Proceedings of the International Conference on Informatics and Analytics最新文献

Machine Learning Techniques for Intrusion Detection: A Comparative Analysis 入侵检测的机器学习技术:比较分析

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980378

Yasir Hamid, M. Sugumaran, L. Journaux

{"title":"Machine Learning Techniques for Intrusion Detection: A Comparative Analysis","authors":"Yasir Hamid, M. Sugumaran, L. Journaux","doi":"10.1145/2980258.2980378","DOIUrl":"https://doi.org/10.1145/2980258.2980378","url":null,"abstract":"With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework's security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.","PeriodicalId":276705,"journal":{"name":"Proceedings of the International Conference on Informatics and Analytics","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127115977","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: 30

DCT based Audio Steganography in Voiced and Un-voiced Frames 基于DCT的浊音和非浊音帧音频隐写

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980360

Aniruddha Kanhe, G. Aghila

引用次数: 19

Attribute Based Encryption with Fine-grained Access Provision in Cloud Computing 云计算中基于属性的细粒度访问加密

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980444

G. Tamizharasi, B. Balamurugan, R. Manjula

引用次数: 9

Beta Reputation and Direct Trust Model for Secure Communication in Wireless Sensor Networks 无线传感器网络中安全通信的Beta信誉和直接信任模型

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980413

Priayoheswari B, K. K., K. A

引用次数: 12

Non Uniform Area Coverage (NUAC) for an agricultural robot based on grid decomposition 基于网格分解的农业机器人非均匀区域覆盖

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980398

S. Kalaivanan, R. Kalpana

引用次数: 0

Evolutionary Neural Networks versus Adaptive Resonance Theory Net for Breast Cancer Diagnosis 进化神经网络与自适应共振理论网络在乳腺癌诊断中的应用

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980458

T. Nayak, T. Dash, D. C. Rao, P. Sahu

引用次数: 7

Accessibility Evaluation of Indian Railway Websites 印度铁路网站的可访问性评价

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980393

V. Balaji, K. Kuppusamy

引用次数: 3

Location Based Context Aware user Interface Recommendation System 基于位置的上下文感知用户界面推荐系统

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980418

D. Natarajasivan, M. Govindarajan, Assistant Professor

引用次数: 6

A Dominating Set Based Data Gathering in Wireless Sensor Network using Fuzzy Logic and ACO 基于模糊逻辑和蚁群算法的无线传感器网络控制集数据采集

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980415

Sanjoy Mondal, Saurav Ghosh, U. Biswas

{"title":"A Dominating Set Based Data Gathering in Wireless Sensor Network using Fuzzy Logic and ACO","authors":"Sanjoy Mondal, Saurav Ghosh, U. Biswas","doi":"10.1145/2980258.2980415","DOIUrl":"https://doi.org/10.1145/2980258.2980415","url":null,"abstract":"In the recent furtherance of MEMS technologies such as high speed processor with memory, and other radio technology, it entitled a wireless sensor networks to collect useful information from the remote hazardous area such as dense forest, deep sea floor etc. The collected data is accumulated and then transmitted to Base Station for further processing to take necessary actions. Wireless sensor network subsist of inexpensive with restrained battery power henceforth it is essential to take up a competent data acquisition and aggregation technique to attain enlarge network lifetime. In Hierarchical Routing Protocol (HRP) some node transmits sensed data to the Base Station (BS) which is more energy intensive task while others are engaged in local communications which provides an unbalanced network. So it is important to distribute the load evenly. In this paper we propose a novel energy efficient data gathering method coined as Fuzzy-DS-ACO in which Fuzzy C Means (FCM) is used to divide sensor nodes into clusters. Data redundancy is another problem in HRP as multiple nodes gathered data from the same area. A dominating Set (DS) for each cluster is found and ACO is used to form cluster chain. ACL for each cluster chain is elected depending on surplus energy of nodes and immediacy to the BS by using Fuzzy Logic system. An upper level chain is constructed among CL's using the same ACO and consequently a Super Leader (SL) is selected which sends aggregated data to BS directly. Simulation results show that Fuzzy-DS-ACO surpasses LEACH, LEACH-C, PEGASIS and DFCP in terms of total lifespan of network and load balancing alongside it provides good percentage coverage of the deployment area. It is shown that results are also statistically significant.","PeriodicalId":276705,"journal":{"name":"Proceedings of the International Conference on Informatics and Analytics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133058611","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: 2

Distributed denial of service attack detection using Naive Bayes Classifier through Info Gain Feature Selection 基于信息增益特征选择的朴素贝叶斯分类器分布式拒绝服务攻击检测

Proceedings of the International Conference on Informatics and Analytics Pub Date : 2016-08-25 DOI: 10.1145/2980258.2980379

Ningombam Anandshree Singh, Khundrakpam Johnson Singh, T. De

{"title":"Distributed denial of service attack detection using Naive Bayes Classifier through Info Gain Feature Selection","authors":"Ningombam Anandshree Singh, Khundrakpam Johnson Singh, T. De","doi":"10.1145/2980258.2980379","DOIUrl":"https://doi.org/10.1145/2980258.2980379","url":null,"abstract":"Distributed Denial of Service (DDoS) attack brings revenue loss, productivity loss, reputation damage, theft, etc. to huge banking and business firms. This leads to the necessity of a good DDoS prevention and detection techniques. The paper aims to provide a better solution to these problems using features analysis. The statistical characteristics or parameters of the incoming packets viz. MTI (mean time intervals), POIP (probability of occurrence of IP), TTL (time to live), ACK value, SYN value, time stamp field, differentiated service field and sequence number are analyzed. The incoming packets are classified into normal and attack packets by deploying Naïve Bayes classifier algorithm using the attack and normal profile from previously available datasets. Information gain algorithm is used to decrease the computation time, memory usage and increase efficiency of detection by reducing the number of parameters. The performance increases with more consistency after the application of information gain. The efficiencies of detection before and after the application of information gain are 98% and 99.5% respectively. The computation time is also reduced by 46.6%. In the paper, we use CAIDA 2008 and CAIDA anonymous trace 2015 datasets for feature selection and classification.","PeriodicalId":276705,"journal":{"name":"Proceedings of the International Conference on Informatics and Analytics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114442991","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: 23