Big Data: Map Reduce Based Outlier Identification for Fraud

hulking Data Map Reduce Based Outlier Identification for inventionBig Data Fast, Parallel Map Reduce ground outlier realisation for taradiddle and irreverence sleuthingPooja Vijay Pawar Ann Marie JoyAbstract wholeness of the virtually challenging aspect of Big Data analytics is real time observe of info. Larger and galacticr amount of info is creation collected and stored on a daily creation, thus increase the compulsion for quick, effective and businesslike way of analysing the info in determining potential malicious selective information. in either episode cyberspace security threats ar increasing at an alarming array and atomic sum up 18 becoming increasingly sophisticated and difficult to detect. Web profession founderd by non-human activities such as botnets or worms consume network resources, deceive people and affect network security. Most of the existing work in Fraud espial/Intrusion staining regard beingness an outlier as a binary property . In this paper we use a congeneric minginess establish approach utilize as a MapReduce job, which forms a sense of degree of a selective information sharpen being an outlier this is much meaningful and similarly conk out immune to false positives.KeywordsBig Data, Fraud spotting, Intrusion Detection, Hadoop, Outlier, Cluster, Security, Relative compactness, LOF1. IntroductionWe atomic number 18 currently living in a world where we be surrounded and ruled by data. Continuously, exp singlentially huge amounts of data is collected, stored, processed and made available in a variety of forms e truly day. Recently, Network Intrusion and Fraud catching has stock annexd attention with regard to network security, brinyly delinquent to this.Big Data is drasti environy changing the way in which we detect artifice and intrusion in real time utilise advanced analytic solutions that be precise powerful, complex and fast. In this paper, we propose a methodological analysi s to detect Fraudulent Web occupation and Intrusion in a network using MapReduce-based outlier maculation. These features help in filtering out clients that generate sub shape traffic and specifically show dissimilar levels of potential anomalous traffic for distributively suspicious client. The spy abnormal web traffic stack be visualized easily and this method exhaustively deal be implemented for gravid net works and atomic number 50 be scaled harmonisely.Outlier sack be define as a data which is very distinct from the an pause(prenominal) data of the aforesaid(prenominal) dataset, based on some blank space measure. Outlier detection, being a signifi female genitalst data mining problem has engendered a lot of enquiry involution in the recent past. As a result, divers(a) methods for outlier detection devote been developed particularly for dealing with numerical data. However, outlier detection for monotone data still remains an unexplored field. Addressing th is requirement, we propose a two-phase algorithmic rule for detecting outliers in two-dimensional data based on a novel definition of outliers. This algorithm initially explores a clustering of the effrontery data which is fol mooed by the ranking phase for determining the set of some likely outliers. The proposed methodology is expected to show better results as it backside identify antithetical fictional characters of outliers, using independent ranking scheme based on the inherent clustering anatomical structure of the granted data.Hadoop is a very prevalent open source Apache project, which is apply for storing and processing huge saturation of data on commodity hardw ar. Hadoop package primarily consists of MapReduce Engine and Hadoop deposit system. Many Frameworks gravel been built on top of Hadoop. Using the distributed computer architecture of Hadoop in this paper we discuss how we can pink it for identifying outliers in seizured network data.2. Related Work thither has been lot of explore and number of proficiencys which start out been developed in the past two decades with adore to fraud detection and Intrusion Detection. Lot of machine get h superannuated ofing proficiencys such as unquiet Networks, Markov model, K Ne atomic number 18st Neighbour earn drawn special attention. In this paper we use unsupervised Machine Learning techniques to identify duplicitous proceeding using Hadoop.Most current practices to the process of detecting intrusions exploit some form of rule-based study. Rule-based analysis depend on sets of predefined rules that ar tack by an administrator and be automatically make believed by the system. The use of machine-driven system techniques in intrusion detection mechanisms was a significant milepost in the development of effective and practical detection based tuition security systems. Rule-based systems suffer from an incompetence to detect attacks situations that may fall out everywhere an exte nded duration of time.However close to significant reach of anxious networks in intrusion detection is the capability of the neural network to learn the characteristics of abnormal attacks and recognize patterns that are unlike any which have been detected before by the network.Majority of studies that proposed Hidden Markov Model to implement IDS are cogitate to host-based systems, i.e., IDS that analyses the action at laws performed on a single host to detect attempts of intrusion.3. Outlier Detection algorithmsOutliers can be detected using various polar techniques. near of the techniques are discussed under3.1 Distance-based and flock ApproachesDistance-based methods do not make conventions for the data since they basically compute the blank surrounded by each power point. For example, Knorr et al. Proposed a k-NN technique where, if m of the k close neighbours of a point are within a specific distance d, then the point can be buildified as normal. Knorr et al. points as an outlier if at least p% of the points in the dataset lie much(prenominal) than distance 10 d from it. These methods show eminent computational complexity (e.g. nearest neighbour based methods have quadratic equation complexity with respect to the number of data points) which renders them impractical for really large datasets. Several approaches may be engaged to make the k-NN queries faster (to fulfill withdrawar or logarithmic time), such as an indexing structure (e.g. KD-tree, or X-tree) however these structures have been sh birth to break d accept as the dimensionality fathers.Clustering can be defined as the task of grouping a set of physical marks in such a way that objects in the same group share some feature similarity among each separate than to those in other groups. Clustering is one of the very popular techniques currently being used in outlier detection.Any object that has weak membership to the cluster it be massives to is a potential outlier. I f in that location are any petty clusters from other clusters, then the smaller cluster could potentially be an outlier. For instance there could be many different kinds of fraudulent transactions which readiness have full(prenominal)er(prenominal)(prenominal) similarity among themselves and form a cluster.There are additional problems with clustering. Clustering algorithms are optimized to find clusters instead than outliers. Hence, sometimes it may be hard to tell whether a cluster belongs to fraudulent transactions or some saucy emergent secureing behaviour of a legitimate user. Hence before making a final call we must perform additional analysis.3.2 Statistical diffusionStatistical outlier detection was one of the most basic approaches dating back to the 19th century. variable statistical methods have been proposed, together with use of robust outliers estimates of the 2-dimensional statistical distribution parameters, e.g. minimum covariance 9 determinant (MCD) and minimum volume ellipsoidal (MVE). hotshot critical problem of statistical-based methods is the suitable model for each dataset and application. alike, as data rises in dimensionality, it becomes ever to a greater extent perplexing to estimate the four-dimensional distribution. As the data increases in dimensionality, data ranges finished a larger volume and becomes sparse. In addition to the reduction this has on performance, it besides spreads the lentiform hull, thus altering the data distribution. This can be change by pre selecting the most noteworthy features to work, projecting to a reject-dimensional subspace, or applying Principal portion Analysis (PCA). Another methodology to deal with utmoster dimensionalities is to organize data points in convex hull layers according to their peeling depth, based on the idea that outliers are data objects with a shallow depth tactual sensation upon. figure of speech 1In this approach one main assumption is that data objects follows a certain distribution (E.g. Gaussian) and normal data objects occur in a spunky up probability region of this model.Fig.1 shows an example where there is high concentration of data points lying in the normal region which associates to normal data points the mini distributions on both sides of the normal distributions are executable outliers. As shown in Fig.1 outliers give deviate powerfully from this distribution.There are lot of issues with this technique too, main being the curse of dimensionality, other being lack of robustness. This is because Mean and standard deviation are very rude(a) to outliers.3.3 Density Based approachIn this technique we compare the closeness nearly a data point with the density around its topical anesthetic neighbours. The computed density is called an outlier score. The main assumption here is that the density around a normal data point is almost similar to the density around its topical anaesthetic neighbours. Here Density/Outlier score means that some clusters are obtusely packed and some others are not. Mathematically, it is defined as the reverse of the average distance to the k nearest neighbours. humbleder density of a data point signifies that the probability of it being an outlier is very high. There have been many variants of Density based approaches suggested in the past few decades, bulk of which deal with decreasing the computational time complexity. In Fig.2 the points which are densely packed, appearing yellow indicate normal data points, the ones which are away(p) from the cluster are outliers, assertable candidates for malicious data. In this paper, we use this technique to settle down possible candidates for outliers.4. ExperimentFor our experiment we used KDD Cup 1999 ready reckoner network intrusion detection dataset for testing and evaluating our approach. We used Relative Density 3 based approach for our system. Which involved 4 Map Reduce Tasks. The algorithmic programs works as fol lowsComputing K-NN 3We begin with the notion of K-Nearest Neighbour of object p. translation K-Nearest Neighbour of object pFor any positive integer k, the k-distance of object p, denoted as k-distance (p), is defined as the distance d(p,o) between p and an object o D such that(i) For at least k objects oDp it holds that d(p,o) d(p,o), and (ii) For at most k-1 objects oDp it holds that d(p,o) .Given the k-distance of p, the k-distance neighbourhood of p contains every object whose distance from p is not greater than the k-distance, i.e.Nk-distance (p) (p) = q Dp d(p, q) k- distance(p) These objects q are called the k-nearest neighbours of p.We use 2 MapReduce tasks, one to compute the pairwise distance between data points as explained preceding(prenominal) and other to compute the density of the data point. The density of a data point is simply the inverse of the average distance to the k nearest neighbours.Finding all the neighbourhood group the data points are associated wi th and everywherely give them grotesque idWe define one more term reachability distance of an object p w.r.t the data point o to determine the neighbourhood.definition Reachability distance of an object p w.r.t. object orLet k be a natural number. The reachability distance of object p with respect to object o is defined asreach-distk(p, o) = max k-distance(o), d(p, o) .The high the observe of k, the more similar the reachability distances for objects within the same neighbourhood.We use the same MapReduce cast as before with slightly different conformation to identify the neighbourhood. at one time neighbourhood are identified they are given a unique ID.Using previous results, create a mapping between data point and its density.In a typical density-based clustering algorithm, there are two parameters that define the notion of density(i) a parameter MinPts specifying a minimum number of objects(ii) a parameter specifying a volume.These two parameters determine a density thres hold for the clustering algorithms to operate. That is, objects or regions are connected if their neighbourhood densities exceed the given density threshold. To detect density based outliers, however, it is necessary to compare the densities of different sets of objects, which means that we have to determine the density of sets of objects dynamically. Therefore, we keep MinPts as the only parameter and use the values reach-distMinPts(p, o), for o NMinPts(p), as a measure of the volume to determine the density in the neighbourhood of an object p.Definition Density of an object plrdMinPts(p) = 1/Intuitively, the local reachability density of an object p is the inverse of the average reachability distance based on the MinPts- nearest neighbours of p. Note that the local density can be if all the reachability distances in the summation are 0. This may occur for an object p if there are at least MinPts objects, different from p, but sharing the same spatial coordinates, i.e. if there are at least MinPts duplicates of p in the dataset. For simplicity, we impart not handle this case explicitly but simply assume that there are no duplicates.Hence in our MapReduce implementation, source we sort the data points based on density data and the neighbourhood, such that in the input for the reducer, we get first value as density, and the subsequent values are the neighbourhood ids. ascertain the Relative Density or LOF (Local Outlier Factor)Results from the previous step is then used in another MapReduce task to compute the relative density or also called as Local Outlier Factor (LOF).DefinitionLOFMinPts(p) = The outlier factor of object p captures the degree to which we call p an outlier. It is the average of the ratio of the local reachability density of p and those of ps MinPts-nearest neighbours. It is light-headed to see that the lower ps local reachability density is, and the higher the local reachability densities of ps MinPts-nearest neighbours are, the higher is the LOF value of p. In the succeeding(a) section, the formal properties of LOF are made precise. To alter notation, we drop the subscript MinPts from reach-dist, lrd and LOF, if no confusion arises.Finally, Data Points which have low relative Density or LOF are determined as possible candidates for outliers.5. ConclusionExisting Intrusion detection system are in nascent stage in handling extremely large traffic and the data transfers in large Networks. MapReduce Framework can handle large amount of data quickly and cost-efficiently. Thus our proposed methodology for Outlier detection using Relative Density based approach not only can handle large amount of data but also scales easily. In near future full of MapReduce based IDS inescapably to developed and evaluated. We also plan to explore multiple branchifier system compared to single classifier to get improved results.6. Ack at one timel advancementThis work is supported by CSE Department, PES Institute of Technology.7. Re ferences1 Barnett, V., Lewis, T. (1995). Outliers in Statistical Data. Wiley, 3rd Edition. 2 Davide Ariu, Giorgio Giacinto, and Roberto Perdisci, Sensing attacks in Computers Networks with Hidden Markov Models. 3 Ng, Jorg Sander, Hans-Peter Kriegel, Raymond T, Markus M. Breunig. LOF Identifying Density-Based Local Outliers 4 Manh Cong Tran, lee side Hee Jeong, Yasuhiro Nakamura. Abnormal Web Traffic Detection Using Connection Graph. 5 Suri, N.N.R.R. center of attention for AI Robot., Bangalore, India Murty, M.N. Athithan, G..An algorithm for mining outliers in categorical data through ranking. 6 Kuo Zhao, Liang Hu. Intrusion Detection and Pr pull downtion in high speed network.7 Qing He Yunlong Ma, Qun Wang, Fuzhen Zhuang, Zhongzhi Shi.Parallel Outlier Detection Using KD-Tree Based on MapReduce 8 Koufakou, A. Sch, FL Secretan, J., Reeder, J., Cardona, K., Georgiopoulos, M.Fast parallel outlier detection for categorical datasets using MapReduce. 9 Ganesh Ananthanarayanan, Srikant h Kandula, Albert Greenberg, Ion Stoica, Yi Lu, Bikas Saha, Edward Harris . Reining in the Outliers in Map-Reduce Clusters using Mantri. 10 H. Gunes Kayack, A. Nur Zincir-Heywood, Malcolm I. Heywood. Selecting Features for Intrusion DetectionA Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets. 12 E. Eskin, A. Arnold, M. Prerau, L. Portnoy, S. Stolfo, A geometric framework for unsupervised anomaly detection Detecting intrusions in unlabeled data, in Applications of Data minelaying in Computer Security, Chapter 4, D. Barbara and S. Jajodia (editors), Kluwer. 13 Q. He, F.Z. Zhuang, J.C. Li, Z.Z. Shi. Parallel implementation of classification algorithms based on mapreduce. transnational Conference on Rough Set and Knowledge Technology. 15Koufakou, A., Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G., Reynolds, K., A climbable and Efficient Outlier Detection Strategy for Categorical Data, Intl Conference on Tools with slushy Intelligence ICTAI, October, 2007. 16 Big Data Analytics for Security Intelligence, CLOUD SECURITY alliance September 2013. 17 DuMouchel W., Schonlau M. A Fast Computer Intrusion Detection Algorithm based on Hypothesis Testing of Command Transition Probabilities, Proc. quaternary Int. Conf. on Knowledge Discovery and Data Mining, natural York, NY, AAAI Press, 1998, pp. 189-193. 18 Ramaswamy S., Rastogi R., Kyuseok S. Efficient Algorithms for Mining Outliers from Large Data Sets, Proc. ACM SIDMOD Int. Conf. on focussing of Data, 2000. 19 Fawcett T., Provost F. Adaptive Fraud Detection, Data Mining and Knowledge Discovery Journal, Kluwer Academic Publishers, Vol. 1, No. 3, 1997, pp. 291-316. 20 Holtz, Marcelo D., Bernardo M. David, and Rafael Timteo de Sousa Jnior. Building Scalable Distributed Intrusion Detection Systems Based on the MapReduce Framework. Telecomunicacoes (Santa Rita do Sapucai) 13 (2011) 22-31. 21 DuMouchel W., Schonlau M. A Fast Computer Intrusion Detection Algorithm based on Hypothesis Testing of Comma nd Transition Probabilities, Proc. quaternate Int. Conf. on Knowledge Discovery and Data Mining, untested York, NY, AAAI Press, 1998, pp. 189-193.1Onida Electronics New overlap LaunchOnida Electronics New Product LaunchLAUNCHING OF A NEW PRODUCT ONIDA laptop computerSCOMPANY DESCRIPTIONONIDA is one of the largest television manufacturing companies in INDIA. It is one of the largest and rapid growing companies in the same field. Onida as a ac community was founded in 1981 as a public smart set. Its head draw in is in MUMBAI, MAHARASHTRA, INDIA. Onida deals in a type of perseverance known as electronic indus distort.Onida family started their business with electronics and then expanded their business with various gains as LCD TVS, PLASMA TVS, TELEVISIONS, DVD AND HOME THREATER SYSTEM, AIR CONDITIONERS, backwash MACHINES, MICROWAVES, PRODUCTS RELATED TO PRESENTATION, and INVERTERS AND ALSO MOBILE PHONES.Onida phoner is a most popular fault now. It has got his network as 3 3 branch offices and 208 guest intercourse centers. Also the guild is having 41 depots spread all over the country. Onida is also having a grocery capitalization of 400 crore approx. also onida electronics won an awarding as AWARD FOR EXCELLENCE IN ELECTRONICS in 1999 from MINISTRY OF INFORMATION TECHNOLOGY, presidential term OF INDIA.EXECUTIVE SUMMARYOnida is one of the largest rapid growing companies. It is one of the most successful companies in term of professionalism. The main objective is to prove case of competitive prices and client satisfaction. It is one of the objective is to show notebook PC as ensured feeling, availableness and guest satisfaction. This following merchandiseplaceing plan forms the basis for the ingress of ONIDA LAPTOPS by ONIDA COMPANY one of the famous come with in India.The analysis will allow us to follow for the doing of the follows strategic goals. ONIDA LAPTOPS will be grocery storeed to reinforce the companys status as drawing c ard in innovation and successful increase launching. This new result launching will enable us to add RS.100 crores to companys turnover with a forecasted sales evolution prospect of 10-15% over the next 5 years.And while grateful the need and designing the intersection to match individuals life-style. Success will be reflected by a sizeable capture of market shares within this market, while strategically carrying the company up to the top spot as the market leader in laptop segment. Export potentials in the market will be considered in all the western countries as USA, Russia, France, and Iraq. Nigeria, Yemen and even further elaborateness can be planed.The main aim of the company is to attract a sizeable market share of the laptop segment. Also the number of guests depending upon laptops rather than personal computers is taken financial aid off. The main aim is to treat one lakh units of laptops in the first year considering as this brand as a new brand and being expert ise in the colligate field having manufactured the crop for several brands and having also sold its own televisions.EXPECTED TURNOVERAbove chart shows the expected turnovers in crores over the coming years in turnover of onida laptops in various countries.In 2009 as the product is being launching in 2009 only so started expected turnover will be near approximately 90 lakhs. With the ceaseless supply of good flavor and good work with good client table service expected turnover will start increasing.Company is formulation to use new technology and other servicer beneficial for guests and company in terms of turnover which will help in increase the expected turnover to 150 lakhs in 2012. This proves that launching of onida laptops will be beneficial for the brass.SITUATIONAL ANALYSISONIDA notebook computer PC is a new involution line of onida. Onida television portfolio has been well received and now ONIDA NOTEBOOK PC is going to be launched for onidas continues success and future profitability. Onida as a company is famous for quality products at reasonable prices and it cleft best facilities. It will offer NOTEBOOK PC or LAPTOPS in various configuration as well as prices to satisfy every kind of customer. One issueant key to success or for the development of the product is to create product awareness and harvest-festival of customer base. merchandiseING SUMMARY market summary of onida laptops possess sufficient information nigh the market needs and customer needs, wants, desires and subscribe tos. It will also help to give right offer to right customer so that company can achieve customer satisfaction and can communicate with them in a better way. Also this lead to have some historic information well-nigh customer which will help to make customer a delightful customer. stigma MARKETThe target market of ONIDA LAPTOPS or NOTEBOOK PC is shown belowAbove shows that there is a target market of onida laptops as bodily USERSEND USERSOTHERSEnvironment al analysisMacro environmental factorsGrowing disposable income and low penetration levels would ensure greater share of wallet for the consumer industry and would help in achieving the predetermined targets. The inventions and innovations would help in shaping the industrys future. It would be necessary to catch the changing trends in consumer lifestyles and offer the right product at right time to facilitate further growth and achievement of targets. There is a need to launch a product which will give maximum customer satisfaction and satisfy there needs.micro environmental factorsThe cost would further come down with enhanced possibilities of better and easy cross border talks with suppliers worldwide and the range of offeringsWould go up. With the increase in number of players, there would be more action and the industry would feel the buzz around it and would need constant high pitch communication with the customers. Network has a satisfying influence on the perverting decisi on and the organization that would manage the channel better, would have a substantial edge over the others.Competitive StrategyOnida will try to offer more valuable offerings to the customers and would try to manage customer relationships. Onida as a company in order to be effective and in advance of competition would try to maximize benefits to the consumers. They will also react powerfully to price changes made by the competitors and organizations would look at integration on backend as well as the front ends.MARKET DEMOGRAPHICSThe profile of ONDA NOTEBOOK PC or LAPTOPS customer consists of the following GEOGRAPHIES, DEMOGRAPHICS, and BEHAVIOR FACTOR geographical FACTORSGeographical factors have been classified asONIDA NOTEBOOK will have specific domestic geographic target area. They will serve the product to domestic market.Onida will try to cover the Metropolitan area through their own distribution channel.DEMOGRAPHICAL FACTORSDemographical factors have been classified on the basis of the following attributesThere will be almost same featured notebook pc or laptop for the corporate, end users and other users.University teachers and other researchers can use it for their research work and analyzing the environment.High, Middle, Upper middle and middle class use it and reasonable pricing scheme will help to grease ones palms of these notebook pc by every potential users.BEHAVIOUR FACTORSIts a general behavior of every human that NOTEBOOK PC or LAPTOP increases the status and prestige of the user.In todays busy world there is need of a product which is available with consumer anywhere or any time and at every menses of time.Customer feel that they have a separate image and prestige by using ONIDA LAPTOPS.MARKET NEED FOR ONIDA LAPTOPSONDA LAPTOPS will provides its customers the opportunity to choose NOTEBOOKs with different configuration and varieties.These laptops will also fulfill the requirements of customers and also provide benefit as- intelligent pr icingConsumer needs a high quality product at reasonable price, for that reason ONIDA try to provide high quality product at a reasonable price. So that more and more customer can be attracted towards onida notebook pc.High qualityConsumer want high quality product, which is must be high in regard to performance. ONIDA time-tested to meet this need of consumer at its level best. Quality was the main point to be taken care of by the company as this the main motto of company.Different varietiesChoices of customer vary from person to person .So ONIDA provides different laptop with different features. And provide laptops with different configuration.Product availabilityOne of the important things to be taken care of is Customer and customer desire availability of product so that they can buy the product at any time they need.MARKET TRENDSONIDA will fix different type of laptop which not only fulfill quality aim of customer but also reasonable price. So that more and more customer wi ll purchase these laptops. Now a days NOTEBOOK PC production company is growing and more competitors are coming in the market. Now even customer are more aware about the product for that reason they want different types of product with different features and different styling features. The market for LAPTOPS with new and new technologies is growing faster, competition are increasing in these markets. Thus, ONIDA made segments for their product. They divided their customer in to three groups, and give emphasis on each of the group. So that company can capture all kind of customers in market.MARKET GROWTHGradually come in ONIDA Company is going upward with a strong competition. With the increase in domestic market, our international market growth statistics is increasing day by day. Now with the launch of new laptops with newer technology the market share of our company is going to increase further.COMPETITIVE STRATEGIESOnida will try to offer more value offerings to the customers an d would try to manage CUSTOMER RELATIONSHIP. The firms in order to be effective and ahead of competition would try to maximize benefits to the consumers. Firms would react strongly to price changes made by the COMPETITORS and organizations would look at INTEGRATION on BACKEND as well as the FRONTENDS.CURRENT MARKET SITUATION OF LAPTOP MARKETMarket OverviewLuxury goods are now being comprehend as necessities as now customers are having higher disposable incomes being spent on lifestyle products. There is a discernible discharge in the consumers gustatory perception in favor of higher-end, technologically superior mark products, the demand being spurred by increasing consumer awareness and preference for new models now the modern educated customer is not confined to old technologies and old products. Now customers want to try newer products with newer technologies.Quality products with superior technology and technology up gradation have helped the industry to achieve higher growt h in terms of volume and also higher realization in value terms. Rate of growth in production has been more in terms of quantity or in volume growth rather than the growth in value terms for a number of products. This has happened because of constantly falling prices over the years due to competition among the major(ip) PLAYERS, AGGRESSIVE trade STRATEGIES AND DECLINING IMPORT TARIFFS.Competition has forced companies to offer efficient AFTER SALE SERVICE and support and this, in turn, has swayed customer preference for good brands. There are positive growth trends in expert goods segments white goods and consumer electronics during and points to sustained growth because of emerging opportunities and strong fundamentals of the economy.Because of growth in production in the organized segment and domestic availability of branded products due to lowering of import duties and other liberalmeasures, the share of uncoordinated segment has come down sharply to only 8 to 10 per cent from 40 to 50 percent. The price difference between branded and unbranded goods has narrowed down and with branded players providing good afterward(prenominal) sales services and support consumer prefers to buy branded products.The industry related to technology appears to have two clearly differentiated segments. The MNCs have an edge over their Indian counterparts in terms of technology combined with a steady flow of capital. The domestic companies compete on the basis of their well-acknowledged brands, an all-embracing distribution network and an insight into local market conditions. Competitive strategies wave around strong brand differentiation and prices.Bargaining power of customers is high due to availability of many brands. direct is Cyclical and seasonal. Demand is high generally on the basis of requirement of customers as corporate customer or end user. Demand for technology is present throughout the year. There is no preference on the basis of any special calendar month when demand for technology is more in one month than other month.Rural India which accounts for nearly 70% of the total number of households, offers plenty of stage setting and opportunities for the white goods industry. Increasing consumer awareness and preference for new models have added to the demand in rural areas also. And evolution of education had made more and more people rely on technology.Attractive consumer loan schemes with reduced interest rates over the years by the financial institutions and commercial banks and the never-never schemes have added to the surge in demand. Besides, the consumer goods companies arethemselves coming out with fascinating financing schemes to consumers through their extensive dealer network. The usage of earnings by the market functionaries has lead to intelligence sales of the products. It has helped sustain the demand bellow witnessed recently in this sector.SWOT ANALYSIS(STRENGTHS, WEAKNESSES, OPPORTUNITIES, THREATS)STRENGTHSQuality product and services. public brand name.Continuous research and developmentStrong existing distribution channel.Increased share of organized sector as compare to unorganized sector.Attractive design.Body made of silver and plastic and available in many colors.Most of the buyers are satisfied.Presence of established distribution networks in both urban and rural areas.WEAKNESSESPoor government spending in infrastructure and other related things.Lack of promotional activities by companies.Low purchasing power of consumersInsufficient capital.Legal and political barrier regarding import, revenue and shipment and other difficulties.Difficulties due to competitors.Old technologies.OPPURTUNITIESLow penetration levelsDemand of NOTEBOOK PC is increasing.Improved market portfolio.Greater demand due to changing lifestylesIncreasing sales throughout the country and internationally.Availability of easy finance.Promotional activities to increase brand image.Growing disposable income of consumer sIncreasing demand in rural sector.THREATSStrong competitorsLegal political difficulties regarding import duty, task etc.Entrance of new competitors in the market.The price of NOTEBOOK or laptops is decreasing continuously.Cheap Imports from Singapore, China and other Asian countriesHigher import duties on raw materials imposed in the Budget 2008-09.COMPETITORSOnida has established its own market. But still they have to face the competitors. Some of the major competitors of onida laptops areHP HPs NOTEBOOK is having maximum market share in capturing the market of NOTEBOOK PC in India as well as internationally. HPS notebooks are produced in China. They are holding a leading position for long time in NOTEBOOK PC in our country.ACER genus Acer is the Chinese producer of NOTEBOOK or laptops. ACER has its maximum market share of NOTEBOOK PC in Bangladesh. They have fewer shares in Indian market as compare to acre.COMPAQ Mitsubishi is Taiwan Company. COMPAQ was an automobile company in itially. Now it is producing NOTEBOOK PC also. It is now giving a strong competition to notebook or laptop market. dingle Firstly they produce televisions. But now they have enhances its business by producing NOTEBOOK PC. And DELL is now giving a strong competition in laptop market as well as technological market.MARKET handle OF ONIDAPRODUCT OFFERINGONIDA will offers different type of configuration at different price to our consumer for chooses their expected product. Following are the items-POINTS OF SUCESSStrong qualityBetter customer relationship managementBetter service practicable efficiency and integrationEffective channel managementConstant product change and product mix managementImage of brand and productDistribution channelRetention of customerMARKETING STRATEGIES merchandise strategies are helpful in creating awareness, interest and appeal from our target market. So that more and more market can be capture.Basically merchandise strategy is based on superior performance of the following areasDifferent configuration.Product quality.Delight user.MISSIONThe main mission is to provide quality and product at a competitive price. Growth in diversity and continuous contribute to the growth is being the main market challenger. Also To benefit society at large through Innovation, Quality, Productivity, Human Development and Growth, and to generate sustained surpluses, always striving for excellence, within the framework of law, and in zero point but the truth in which we base every action3.2 Marketing ObjectiveCapture the market as a market leader.To gather quantitative and qualitative leadership in the technological sector.Maintaining positive and change magnitude sales growth than the competitor.Increase the market share by market development and services.To increase product awareness and sales by convincing promotional activities.Becoming a globally recognized and prestigious company through synergistic business investment.Differentiation through in novation and choler through empowerment.Also cost through economies of scale and world class systems and procedures that bring in delight of stakeholders.FINANCIAL OBJECTIVEThe company aims to betray one lakh units of NOTEBOOKS in first year of its launching and is expecting a target turnover of Rs.100 Crores from laptop market in first year.It expects to grow at 10-15% in next tail fin years by satisfying customer needs through its offerings.They aim to make considerable cyberspace and achieve economies through backend and front end integration.And maintaining double digit each year.STP (SEGMENTATION, TARGETING, POSITIONING)SEGMENTATION OF MARKETThe company has segmented its target market on the basis of incomes and lifestyles.People who are well educated, have lifestyle as an important element and have high income can buy laptop.TARGET MARKETThe potential consumers are separated into various segments- collective User End User and others. The primary merchandising opportunity i s sell laptops to these well defined, accessible target market segments. merged User Corporate users are the users who buy the laptops for their official heading only. The MD, GM, DGM, CEO and other.Levels in offices are the main corporate users.End User End users are the users who buy the laptops for their personal use. Teachers, Students of private University, businessman etc are mainly consider as the End users.Others other then as mention above.POSITIONING IN MARKETOnida is trying to maintain its position as a NOTEBOOK company. The position will be achieved by providing quality product, competitive price, and according to consumers demand and by delighting consumers. There should be experienced managers to make awareness about the product to customer. Company is also promising to offer quality offerings and better services and make a satisfied consumer as its brand ambassador. The companys will advertise its product as the synonym of truth and providing it at a reasonable price .STRATEGIESONIDAS main primary marketing strategy is to render and firstly create customer awareness regarding the products as well as availability of product.Other marketing strategy are-Providing Total Quality Management (TQM)Customer OrientationProviding international standard productsTo increase the product line as well as length as per the expectations of the consumersCompetitive prices of product.MARKETING MIXONIDAS marketing mix is comprised of the following approaches to product, price, promotion, and place or customer service.PRODUCTo BRAND NAMEThe name of the brand is ONIDA NOTEBOOKS.o PPRDUCT CLASSIFICATIONONIDA NOTEBOOK has five types of product. These are-W125U-T3000, W3001U-T4150, W4200-T4500, W4510U, W5520Uo QUALITY OF PRODUCTONIDA is mainly popular for the maintaining of high quality of its products. Total Quality Management (TQM) is rigorously practiced here.o DESIGNAttractive Design, Color, configuration, Comfortable weight.o boxingONIDA supply the notebooks to th e users with attractive packaging. It provides special cartoon with strong handling of the NOTEBOOKs to its dealers.o SizeThe products size vary from to one product serial to other product serieso ServiceONIDA provides best after sales service and take feedback for its products.PROMOTIONo SALES PROMOTIONONIDA provide initially shot-term incentives to encourage and purchase or sale of its products. Occasionally company has intractable to give special discounted price for its products. Company also opinionated to give T-shirts, caps, bags etc. to lure the customers.o ADVERTISEMENTCompany decided to gives advertisement for ONIDA NOTEBOOK through newspaper, billboard, popular magazine, leaflets, sponsoring on game competition, internet etc.o PERSONAL SELLINGONIDA company is also deciding to sell laptops through personal selling but till now we havent arrange any kind personal selling.PRICEo advert PRICINGPricing of the product are being made on the basis of technology used in the pr oduct and depending upon the various series of onida. The various pricing of various varieties of laptops areo DISCOUNTCompany has decided to allow discount facilities to all the retailers and dealers.o Payment periodONIDA will sell NOTEBOOK on credit to its distributor and retailers and after the sale e period of 2 months can be given for making payment.PLACECHANNELSONIDA has its own distribution channel for the distribution of their products.DISTRIBUTORONIDA have own distribution channel for distributing their products.LocationONIDA covers territory areas of our country to capture the market for their products.TRANSPORTION FACILITIESONIDA has its own transportation facility for its distributors. Distributors are themselves responsible for taking the products to their showrooms.MARKETING RESEARCHOne of the important things is research work. Research is very vital for any company to know about current market position and also to predict future needs. knowledge is collected through the dealer and retailer.o Question How did you hear about our ONIDA NOTEBOOK product? note the answer and this answer can be use for the promotional activities.o Customer suggestion New feedback service and customers suggestions system to gain additional information. They want to know from the customers about- What suggestion do you want to give to company to improve our product? Why do you need a laptop and why onida laptop only?FINANCIALSIt is very important while the launching of a product to take care of the financial overview of ONIDA NOTEBOOK related to marketing activities. ONIDA address brake even analysis (BEA), sales forecast, expense forecast, and showed how this activity are link to the marketing activity.BREAK EVEN ANALYSISBreak even is a point where cost is equals to sales. The break-even analysis below shows the number of single sales, or units, that we must realize to break-even. Analysis of breakeven point is based on the cost and the sales of a company.SALES dep endSales forecast means planning or estimation of sales so that future sales can be assessed. ONIDA thinks that the sales forecast will be achieve into the five main streams W1250U-T3000, W3001U-T4150, W4200U-T4500, W4510U and W5520U. This will steadily increase the sales. As the advertising budget allows the target market forecast, the listed of all the potential customers get divided into separate groups. The forecasted customers group divided into various categories Corporate Users, End Users and others.EXPENSE FORECASTEvery company has to make a prior forecast of expenses. It is important to mark such forecast to limit the expenses made in future. These expenses are to be budgeted at approximately 5% of total sales for 2007-08 and 6% for 2008-09. Mainly expenses are to be tracked in the major marketing categories as-NEWSPAPER ADVERTISEMENT, PROMOTIONAL EVENTS, BILLBOARDS, PRINTED LEAFLETS, and ADVERTISEMENT IN TELEVISIONS ETC.CONTROLING FACTORSThe main purpose of ONIDA NOTEBOOKs marketing plan is to serve as a guide for the organization while launching of the laptops successfully. This plan is all about implementation and changing the business and also to make it better. In this marketing plan we look at specific implementation programs, and the details that it takes to make it happen. The following are the areas which will be specially monitored to enhance the performance of company and launching of laptops.Contingency PlanningContingencies likely to arisePrice WarNew Technologies in the marketMore CompetitorsCompanys Strategy to counter the sameCreating value prepositionContinuous innovation and product developmentCreating distinct brand individualism