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Supervised malware learning in cloud through System calls analysis

Supervised malware learning in cloud through System calls an...

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2021 IEEE international conference on Innovative computing, intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Maheswari, K. Uma Shobana, G. Bushra, S. Nikkath Subramanian, Nalini Bharathi Women's College Dept. of Computer Science Chennai India Madras Christian College Department of Computer Applications Chennai India St. Joseph's Institute of Technology Dept. of Information Technology Chennai India Prathyusha Engineering College Dept of CSE Chennai India

ISBN: (纸本)9781665435215

Even if there is a rapid proliferation with the advantages of low cost, the emerging on-demand cloud services have led to an increase in cybercrime activities. Cyber criminals are utilizing cloud services through its distributed nature of infrastructure and create a lot of challenges to detect and investigate the incidents by the security personnel. The tracing of command flow forms a clue for the detection of malicious activity occurring in the system through System Calls Analysis (SCA). As machine learning based approaches are known to automate the work in detecting malwares, simple Support Vector Machine (SVM) based approaches are often reporting low value of accuracy. In this work, a malware classification system proposed with the supervised machine learning of unknown malware instances through Support Vector Machine-stochastic Gradient Descent (SVM-SGD) algorithm. The performance of the system evaluated on CIC-IDS2017 dataset with labelled attacks. The system is compared with traditional signature based detection model and observed to report less number of false alerts with improved accuracy. The signature based detection gets an accuracy of 86.12%, while the SVM-SGD gets the best accuracy of 99.13%. The model is found to be lightweight but efficient in detecting malware with high degree of accuracy. © 2021 IEEE.

关键词： Malware

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Scientific Workflow Management on Hybrid Clouds with Cloud Bursting and Transparent Data Access 21st

Scientific Workflow Management on Hybrid Clouds with Cloud B...

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21st international conference on Computational Science, ICCS 2021

作者： Baliś, Bartosz Orzechowski, Michal Dutka, Lukasz Slota, Renata G. Kitowski, Jacek Department of Computer Science AGH University of Science and Technology Krakow Poland AGH University of Science and Technology ACK Cyfronet AGH Krakow Poland

ISBN: (纸本)9783030779603

Cloud bursting is an application deployment model wherein additional computing resources are provisioned from public clouds in cases where local resources are not sufficient, e.g. during peak demand periods. We propose and experimentally evaluate a cloud-bursting solution for scientific workflows. Our solution is portable thanks to using Kubernetes for deployment of the workflow management system and computing clusters in multiple clouds. We also introduce transparent data access by employing a virtual distributed file system across the clouds, allowing jobs to use a POSIX file system interface, while hiding data transfer between clouds. To balance load distribution and minimize the communication volume between clouds, we leverage graph partitioning, while ensuring that the algorithm distributes the load equally at each parallel execution stage of a workflow. The solution is experimentally evaluated using the HyperFlow workflow management system integrated with the Onedata data management platform, deployed in our on-premise cloud in Cyfronet AGH and in the Google Cloud. © 2021, Springer Nature Switzerland AG.

关键词： Workflow management

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The Hybrid Navigation Method in Face of Dynamic Obstacles 21st

The Hybrid Navigation Method in Face of Dynamic Obstacles

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21st international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2020

作者： Zhao, Kaidong Ning, Li Shenzhen Institutes of Advanced Technology Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030692438

With the development of society and lifestyle changes. Robo-tic navigation based on traditional navigation and positioning techniques can hardly cope with navigation in complex dynamic scenarios. Traditional navigation and positioning algorithms, such as Dijkstra and A*, cannot perform path planning in dynamic scenarios. In this paper, we present an improved reinforcement learning-based algorithm for local path planning that allows it to still perform well when there are more dynamic obstacles. The algorithm has the advantage of low cost to transform into others situation and fast training speed over the navigation algorithm based entirely on reinforcement learning. © 2021, Springer Nature Switzerland AG.

关键词： Navigation

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European conference on Service-Oriented and Cloud computing, ESOCC 2020

1st International Workshop on Edge Adoption and Migration, E...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European conference on Service-Oriented and Cloud computing, ESOCC 2020

ISBN: (纸本)9783030719050

The proceedings contain 18 papers. The special focus in this conference is on Edge Adoption and Migration. The topics include: Towards Resolving Security Smells in Microservice-Based Applications;towards Citizen-Centric Marketplaces for Urban Sensed Data;DevOps and Quality Management in Serverless computing: The RADON Approach;5G-CARMEN: Service Continuity in 5G-Enabled Edge Clouds;Services computing for Cyber-Threat Intelligence: The ANITA Approach;Quality Assurance of Heterogeneous Applications: The SODALITE Approach;fogProtect: Protecting Sensitive Data in the computing Continuum;intelligent Monitoring of Virtualized Services;edge computing Simulation Platforms: A technology Survey;platforms for Serverless at the Edge: A Review;formal Modeling and Simulation of Collaborative intelligent Robots;virtual Machine Placement for Edge and Cloud computing;Cloud-Edge Microservice Architecture for DNN-based distributed Multimedia Event Processing;modelling Service-Oriented Systems and Cloud Services with Heraklit;an Evaluation of Frameworks for Microservices Development;mining the Architecture of Microservice-Based Applications from their Kubernetes Deployment.

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Cross Layer Optimization and distributed Reinforcement Learning for Wireless 360° Video streaming

Cross Layer Optimization and Distributed Reinforcement Learn...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Anis Elgabli Mohammed S. Elbamby Cristina Perfecto Mounssif Krouka Mehdi Bennis Vaneet Aggarwal *Department of Industrial and Systems Engineering Interdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS) Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Telefónica Research Spain Bilbao School of Engineering University of the Basque Country (UPV/EHU) Spain Centre for Wireless Communications University of Oulu Finland School of Industrial Engineering Purdue University USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.

关键词： Wireless communication Measurement Cross layer design streaming media Physical layer Ad hoc networks Robustness Quality of experience Optimization Videos

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The 6G computing Continuum (6GCC): Meeting the 6G computing challenges

The 6G Computing Continuum (6GCC): Meeting the 6G computing ...

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6G Networking (6GNet), international conference on

作者： William Tä rneberg Emma Fitzgerald Monowar Bhuyan Paul Townend Karl-Erik Å rzé n Per-Olov Ö stberg Erik Elmroth Johan Eker Fredrik Tufvesson Maria Kihl Department of Electrical and Information Technology Lund University Sweden Department of Computing Science Ume&#x00E5 University Sweden Department of Automatic Control Lund University Sweden

6G systems, such as Large intelligent Surfaces, will require distributed, complex, and coordinated decisions through-out a very heterogeneous and cell free infrastructure. This will require a fundamentally redesigned software infrastructure accompanied by massively distributed and heterogeneous computing resources, vastly different from current wireless networks. To address these challenges, in this paper, we propose and motivate the concept of a 6G computing Continuum (6GCC) and two research testbeds, to advance the rate and quality of research. 6G computing Continuum is an end-to-end compute and software platform for realizing large intelligent surfaces and its tenant users and applications. One for addressing the challenges or orchestrating shared computational resources in the wireless domain, implemented on a Large intelligent Surfaces testbed. Another simulation-based testbed is intended to address scalability and global-scale orchestration challenges.

关键词： 6G mobile communication Wireless networks Scalability Microprocessors Computational modeling Computer architecture Real-time systems

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Cluster Head Selection using Dingo Optimization Algorithm in Mobile Ad Hoc Network

Cluster Head Selection using Dingo Optimization Algorithm in...

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intelligent Algorithms for Computational Intelligence Systems (IACIS), international conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Piyush Kumar Pareek Kiran Polimetla Pankaj Zanke Hassan Mohamed Ali School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department. of Information Technology Adobe Inc Austin Texas USA Sapiens International Corporation Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Efficient Energy-Efficient Cluster Head Selection (EECHS) remains a critical issue in Mobile Ad Hoc Networks (MANETs), where the selection of an appropriate cluster head (CH) can significantly enhance power efficiency. Clustering in routing plays a vital role in optimizing MANET performance by coordinating pathways through various analytics and criteria. Nodes in MANETs form clusters to improve communication, employing strategies such as distributed clustering and cluster-based routing. Despite their application, these methods often suffer from complexity and high overhead, which compromise their *** study proposes the Dingo Optimization Algorithm (DOA) as a viable solution to the EECHS problem. DOA inherits its exploration and exploitation processes from the natural behavior of dingoes, involving techniques such as pursuing, approaching, encircling, and pestering to identify the optimal CHs. Extensive experimental research conducted on the NS2 simulator demonstrates that the proposed method achieves a packet delivery ratio (PDR) of 96%, a throughput of 240 Kbps, and an energy consumption of 21 mJ for a network with 500 nodes. The simulation results and replication findings indicate that this strategy is particularly suitable for sensitive MANET applications, outperforming existing routing tactics in terms of efficiency and effectiveness.

关键词： Energy consumption Heuristic algorithms Simulation Clustering algorithms Throughput Routing Energy efficiency Mobile computing Optimization Mobile ad hoc networks

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Proprioceptive state Estimation of Legged Robots with Kinematic Chain Modeling

Proprioceptive State Estimation of Legged Robots with Kinema...

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IEEE-RAS international conference on Humanoid Robots

作者： Varun Agrawal Sylvain Bertrand Robert Griffin Frank Dellaert Institute for Robotics and Intelligent Machines and School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Florida Institute for Human & Machine Cognition Pensacola FL USA Intelligent Systems and Robots University of West Florida Pensacola FL USA

ISBN: (纸本)9798350309805

Legged robot locomotion is a challenging task due to a myriad of sub-problems, such as the hybrid dynamics of foot contact and the effects of the desired gait on the terrain. Accurate and efficient state estimation of the floating base and the feet joints can help alleviate much of these issues by providing feedback information to robot controllers. Current state estimation methods are highly reliant on a conjunction of visual and inertial measurements to provide real-time estimates, thus being handicapped in perceptually poor environments. In this work, we show that by leveraging the kinematic chain model of the robot via a factor graph formulation, we can perform state estimation of the base and the leg joints using primarily proprioceptive inertial data. We perform state estimation using a combination of preintegrated IMU measurements, forward kinematic computations, and contact detections in a factor-graph based framework, allowing our state estimate to be constrained by the robot model. Experimental results in simulation and on hardware show that our approach out-performs current proprioceptive state estimation methods by 27% on average, while being generalizable to a variety of legged robot platforms. We demonstrate our results both quantitatively and qualitatively on a wide variety of trajectories.

关键词： Legged locomotion Computational modeling Current measurement Propioception Humanoid robots Hardware Trajectory

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Relation Prediction of N-ary Knowledge Graph Based on Multi-information Propagation

Relation Prediction of N-ary Knowledge Graph Based on Multi-...

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IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Peijie Wang Jianrui Chen Luheng Yang Julong Li School of Comp. Sci. Shaanxi Normal University Xi’an China

Knowledge Graph (KG) is currently the most popular graph structure. For graphs, however, they are frequently incomplete due to the lack of most information in the real world. Knowledge Graph Completion (KGC) has become the most interesting topic for researchers. At the same time, the relationships in social networks have expanded from binary relationships to more complex high-order relationships. However, the current research methods on multi-relationship still lack the interaction between entities, and the corresponding entity information under each relationship is different. Based on this, we propose a high-order GCN-based multi-relation prediction model (denoted as MHGCN). Firstly, an entity adjacent matrix is constructed for each relation, and a high-order graph convolutional network (GCN) is used to propagate the neighbor information among entities within the relation. Secondly, a probabilistic calculation method that integrates entity information is designed to judge the existence of facts. Experimental analysis is carried out on three representative datasets, which illustrate the effectiveness of our proposed algorithm. In particular, for FB-AUTO dataset with unfixed number of entities, the MRR of MHGCN reaches 0.883. The MRR of the fixed entity number dataset JF17K-4 is up to 0.828. It further shows that MHGCN is not only applicable to datasets with unfixed number of entities, but also applicable to datasets with fixed number of entities.

关键词： Knowledge engineering Social networking (online) Knowledge graphs Predictive models Probability Ubiquitous computing Probabilistic logic

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Approximation Algorithm for Connected Submodular Function Maximization Problems

Approximation Algorithm for Connected Submodular Function Ma...

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international conference on distributed computing Systems

作者： Wenzheng Xu He Xue Jing Li Weifa Liang Zichuan Xu Pan Zhou Xiaohua Jia Sajal K. Das College of Computer Science Sichuan University Chengdu P. R. China Sichuan Fire Science and Technology Research Institute of MEM Chengdu P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong P. R. China Department of Computer Science City University of Hong Kong Hong Kong P. R. China School of Software Dalian University of Technology Dalian P. R. China School of Cyber Science and Engineering Huazhong University of Science & Technology P. R. China Department of Computer Science Missouri University of Science and Technology Rolla USA

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ so that a given submodular function $f (S)$ on $S$ is maximized while the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in $S$ , the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel $\frac{1-1/e}{2h+2}$ -approximation algorithm for the problem, improving the best approximation ratio $\frac{1-1/e}{2h+3}$ for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where $e$ is the base of the natural logarithm, $h$ is a parameter depends on the problem and its typical value is 2. In addition. when $h= 2$ , the algorithm approximation ratio is at least $\frac{1-1/e}{5}$ and may be as large as 1 in some special cases when $K$ ≤21, and is no less than $\frac{1-1/e}{6}$ when $K$ ≥ 22, compared with the current best approximation ratio $\frac{1-1/e}{7}(= \frac{1-1/e}{2h+3})$ for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： Upper bound Approximation algorithms Autonomous aerial vehicles Data models distributed computing intelligent sensors Monitoring

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