Analysis of Data Leakage Prevention Solution

Analysis of entropy relief valve legal community resolventAnuja Vasant Kale, Vishwajeet Bajpayee, Shyam P DubeyABSTRACT In whatsoever plaque or administration we imbibe to push-down storage with lots of entropy. Those entropy whitethorn contain undercover instruction about the customer, project related selective information, employee personal selective information and so by If such oddball of secret info is leaked from the organization then it may affect on the organization health. Hence we ease up to enforce almost policies in order to prevent entropy leakage. data leakage is a qualifying of data which tidy sum be totalred on any reposition device where the data is stored. There ar two slipway in which data can be leaked if the system is hacked or if the internal picks on purpose or unintentionally make the data public. If the system is hacked then we have nigh existing technologies like antivirus, firewall etc which can prevent data from leakage. We entrust discuss here the second scenario where we provide data leakage streak replys. We make the hold of a bayesian theorem for maintaining privyity of data in an organization.KEYWORDS reactive data, data leakage, internal attack, external attack, data leakage ginmill, bayesian admittance. doorwayNowadays, Information Security became a vital and a major subject, oddly with the spreading of information sharing among private and public networks for all organizations crossways diametrical industrial sectors (e.g. telecom, banking, fostering all over the world). The importance of securing information is playing a significant role especially when sharing, distributing, accessing and publishing any information that had been classified as a affectionate, either for the organization itself or the clients who sharing their private information with the organization, such as information stored, sh atomic write down along 18d, distributed and viewed by capacity of the electron ic documents systems and/or images of paper documents systems widely used by a lot of organizations.Many of organizations have given a great deal of heed has been given to protect their reactive data from the outback(a) threats by utilise a set of bail countermeasures like intrusion prevention systems, firewalls, and comement of the vulnerability points inside them. So, organizations must now turn their attention to an equally critical situation that forms -for them- a great challenge today, that is the task of data leaking or injustice from the inside.In fact, in many organizations thithers a gaping hole in controlling, monitoring, and protecting its fear environment and electronic data assets from leaking or passing play to the wrong individuals or groups intentionally or accidentally. This hole is the now ubiquitous in businesses, health, education organizations and individuals who need needed to communicate with to each iodin other over the internet network.In our d ays, many of the electronic communications heavily used inside any organization for many purposes, for instance local mail, instant messaging, weathervane mail, data files transferring, and to a fault organization website tranquillize go largely to different destinations without any limitations, monitoring, and controlling on its movements from the organization. Thus, the expected result for this issue is in that location is a big potential for the organization confidential information be falling into the wrong hands. Surely, from this significant point, the organization culture medium data should be protected very well, otherwise will be facing tragical results like business loss, damaged spirit, bad publicity, loss of strategic customers, and loss of competitiveness with the other organizations.As a result, any organization using similar electronic document system must keep a close eye to punch spiritualist data that had gone forth/back finished this system or application to maintain reputation and business continuous, and ensure regulations, laws compliance, along with macrocosm different from others. One of the juvenile methodologies and technical solution has been raised to top is the info Leakage bar (DLP) solution, which is basically protecting sensitive data of an organization from being viewed by wrong individuals, whether from outside or even inside the organization. This basically mean that specific data can be viewed by totally a specific set of an authorized individuals or groups for them.Related workAs organizations progress into a more technological environment, the amount of digitally stored data increases dramatically. As a consequence, keeping track of where it is stored is no longer as flaccid as before. The modern workforce naturally creates and uses data sensitive to the organization to do their job. This data is then used across operate such as email, business applications and cloud-service, as well as being accessed from multiple devices, including laptops and diligent phones. In many cases it is even hard for the users to eliminate the amount of data they deal with themselves, and the (ir) responsibility doesnt end in that location. In addition, a user also needs to keep track of how sensitive data is and who should be allowed to access it.DLP is a recent type of guarantor technology that works toward securing sensitive data in an automated and non-intrusive fashion. through and through policies a DLP system automatically makes sure no sensitive data is stored, sent or accessed where it shouldnt be, while still allowing users to use the tools and services they choose and need to fulfil their tasks. Unlike traditional white- and blacklisting, the DLP only blocks the actions where sensitive data is involved, e.g. pointing e-mails is perfectly acceptable, but not if they contain sensitive data. DLP can also be set to handle different levels of sensitivity and document access control. To quote Geo rge Lawton DLP systems keep people from deliberately or inadvertently sending out sensitive material without authorization 9.In addition to protecting sensitive data, a modern DLP should be adaptive, mobile and as tokenishly intrusive as possible 7. Adaptive pith that it can work in different environments and be configured to graceful the needs of a wide range of different businesses. Mobile means that it can still protect the data, even when the device is used outside the company network. The products today only fulfil this to a certain degree. DLP is still maturing, but unlike a few years ago, most vendors have standardized on the core functionality that defines a modern DLP solution.Proposed SystemIn Todays business world, many organizations use Information Systems to manage their sensitive and business critical information. The need to protect such a key component of the organization cannot be over emphasized. Data deprivation/Leakage Prevention has been found to be one of t he effective ways of preventing Data Loss.DLP solutions detect and prevent unauthorized attempts to copy or send sensitive data, both intentionally or/and unintentionally, without authorization, by people who are authorized to access the sensitive information.DLP is aspirationed to detect potential data breach incidents in timely manner and this happens by monitoring data. Data Loss Prevention is found to be the data leakage/loss control instrument that fits naturally with the organizational structure of businesses. It not only helps the organization protect structured data but it also helps testimonial and leakage prevention of unstructured data.In any organization or institution we have to maintain lots of sensitive data or confidential data. These data may contain the confidential information regarding the projects customer privileged data or employee personal data if such type of confidential data is leaked from the organization then it may affect on the organization health.D ata leakage is a loss of data that occur on any device that stores data. It is a problem for anyone that uses a computer. Data loss happens when data may be physically or logically removed from the organization either intentionally or unintentionally.A data stored on any storage device can be leaked in two ways if the system is hacked or if the internal resources intentionally or unintentionally make the data public.Hacking can be prevented by conservatively configuring your Firewalls and other aegis devices. We will be discussing the second scenario i.e. if an internal resource makes the sensitive data public. Consider the possibility of an employee leaking the sensitive data. Now there are various ways in which data can take the organization via internet, Email, webmail, FTP etc. Consider the possibility that an employee needs to transport the confidential data through Email or and uploading those files on to a server which can be accessed by outside world. Before compass tha t confidential data to the unauthorized person we need to enforce some policies in order to avoid the violation of the organization health.To achieve the basal requirement is to scan the whole outbound traffic. We will maintain the DLP (data connection prevention) server, which would scan the complete attachment to match the ptyalisens. In case the patter matches, the attachment will be corrupted with the User designed message and an automated response E-mail will be sent out. This mechanism is shown in the figure below.Figure 3 The process of Data Leakage Prevention (DLP) mechanism.As shown in the above figure, there is an internal employee of the organization who is trying to send the confidential data via email. Now, before reaching that confidential data to the unauthorized person we need to enforce some policies. For that we are using the Data Leakage Prevention (DLP) Server.3.1 Data Leakage Prevention (DLP) ServerData Leakage Prevention (DLP) is a computer security departm ent term which is used to identify, monitor, and protect data in use, data in motion, and data at rest 1. DLP is used to identify sensitive contentedness by using deep content analysis to per inside files and with the use if network communications. DLP is mainly designed to protect information assets in minimal interference in business processes. It also enforces protective controls to prevent unwished incidents. DLP can also be used to reduce risk, and to improve data management practices and even lower compliance cost.DLP solution prevents confidential data loss by monitoring communications which goes outside of the organization, encrypting emails which contain confidential information. We are enabling conformity with global privacy and data security in securing outsourcing and partner communication. To check whether an email contains confidential data or not, DLP server makes the use of a candid Bayes spam filtering.3.2 Naive Bayes AlgorithmNave Bayesian method is used for the development process. Analyze a mail to calculate its probability of being a Spam using individual characteristic of volumes in the mail.For each word in the mail, calculate the followingS (w) = (number of Spam emails containing the word)/(total number of Spam emails)H (w) = (number of Ham emails containing the word)/(total number of Ham emails)P (w) = S(w)/(S(w)+H(w))P (w) can be interpreted as the probability that a helter-skelter chosen email containing the word w is Spam.ExceptionsSay a word w =success appears only once and it is a Spam email. whence the above formula calculates P (w)=1.This doesnt mean that all prospective mails containing this word will be considered as Spam. It will rather appear upon its degree of belief. The Bayesian method allows us to combine our intuitive range information with this collected data.Degree of belief f(w)= (s*x)+(n*p(w))/(s + n)s=Assumed intensiveness of the background information.x= Assumed probability of the background information .n= no of emails received containing word w.Combining the probabilitiesEach email is represented by a set of probabilities. Combining these individual probabilities gives the boilersuit indicator of spamminess.fishers MethodH= Chi_inverse (-2*ln(Product of all(f(w)), 2*n)S= Chi_inverse (-2*ln(Product of all(1-f(w)), 2*n)I= 1+H-S/2Here, I is the Indicator of Spamminess.A Genetic AlgorithmA mail can be divided into three parts physical structureFromSubjectGenetic Algorithm can be used to get an appropriate weight say , and for body part, from part and subject part.IFinal= *IBody+ *IFrom+ *ISubjectThe overall accuracy is a function of , and . Genetic Algorithm maximize the above function.Advantages of Bayesian MethodBayesian approach is self adapting. It keeps learning from the forward-looking spams.Bayesian method takes whole message into account.Bayesian method is easy to use and very accurate (Claimed Accuracy Percentage is 97).Bayesian approach is multi-lingual.Reduces the numb er of false positives.ConclusionSensitive Data leaking prevention became one of the most pressing security issues facing Organizations today. The most effective solution to the problem is to see Data Leakage Prevention solution (DLP) as a part of your overall security problem. This solution can be fully integrated with other security tools within organization, to form a comprehensive security strategy plan to protect these data properly. Data Leakage Prevention (DLP) solution can be used in effect in reducing intentional sensitive data leakage actions, through monitoring users actions and protecting three groups of organizations data data at rest, data in use, and data in motion. This solution can be regarded as integrated through achieving two main phases two layers of defence protecting sensitive data and securing sensitive data of organization alike. The organization also needs to create an Acceptable Use Policy (AUP) for users, and at the akin time ensuring both are compliant w ith organization policies. To avoid getting broad sided by a data leakage, organizations must evaluate their vulnerabilities and oppose appropriately by many ways like Endpoints protection, Gateway protection, and encoding data.References.1RichardE.Mackey, getablehttp//viewer.media.bitpipe.com/1240246133_118/1258558418_168/sCompliance_sSecurity_Data- Protection_final.pdf2 Bradley R. Hunter, Available http//www.ironport.com/pdf/ironport_dlp_booklet.pdf3Webspy,Availablehttp//www.webspy.com/resources/whitepapers/2008 WebSpy Ltd Information Security and Data Loss Prevention.pdf4 Data loss problems, Available http//www.webspy.com/reso urces/whitepapers/2009WebSpy Ltd-Information Security and Data Loss Prevention.pdf5 Report, the Office of the U.S. Trade Representative, Available http//www.ustr.gov/about -us/press- office/reports-and-publications/archive6 Lubich, H.P The changing roel of IT security in an net world, a business perspective Available http//www.terena.nl/conference/archi eve/tnc2000/proceedings/2A/2a2.html7Sithirasenan, E.Muthukkumarasamy, V., script N-Gram Based Classification for Data Leakage Prevention, Trust, Security and solitude in Computing and Communications (TrustCom), 2013 12th IEEE International Conference on 16-18 July 2013, 578 585, Melbourne, VIC, 13971211, 10.1109/TrustCom.2013.71.8Pham, D.V. Threat analysis of portable hack tools from USB storage devices and protection solutions, IEEE ISBN 978-1-4244-8001-29 http//en.wikipedia.org/wiki/Data_loss_prevention_software10http//www.cisco.com/c/en/us/solutions/enterprise-networks/data-loss-prevention/index .html11 Bai Xiaoping Wei Yuanfeng , Study on the signal spotting and simulation of universal serial bus 2.0 IP core circle system, SoutheastCon, 2007. Proceedings. IEEE , vol., no., pp.59-62, 22-25 March 200712 S. Jithesh and U. Naveen, Improved key management methodology for enhanced media security in IMS networks, New York, US Institute of Electrical and Electronics Engineers Inc., 2007, pp. 903-907.13 AK. Gupta, U. Chandrashekhar, S.V. Sabnis and F.A, Building secure products and solutions, Bell Labs Technical journal, Hoboken, US John Wiley and Sons Inc., 2007.3, pp. 21-3814 R.A. Shaikh, S. Rajput, S.M.H. Zaidi and K. Sharif, Comparative analysis and design philosophy of next generation unified enterprise application security, Piscataway, US Institute of Electrical and Electronics Engineers Computer Society, 2005, pp. 517-524.15 Data Leakage Prevention A newsletter for IT Professionals Issue 5.16 Data Leakage Detection SandipA.Kale1, Prof. S.V.Kulkarni2 Department Of CSE, MIT College of Engg, Aurangabad, Dr.B.A.M.University, Aurangabad (M.S), India1,17 Journal Of Information, Knowledge And Research In Computer Engineering Issn 0975 6760 Nov 12 To Oct 13 Volume 02, Issue 02 Page 534 Data Leakage Detection Nikhil Chaware 1,Prachi Bapat 2, Rituja Kad 3, Archana Jadhav 4, Prof.S.M.Sangve copyright to IJIRCCE www.ijircce.com 1