2018 Eleventh International Conference on Contemporary Computing (IC3)最新文献_第2页

A Novel Approach for Detecting Malware in Android Applications Using Deep Learning 一种利用深度学习检测Android应用中恶意软件的新方法

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530668

Prashant Kaushik, P. Yadav

{"title":"A Novel Approach for Detecting Malware in Android Applications Using Deep Learning","authors":"Prashant Kaushik, P. Yadav","doi":"10.1109/IC3.2018.8530668","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530668","url":null,"abstract":"Detection of android malware depends on the feature vector extraction of android applications statically and dynamically. Static analysis has advantage over dynamic analysis as it covers all source code from byte code and manifest files which contains the permission for applications whereas dynamic analysis of the APK files includes the features like the no. of system calls an application makes, network url it access etc. Feature vector updation in the dataset due to version updates of application creates a challenge for the existing tool to classify the application as malicious or benign. This work creates an combination of neural network and automated tool which collects and updates the feature vectors in the training dataset. This dataset is used by the neural network for its reinforcement training for better classification and classify the application in three classes malicious, benign and can't say. Tensor flow is used for making a neural network which learn from the data extracted in the form of feature vector and classify the applications in the category of malicious, benign or can't say. The work has been able to achieve accuracy of over 80% for a dataset of 1000 sample applications with over 15 different feature vector extracted using designed automated feature vector collection modules.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"138 1 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128763337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Comparative Study of Spam SMS Detection Using Machine Learning Classifiers 基于机器学习分类器的垃圾短信检测比较研究

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530469

Mehul Gupta, Aditya Bakliwal, Shubhangi Agarwal, Pulkit Mehndiratta

引用次数: 35

Teaching Methodology for IoT Workshop Course Using Node-RED 基于Node-RED的物联网工作坊课程教学方法

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530664

R. Krishnamurthi

引用次数: 7

Enhancing Influence Maximization in Social Networks Using Parallel Reverse Reachability Set Computations 利用并行反向可达集计算增强社交网络中的影响最大化

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530640

Mridul Haque, D. Banerjee

{"title":"Enhancing Influence Maximization in Social Networks Using Parallel Reverse Reachability Set Computations","authors":"Mridul Haque, D. Banerjee","doi":"10.1109/IC3.2018.8530640","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530640","url":null,"abstract":"Influence Maximization (IM) is the problem of finding a small subset of nodes from a large social network which can potentially spread influence in the maximally. IM finds widespread applications in viral marketing, targeted advertisement, control of epidemics and feed recommendations. In recent years several novel solutions have been proposed [9], [13], [14] for solving IM which have progressively given asymptotically superior results than the previous. In general IM algorithms can take up to several days to find the maximal influence nodes on billion scale social networks. In this work, we carefully observe the execution profile of a state-of-the-art solution (Stop and Stare (SSA) [14]) and investigate opportunities for parallelization. IM algorithms typically involve randomization, and we propose to exploit some of the architectural and programming benefits offered by modern processors so as to achieve quicker execution. We propose a new algorithm for parallel generation and storage of random samples in the SSA algorithm and implement them on both multi-core and many-core processors. We show that our solution provides nearly 1.8x improvement in running times on a large social network, while ensuring that the maximal influence computed using our technique is at par with the original solution.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134078700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Influence Maximization Using Genetic Algorithm with Dynamic Probabilities 动态概率遗传算法的社会影响最大化

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530626

Sakshi Agarwal, S. Mehta

{"title":"Social Influence Maximization Using Genetic Algorithm with Dynamic Probabilities","authors":"Sakshi Agarwal, S. Mehta","doi":"10.1109/IC3.2018.8530626","DOIUrl":"https://doi.org/10.1109/IC3.2018.8530626","url":null,"abstract":"The previous decades have observed the exponential growth of online social networks, where billions of users exchange information with each other and generate tremendously large quantity of the content. This dominance of social networks in our daily life has encouraged more consideration of researcher in the field of information diffusion, where a small bit of information could widespread through “world of mouth” effect. One of the key research problems in information diffusion is influence maximization, which is a NP-hard problem. Influence Maximization (IM) is the problem to find k number of nodes that are most influential nodes of the network, which can maximize the information propagation in the network. Various heuristics available to find most influencing nodes of the network include random, high degree, single discount, general greedy and genetic algorithm with weighted cascade etc. In this paper, we proposed dynamic probability based genetic approach using topic affinity propagation (TAP) method to find the optimal set of influential nodes of the network. The efficiency of the proposed approach is analyzed on two large-scale networks. Results express that the proposed algorithm is able to improve the influence spread by 6% to 13% with respect to various influence maximization heuristics.","PeriodicalId":118388,"journal":{"name":"2018 Eleventh International Conference on Contemporary Computing (IC3)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134114877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Recommendation Systems: Past, Present and Future 推荐系统:过去、现在和未来

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530620

S. Nehete, S. Devane

引用次数: 3

A Stacked Technique for Gender Recognition Through Voice 语音性别识别的堆叠技术

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530520

Pramit Gupta, Somya Goel, Archana Purwar

引用次数: 14

Mitigation and Detection of DDoS Attacks in Software Defined Networks 软件定义网络中DDoS攻击的缓解和检测

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530514

Shariq Murtuza, Krishna Asawa

引用次数: 10

Predicting the Algorithmic Time Complexity of Single Parametric Algorithms Using Multiclass Classification with Gradient Boosted Trees 基于梯度提升树的多类分类单参数算法时间复杂度预测

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530473

D. Sharma, Sumit K. Vohra, Tarun Gupta, Vipul Goyal

引用次数: 4

Towards Dependency Based Collaborative Method for Requirement Prioritization 基于依赖的需求优先级协作方法研究

2018 Eleventh International Conference on Contemporary Computing (IC3) Pub Date : 2018-08-01 DOI: 10.1109/IC3.2018.8530542

Ankita Gupta, Chetna Gupta

引用次数: 3