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An End-to-End Raw Bytes Based Malware Classifier via Self-Attention Residual Convolutional Network

An End-to-End Raw Bytes Based Malware Classifier via Self-At...

引用

international conference on Computer and Communications (ICCC)

作者： Zihan Hou Xiaoyong Li Linghui Li Jie Yuan Kaiyang Deng Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education College of Cyberspace Security Beijing University of Posts and Telecommunications Beijing P.R.China

Malware classification has always been a fundamental and cutting-edge problem in the field of cyber security. the rapidly evolution of malware variant makes the malware recognition challenging. Most of existing methods are based on symbolic execution, fingerprints, behavior sequences, manually designed feature, etc., which increase the workload of security experts and slow down the security response speed. In this paper, we propose an end-to-end raw byte based method for malware classification. Our proposed model mainly consists of a convolutional module, self-attention residual module and MLP based classifier. the convolutional module is directly used to process the byte stream to extract the high-level feature of the malware. the self-attention residual module is designed to capture the relevant information scattered in different blocks of the malware. the MLP based classifier is to calculate the category probability of the malware. We conduct extensive experiments on the well-known BODMAS dataset which contains 300GB of malwares. Our proposed method achieves 90.0% macro-F1 and is proved to be rational and effective for malware classification.

关键词： Computational modeling Manuals Probability Fingerprint recognition Feature extraction Malware Behavioral sciences

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Development of Advanced Edge computing Framework using Rich Client Devices 8

Development of Advanced Edge Computing Framework using Rich ...

引用

8th international conference on Communications and networking, ComNet2020

作者： Oguchi, Masato Yamaguchi, Saneyasu Ochanomizu University Department of Information Sciences Tokyo Japan Kogakuin University Faculty of Informatics Tokyo Japan

ISBN: (纸本)9781728153209

An idea of edge computing becomes popular as the data can be processed in the vicinity of client IoT devices. Since the performance of client devices is not high, they only collect the data and send them to the edge server to process. Due to the data transfer cost and privacy matters, it is desirable to analyze the data within the client device as much as possible. thus, we focus on a smartphone that is a typical and very popular rich client device. Performance of some types of smartphone is surprisingly high, and it is advanced greatly year by year. In this paper, we propose a framework to analyze data and make use of them inside the Android terminal. Since we already have investigated into the development of elaborative software executed inside Android kernel, and we also have investigated into the deep learning analysis using data obtained from Android terminal, the proposed framework can be realized by utilizing the result of such research works. © 2020 IEEE.

关键词： Edge computing

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Clickbait Analysis and Detection Method on Chinese Social Media

Clickbait Analysis and Detection Method on Chinese Social Me...

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international conference on Big Data computing and Communications (BIGCOM)

作者： Gui Jiayi Yu Ke Hao Zhou Xiaofei Wu International School Beijing University of Posts and Telecommunications Beijing 100876 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

Clickbait has been a nerve-wracking phenomenon because of its enticing, deceitful features driven by commercial purposes after Internet has been widely used. When machine learning methods begin to thrive in Natural Language Processing(NLP) area, clickbait detection task based on machine learning methods has been studied. And later, deep learning methods join the research of clickbait detection. English clickbait detection corpora are built first as the foundation to perform clickbait detection. Nevertheless, public corpus based on Chinese is rarely seen making the research of clickbait detection on Chinese social media hard to continue. In this paper, a Chinese news clickbait dataset based on three mainstream Chinese media named TTNN dataset is constructed. Over 7,000 news are contained in the TTNN dataset among which over 500 news are manually labeled as clickbait based on a set of precise annotation procedures. Detailed analysis is performed on the TTNN dataset from the overall statistics, features of the title text, the source text, and the relation between title and content. the analysis shows that non-clickbait word contains more nouns and proper nouns while clickbait data contains more adjectives. Additionally, certain social medias are found to have higher clickbait-news publishing possibility. Seven classic machine learning models i.e. SVM_Linear, SVM_Sigmoid, KNN, LR, RF, DT, and ADB, and seven classic deep learning models i.e. BERT, BERT+CNN, Text-CNN, Bi-LSTM, Bi-LSTM+A, Bi-LSTM+P, Bert + Bi-LSTM+P are implemented for clickbait detection tasks as baselines. this paper proposes a deep learning model using the integration of BERT and CNN(named BCCD) achieving the best performance as high as 93.66% accuracy among other baselines ultimately. Shortcomings of the TTNN dataset and the BCCD model are also discussed in the paper. However, all analyses conducted in the paper still show the high quality and future research potential of the TTNN dataset and the BCCD

关键词： Deep learning Radio frequency Social networking (online) Publishing Computational modeling Bit error rate Neural networks

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Fog computing enabled Smart Grid Blockchain Architecture and Performance Optimization with DRL Approach 8

Fog Computing enabled Smart Grid Blockchain Architecture and...

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8th IEEE international conference on Computer Science and Network Technology (ICCSNT)

作者： Zheng, Weijun Wang, Wenhua Wu, Guoqing Xue, Chenzi Wei, Yifei State Grid Zhejiang Elect Power Co Ltd Jiaxing Power Supply Co Jiaxing Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728181233

Smart grid is willing to make full advantage of distributed clean energy to alleviate energy crisis and environmental problems. However, distributed renewable energy is usually invisible and uncontrollable for the current power system, and there are intermittent problems in its generation. therefore, how to achieve the power balance, maintain safe operation, and ensure the reliability and quality of power supply when the distributed energy reaches a high penetration in the grid is a huge challenge. Blockchain as one of the research hotspots brings about new solution approach to the dilemma. the two fields have many commons on decentralization, autonomy, marketization and intelligence. In this paper, we discuss the feasible scheme of the integrated system and add fog computing to reduce costs. Considering the realization of system, we choose Hyper Fabric as the basic structure and add verifiable random function to the consensus aimed to improve randomness and security in the encrypted election. Meanwhile, in order to satisfy the business requirements, a flexible adjustment method based on Deep Q Learning algorithm is designed to realize the joint optimization of throughput, latency and storage cost. the proposed scheme provides the advantages including privacy, flexibility, extensibility and implantation simplicity.

关键词： Smart grid Blockchain Consensus mechanism Fog computing Deep reinforcement learning

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Digital Cloud Platform of distributed Data Storage in Sphere of Higher Education 13

Digital Cloud Platform of Distributed Data Storage in Sphere...

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13th international ELEKTRO conference (ELEKTRO)

作者： Perepelkin, Dmitry Saprykin, Aleksey Ivanchikova, Maria Kosorukov, Sergey Ryazan State Radio Engn Univ Fac Comp Engn Ryazan Russia

ISBN: (纸本)9781728175423

Current trends, such as an increase in the number of devices connected to the Internet, an exponential increase in the volume of information, the development of network and cloud technologies, are changing all areas of activity. the global increase in network traffic dictates the need for business to configure large-scale computing systems and networks. To introduce new digital services in various fields, innovative approaches to building computing infrastructures meant for the processing, transmission and storage of large data arrays are needed. the aim of this work is to develop a software component for building a digital cloud platform for distributed data storage in higher education. Current work proposes an architecture and software tools for distributed processing and storage of user data on a digital cloud platform of a higher educational institution. the main class diagrams of the developed cloud platform and the principles of interaction of its main components are described in detail. the functioning process and the features of building a network infrastructure of the developed digital cloud platform for distributed data storage are considered. the developed digital cloud platform implements the capabilities of immediate connection and deployment of new digital services through the use of a wide range of software components and open APIs.

关键词： distributed computing distributed data storage cloud computing digital cloud platform software defined networking

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Deep Learning based Blind Source Identification of whatsApp and Facebook Images

Deep Learning based Blind Source Identification of whatsApp ...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Dipan Banerjee Ruchira Naskar Heritage Institute of Technology Kolkata India Indian Institute of Engineering Science and Technology Shibpur India

Source Identification is the forensic problem of source device detection of digital images/videos/audio. In this paper, we deal with the problem of Source Camera Identification (SCI) of digital images. the existing forensic techniques of image source identification fail in source identification of images taken from Social networking platforms, mainly due to the inherent compression characteristics induced in to those images. In this work, deep neural network based architecture is proposed to identify the source of Facebook and whatsApp images. We used InceptionV3 model, which is a 48-layers deep. It surpasses the state-of-the-art performance in terms of online source networks (OSN) image source identification accuracy. We considered smart phone devices in this work for our experiments, and present our results on the recently published VISION dataset.

关键词： Deep learning Performance evaluation Freeware Image coding Social networking (online) Forensics Digital images

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Improving Intent Correctness with Automated Testing

Improving Intent Correctness with Automated Testing

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IEEE conference on Network Softwarization (NetSoft)

作者： Paul Alcock Ben Simms Will Fantom Charalampos Rotsos Nicholas Race School of Computing and Communications Lancaster University

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

Intent-based networking (IBN) systems have become the de-facto control abstraction to drive self-service, self-healing, and self-optimized capabilities in service delivery processes. Nonetheless, the operation complexity of modern network infrastructures make network practitioners apprehensive towards adoption in production, requiring further evidence for correctness. In this paper, we argue that testing, verification and monitoring should become first-class citizens in reference IBN architecture, in order to improve the detection errors during operations. Towards this goal, we present an extension for an intent architecture that allows IBN system to validate the correctness of network configuration using realistic network emulation. Furthermore, we present an intent use-case that ensure correct operation in hybrid networks.

关键词： Programmable control conferences Emulation Process control Production Critical infrastructure Complexity theory

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MapReduce based Heart Disease Prediction System

MapReduce based Heart Disease Prediction System

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Shaik Fathimabi Ebenezer Jangam A. Srisaila Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

Decision tree is the traditional method to process health-related data. However, there are challenges in developing the distributed decision tree algorithms for massive health data using Hadoop. First, as the datasets are massive, the process of building a decision tree is time-consuming. the second problem is splitting. To solve these issues, we propose MapReduce-based Heart Disease Prediction System (MRHDP) for constructing a decision tree to predict heart disease with the Map Reduce programming model using the Hadoop platform. We develop distributed algorithms using the MapReduce framework, and also we conducted a large-scale experiment on massive data sets. Finally, the results indicate that the MRHDP algorithm exhibits better prediction results for heart disease, indicates better processing with less time productivity and versatility.

关键词： Heart Productivity distributed databases Training data Programming Predictive models Prediction algorithms

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Towards AIOps in Edge computing Environments 8

Towards AIOps in Edge Computing Environments

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8th IEEE international conference on Big Data (Big Data)

作者： Becker, Soeren Schmidt, Florian Gulenko, Anton Acker, Alexander Kao, Odej TU Berlin Complex & Distributed IT Syst Berlin Germany

ISBN: (纸本)9781728162515

Edge computing was introduced as a technical enabler for the demanding requirements of new network technologies like 5G. It aims to overcome challenges related to centralized cloud computing environments by distributing computational resources to the edge of the network towards the customers. the complexity of the emerging infrastructures increases significantly, together with the ramifications of outages on critical use cases such as self-driving cars or health care. Artificial Intelligence for IT Operations (AIOps) aims to support human operators in managing complex infrastructures by using machine learning methods. this paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. the overhead of a high-frequency monitoring solution on edge devices is evaluated and performance experiments regarding the applicability of three anomaly detection algorithms on edge devices are conducted. the results show, that it is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices with a reasonable overhead on the resource utilization.

关键词： edge monitoring anomaly detection AIOps

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Detecting and Inoculating against PTSD in Online Forums and Platforms

Detecting and Inoculating against PTSD in Online Forums and ...

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international conference on Behavior, Economic and Social computing (BESC)

作者： Chereen Shurafa College of Humanities and Social Sciences Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665400244

Posttraumatic stress disorder (PTSD) has significantly impacted the mental health of individuals on a global scale. the linguistic patterns and emotional expressions of PTSD are evident via online behaviors. In this study, we look at both the English and Arabic languages to detect PTSD, and to also devise an intervention for those that are experiencing this form of long-term trauma. this research aims to detect PTSD patterns online, with the ultimate purpose of inoculating against PTSD in online forums and platforms. through the interdisciplinary combination of social media analytics and tech-assisted behavior change, intervention tools can be applied online to detect and inoculate against PTSD. Emojis are a form of expression that vary according to language, and more specifically, dialect. this research aims to create posttraumatic stress disorder specific emoticons, and eventually globally release a curated glossary.

关键词： Social computing Terminology Social networking (online) Mental health Linguistics Stress

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