine Learning Knowledge-enhanced NLP Knowledge-driven Deep Content Understanding Personalized Digital Health Semantic-cognitive-Perceptual computing Implicit Entity Recognition Emoji Sense Disambiguation

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Proceedings of the 2017 ieee 9th international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2017

Proceedings of the 2017 IEEE 9th International Conference on...

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9th ieee international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2017

ISBN: (纸本)9781538606971

The proceedings contain 210 papers. The topics discussed include: on the initiation phase of a mixed reality simulator for air pollution monitoring by autonomous UAVs;combined control of impulse processes in complex systems cognitive maps with multirate sampling;convolutional neural network based solar photovoltaic panel detection in satellite photos;tasks distribution for students testing based on fuzzy logic;detecting aging of process sensors with noise signal measurement;improvement of data acquisition systems for the measurement of physical-chemical environmental properties;the model for decision support on design of the hybrid renewable energy system;information resources processing using linguistic analysis of textual content;the loading computation algorithm on bearings of rollers of belt conveyor;and about the problem of correction of digital regulation algorithms.

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Proceedings of the 2017 ieee 9th international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2017

Proceedings of the 2017 IEEE 9th International Conference on...

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9th ieee international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2017

ISBN: (纸本)9781538606971

The proceedings contain 210 papers. The topics discussed include: on the initiation phase of a mixed reality simulator for air pollution monitoring by autonomous UAVs;combined control of impulse processes in complex systems' cognitive maps with multirate sampling;convolutional neural network based solar photovoltaic panel detection in satellite photos;tasks distribution for students testing based on fuzzy logic;detecting aging of process sensors with noise signal measurement;improvement of data acquisition systems for the measurement of physical-chemical environmental properties;the model for decision support on design of the hybrid renewable energy system;information resources processing using linguistic analysis of textual content;the loading computation algorithm on bearings of rollers of belt conveyor;and about the problem of correction of digital regulation algorithms.

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data Extraction from Natural Language using Universal Networking Language 3

Data Extraction from Natural Language using Universal Networ...

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international conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

作者： Saha, Aloke Kumar Mridha, M. F. Rafiq, Jahir Ibna Das, Jugal Krishna Univ Asia Pacific Dept Comp Sci & Enginerring Dhaka Bangladesh Jahangirnagar Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781538632437

data extraction, which falls under the area of Natural Language Processing (UNL), finds specific data from unstructured data. This research paves the way to introduce a unique technique on data extraction - providing the user with exactly what is asked without any mimicry of unsolicited data. The proposal sets logical and symmetrical relation between the search criteria and operational data. Since the data is unstructured and volume can be relatively high, we have emphasized highly on putting the data under categories - defined and used by the researchers for further exploitation of data. Universal Networking Language (UNL) is efficiently used to compare data and merge. A new approach of machine learning is presented herein that essentially augments efficiency of Natural Language computing (NLC) and cognitive computing (CC). This proposed approach uses UNL relationship and successful test data shows much improved results and efficient generalization. Existing machine learning approaches are widely used on numeric data which are producing expected results but one key contention is the limitation of data type that can be handled. Current models fail to properly train on the semantics, logical consistency;many natural language properties are either ignored or prove too much of a task. Consequently, the approach presented herein this paper carries further positive points in producing meaningful and worthwhile result. Moreover, complex data that are consisted of alphanumeric data, sequence and resulting criteria can be executed correctly.

关键词： Big data data Extraction NLP Universal Networking Natural Language computing Machine Learning

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Design of Router for Spiking Neural Networks 12

Design of Router for Spiking Neural Networks

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2017 ieee 12th international conference on ASIC

作者： Yewen Ni Xiaoxin Cui Yuanning Fan Qiankun Han Kefei Liu Xiaole Cui Institute of Microelectronics Peking University Key Lab of Integrated Microsystems Peking University Shenzhen Graduate School

ISBN: (纸本)9781509066261;9781509066254

The development of large-scale networks with artificial neurons and adaptive synapses has suggested new avenues of exploration for brain-like cognitive *** neural networks(SNNs),highly inspired from natural computing in the brain and recent advances in neurosciences,are often referred to as the 3 th generation of neural network,and become a new research hotspot in the era of artificial *** architecture supports simpler category of biologically-inspired neuron models and more complex large-scale interconnection in on-chip network of neurosynaptic *** paper introduces the design of router used in a spiking neural network,which is able to send and receive spiking information in network properly,as well as perfectly dealing with network anomaly such as data race or traffic *** router is designed for the network at a scale of 64 * 64 neurosynaptic cores at the most,with 256 neurons in each *** the area and estimated power consumption is *** router could also be applied to larger scale of SNN architecture networks effectively.

关键词： frame Design of Router for Spiking Neural Networks

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ANFIS Application to Infer Student Performance in English Learning Using Affective Factors 17

ANFIS Application to Infer Student Performance in English Le...

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Joint 17th World Congress of international-Fuzzy-Systems-Association / 9th international conference on Soft computing and Intelligent Systems (IFSA-SCIS)

作者： Bachtiar, Fitra A. Sulistyo, Gunadi H. Cooper, Eric W. Kamei, Katsuari Brawijaya Univ Fac Comp Sci Malang Indonesia Univ Negeri Malang Fac Letters Malang Indonesia Ritsumeikan Univ Coll Informat Sci & Engn Kyoto Japan

ISBN: (纸本)9781509049172

Teaching and learning practices are essentially meant to equip students with necessary competences. In assessing student learning, few teaching and learning practices consider the role of affective factors. Assessment relying solely on cognitive factors could impact teaching and learning effectiveness although marginal work exists exploring affective factors to enhance teaching and learning effectiveness. The current study considers using affective factors to infer student learning of English skills. Student affective factors, such as motivation, attitude, introversion, extroversion, anxiety, and self-esteem are measured using questionnaires. The data obtained is then used to model student in learning using Adaptive Neuro-Fuzzy Inference System (ANFIS) with a different set of parameters. Evaluation of the models is based on Mean Absolute Error (MAE) and Mean Squared Error (MSE). The result from the experiment shows that the model is able to infer student learning by 0.076 (MAE) and 0.008 (MSE) respectively. In addition, the proposed model that is not too general nor too complex and with fewer training iteration numbers shows the lowest error among other proposed models.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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A Parallel Clustering and Test Partitioning Techniques Based Mining Trajectory Algorithm for Moving Objects 13

A Parallel Clustering and Test Partitioning Techniques Based...

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13th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： He, Qian Chen, Yiting Dong, Qinghe Cheng, Dongsheng Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Minist Educ Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Univ Key Lab Cloud Comp & Complex Syst Guilin 541004 Peoples R China

ISBN: (纸本)9781538621653

In recent years, the intelligent transportation system has been widely used to deal with traffic problems. The analysis of traffic incident is important in intelligent transportation field, and gathering patterns can model various traffic incidents. However, with the increasing amount of moving trajectory data, the traditional mining algorithms of gathering patterns cannot effectively analyze trajectory data. In this paper, we propose a parallel algorithm RDD-Gathering to discover the gathering patterns in massive trajectory data. Based on the algorithm, we further design a system framework of traffic incident analysis and prediction, which can realize the prediction of the abnormal traffic events. Finally, the accuracy and efficiency of the proposed algorithms are validated by extensive experiments based on a real trajectory dataset, and the results of experiments show that the proposed method can effectively improve the efficiency of gathering retrieval.

关键词： Spark Trajectory data mining DBSCAN Gathering patterns

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