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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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An Ensemble learning System to Mitigate Malware Concept Drift Attacks (Short Paper) 13th

An Ensemble Learning System to Mitigate Malware Concept Drif...

引用

13th international conference on Information Security Practice and Experience (ISPEC) / 3rd international Symposium on Security and Privacy in Social Networks and Big Data (SocialSec)

作者： Wang, Zhi Tian, Meiqi Wang, Junnan Jia, Chunfu Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783319723594;9783319723587

machine learning is widely used in malware detection systems as a core component. However, machine learning algorithm is based on the assumption that the underlying malware concept is stable for training and testing. The assumption is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. This paper proposes an ensemble learning system which combines vertical and horizontal correlation learning models. The significant diversity among vertical and horizontal correlation models increases the difficulty of concept drift attacks. And average p-value assessment is applied to fortify the system to be sensitive to hidden concept drift. The experiment results show that the hybrid system could actively recognize the concept drift among different Miuref variants.

关键词： Malware detection machine learning Concept drift Vertical correlation Horizontal correlation

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Design of access control system based on the near field communication (NFC) technology 3

Design of access control system based on the near field comm...

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2018 3rd international conference on Insulating Materials, Material application and Electrical Engineering, IMMAEE 2018

作者： Li, Biqing Tian, Miao Zheng, Shiyong Jiang, Suping School of Information and Communication Engineering Hezhou University Hezhou Guangxi542899 China School of Software Henan University Kaifeng Henan475004 China Wuhan University Wuhan430070 China Guangxi Talent International College Qinzhou Guangxi535000 China

With the development of mobile Internet technology, Android smart phones is gradually integrated into people's lives, and become important information terminal. Short-range wireless communication (NFC) is one of the technologies for use in access control systems, this kind of short-distance wireless communication standard can be achieved within a few centimeters of data exchange between devices. NFC is also in full compliance with ISO standards for management of contactless smart card, which is a notable feature of it an ideal platform. Android smartphones are gradually replace most of the commonly used tools in our lives, such as cameras, maps and so on, and NFC functionality based on the Android platform will gradually replace the life of cash payments and IC card access control system, NFC has become an important trend of development of intelligent access control system. This design from two aspects of hardware and software to take on Android platform-based NFC function plays an important role in access control systems. This design is based on the Android phone's NFC features using single-chip computer system relays, electromagnetic locks, smart home functions, NFC contactless door way humans bring more convenience, farewell forgot to bring my keys at home will not be suffering. © 2018 Published under licence by IOP Publishing Ltd.

关键词： Smartphones

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application of Artificial Immune System Algorithms on Healthcare Data 3

Application of Artificial Immune System Algorithms on Health...

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3rd international conference on Computational Intelligence and Networks (CINE)

作者： Das, Rama Krushna Panda, Manisha Mahapatra, Nirupama Dash, Sweta Shree Natl Informat Ctr Berhampur Orissa India Berhampur Univ Berhampur Orissa India Inst Tech Educ & Res Bhubaneswar India

ISBN: (纸本)9781538625293

Data mining is one of the most significant ways of extracting the important information from a required set of data. Now-a-days the healthcare systems generate a very large amount of data, which are difficult to analyze through traditional methods. Data mining techniques provide the technology to extract meaningful information from these huge healthcare data for decision making. This paper mainly focuses on the analysis and evaluation of different parameters from large healthcare datasets, using Artificial Immune System (AIS) based classification algorithms, and normal classification algorithms. Five life science based datasets focusing on healthcare are considered for our experiment, to evaluate different parameters, using AIS based and normal classification algorithms. The result of the experiment is analyzed to propose the best classifier among the considered algorithms, based on the factors like accuracy, sensitivity, F-measure and specificity. The proposed classifier can further be used for different decision making purposes in healthcare systems.

关键词： Healthcare Data Mining Artificial Immune System Classification Classifier CLONALG CSCA machine learning AIRS1 AIRS2 AIRS2Parallel Immunosl Immunos2 Immunos99 Sensitivity Specificity

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Predicting response time-related quality-of-service outages of paas cloud applications by machine learning 1

引用

3rd international conference on Mobile, Secure, and Programmable Networking, MSPN 2017

作者： Schedel, Angela Brune, Philipp University of Applied Sciences Neu-Ulm Wileystraße 1 Neu-Ulm89231 Germany

ISBN: (数字)9783319678078

ISBN: (纸本)9783319678061

For customers running their applications on Platform-as-a-Service (PaaS) cloud environments it is important to ensure the Quality-of-Service (QoS) of their applications. Knowing in advance if and when a potential problem is likely to occur allows the application owner to take appropriate countermeasures. Therefore, predictive analytics using machine learning could allow to be alerted in advance about potential upcoming QoS outages. In this context, mainly Infrastructure-as-a-Service (IaaS) or Software-as-a-Service (SaaS) have been studied in the literature so far. Studies about predicting QoS outages for the Platform-as-a-Service (PaaS) service model are sparse. Therefore, in this paper an approach for predicting response-time-related QoS outages of web services running in a PaaS cloud environment is presented. The proposed solution uses the open source Apache Spark platform in combination with MLib and binary classification by the naive Bayes algorithm. The approach is evaluated by using test data from a social app backend web service. The results indicate that it is feasible in practice. © 2017, Springer international Publishing AG.

关键词： Quality of service

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machine learning for Anomaly Detection and Categorization in Multi-Cloud Environments 4

Machine Learning for Anomaly Detection and Categorization in...

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4th IEEE international conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE international conference of Scalable and Smart Cloud, SSC 2017

作者： Salman, Tara Bhamare, Deval Erbad, Aiman Jain, Raj Samaka, Mohammed Washington University in St. Louis St. LouisWA United States Qatar University Doha Qatar

ISBN: (纸本)9781509066438

Cloud computing has been widely adopted by application service providers (ASPs) and enterprises to reduce both capital expenditures (CAPEX) and operational expenditures (OPEX). applications and services previously running on private data centers are now being migrated to private or public clouds. Since most of the ASPs and enterprises have globally distributed user bases, their services need to be distributed across multiple clouds, spread across the globe which can achieve better performance in terms of latency, scalability and load balancing. The shift has eventually led the research community to study multi-cloud environments. However, the widespread acceptance of such environments has been hampered by major security concerns. Firewalls and traditional rule-based security protection techniques are not sufficient to protect user-data in multi-cloud scenarios. Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99% detection accuracy and categorization accuracy of 93.6%, with the inability to categorize some attacks. Further, we argue that such categorization can be applied t

关键词： Intrusion detection

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3rd international conference on Soft Computing in Data Science, SCDS 2017

引用

3rd international conference on Soft Computing in Data Science, SCDS 2017

ISBN: (纸本)9789811072413

The proceedings contain 26 papers. The special focus in this conference is on Soft Computing in Data Science. The topics include: Time series machine learning: Implementing ARIMA and hybrid ARIMA-ANN for electricity forecasting modeling;a design of a reminiscence system for tacit knowledge recall and transfer;pattern search based on particle swarm optimization technique for block matching motion estimation algorithm;enhancing parallel self-organizing map on heterogeneous system architecture;geovisualization using hexagonal tessellation for spatiotemporal earthquake data analysis in Indonesia;Incremental filtering visualization of jobstreet Malaysia ICT jobs;software application for analyzing photovoltaic module panel temperature in relation to climate factors;budget visual: Malaysia budget visualization;fuzzy arithmetical modeling of pressurizer in a nuclear power plant;road lane segmentation using deconvolutional neural network;nitrogen fertilizer recommender for paddy fields;Ranking performance of modified fuzzy TOPSIS variants based on different similarity measures;prediction of new bioactive molecules of chemical compound using boosting ensemble methods;Pragmatic miner to risk analysis for intrusion detection (PMRA-ID);intelligent system E-learning modeling according to learning styles and level of ability of students;mining textual terms for stock market prediction analysis using financial news;content quality of latent dirichlet allocation summaries constituted using unique significant words;rare event prediction using similarity majority under-sampling technique;Pattern recognition of balinese carving motif using learning vector quantization (LVQ);Feature extraction for image content retrieval in thai traditional painting with SIFT algorithms;modeling of the gaussian-based component analysis on the kernel space to extract face image.

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AdaBoost typical Algorithm and its application research 3

AdaBoost typical Algorithm and its application research

引用

3rd international conference on Mechanical, Electronic and Information Technology Engineering, ICMITE 2017

作者： Tu, Chengsheng Liu, Huacheng Xu, Bing Computer Science and Engineering college Chongqing Three Gorges University ChongQing WanZhou404000 China

Boosting is popular algorithm in the field of machine learning. Adaboost is the most typical Algorithm in the Boosting family. This paper introduces Boosting and its research status briefly, and introduces the typical... 详细信息

关键词： Adaptive boosting

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Graph-Based machine learning Algorithm with application in Data Mining 3

Graph-Based Machine Learning Algorithm with Application in D...

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3rd international conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Jin, Shimei Chen, Wei Han, Jiarui Syracuse Univ Syracuse NY 13244 USA Zhejiang Univ Technol Hangzhou 310000 Zhejiang Peoples R China HangZhou 14 High Sch Hangzhou 310006 Zhejiang Peoples R China

ISBN: (纸本)9781538619315

machine learning is widely used in various applications such as data mining, computer vision, and bioinformatics owing to the explosion of available data. However, in practice, many data have some missing attributes. The graphic theory serves as a powerful tool for modeling and analyzing many such practical problems, such as networks of communication and data organization. This paper focuses on semi-supervised learning algorithms based on the graph theory, aiming at establishing robust models in the input space with a very limited number of training samples. The use of such algorithm in multiple data mining applications is also discussed.

关键词： component formatting style styling insert

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Analysis of machine learning Techniques Based Intrusion Detection Systems 3rd

引用

3rd international conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Sharma, Rupam Kr. Kalita, Hemanta Kumar Borah, Parashjyoti North Eastern Hills Univ Shillong Meghalaya India Assam Don Bosco Univ Gauhati India

ISBN: (纸本)9788132225294;9788132225287

Attacks on computer Networks are one of the major threats on using Internet these days. Intrusion Detection Systems (IDS) are one of the security tools available to detect possible intrusions in a Network or in a Host. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate as well as low false positive rate. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by authors at different times.

关键词： Intrusion detection system Supervised learning Unsupervised learning KDD'99 Anomaly detection Host intrusion system

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Deep learning and Its application in Silent Sound Technology 3

Deep Learning and Its Application in Silent Sound Technology

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3rd international conference on Computing for Sustainable Global Development (INDIACom)

作者： Varshney, Vibhu Singh, Deeksha Tiwari, Ayush AMU Dept Comp Engn Zakir Husain Coll Engn & Technol Aligarh Uttar Pradesh India

ISBN: (纸本)9789380544199

machine learning is a branch of Artificial Intelligence that deals with making a machine Intelligent. machine learning is being broadly used in the areas of computer vision, Speech Recognition and Natural language Processing. Deep learning is that area of machine learning research which specifically deals with training Artificial Neural Networks having deep architectures. In this paper we provide an introduction to machine learning. Next we discuss the need of deep learning. Then we mention the areas in which deep learning has been successfully used, and finally we propose how deep learning can be applied or how it can be used in Silent Sound Technology.

关键词： Deep architectures Deep belief networks machine learning non-convex optimization Silent Sound Technology(SST) stacked auto-encoders unsupervised pre-training

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