Difference Between Firewall And Intrusion Detection System o681t

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A firewall is a hardware and/or software which functions in a networked environment to block unauthorized access while permitting authorized communications. Firewall is a deviceand/or a sotware that stands between a local network and the Internet, and filters traffic that might be harmful. An Intrusion Detection System (IDS) is a software or hardware device installed on the network (NIDS) or host (HIDS) to detect and report intrusion attempts to the network. We can think a firewall as security personnel at the gate and an IDS device is a security camera after the gate. A firewall can block connection, while a Intrusion Detection System (IDS) cannot block connection. An Intrusion Detection System (IDS) alert any intrusion attempts to the security . However an Intrusion Detection and Prevention System (IDPS) can block connections if it finds the connections is an intrusion attempt. Intrusion detection is a complementary security technology that attempts to analyze and identify any malicious traffic directed against your network. A firewall helps to screen out many kinds of malicious Internet traffic before it reaches your computer.but not detect all types of security attacks.

is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are , and .

Web usage mining is a process of extracting useful information from server logs i.e s history. Web usage mining is the process of finding out what s are looking for on the Internet. Some s might be looking at only textual data, whereas some others might be interested in multimedia data.



Web content mining is the process to discover useful information from text, image, audio or video data in the web. Web content mining sometimes is called web text mining, because the text content is the most widely researched area. The technologies that are normally used in web content mining are NLP (Natural language processing) and IR (Information retrieval). Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.

Web structure mining is the process of using graph theory to analyze the node and connection structure of a web site. According to the type of web structural data, web structure mining can be divided into two kinds: 1. Extracting patterns from hyperlinks in the web: a hyperlink is a structural component that connects the web page to a different location. 2. Mining the document structure: analysis of the tree-like structure of page structures to describe HTML or XML tag usage.

þ Web mining essentially has many advantages which makes this technology attractive to corporations including the government agencies. This technology has enabled ecommerce to do personalized marketing, which eventually results in higher trade volumes. The government agencies are using this technology to classify threats and fight against terrorism. The predicting capability of the mining application can benefits the society by identifying criminal activities. The companies can establish better customer relationship by giving them exactly what they need. Companies can understand the needs of the customer better and they can react to customer needs faster. The companies can find, attract and retain customers; they can save on production costs by utilizing the acquired insight of customer requirements. They can increase profitability by target pricing based on the profiles created. They can even find the customer who might default to a competitor the company will try to retain the customer by providing promotional offers to the specific customer, thus reducing the risk of losing a customer or customers.

þ Web mining, itself, doesn¶t create issues, but this technology when used on data of personal nature might cause concerns. The most criticized ethical issue involving web mining is the invasion of privacy. Privacy is considered lost when information concerning an individual is obtained, used, or disseminated, especially if this occurs without their knowledge or consent.[1] The obtained data will be analyzed, and clustered to form profiles; the data will be made anonymous before clustering so that there are no personal profiles.[1] Thus these

applications de-individualize the s by judging them by their mouse clicks. Deindividualization, can be defined as a tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics and merits.[1] Another important concern is that the companies collecting the data for a specific purpose might use the data for a totally different purpose, and this essentially violates the ¶s interests. The growing trend of selling personal data as a commodity encourages website owners to trade personal data obtained from their site. This trend has increased the amount of data being captured and traded increasing the likeliness of one¶s privacy being invaded. The companies which buy the data are obliged make it anonymous and these companies are considered authors of any specific release of mining patterns. They are legally responsible for the contents of the release; any inaccuracies in the release will result in serious lawsuits, but there is no law preventing them from trading the data. Some mining algorithms might use controversial attributes like sex, race, religion, or sexual orientation to categorize individuals. These practices might be against the anti-discrimination legislation.[2] The applications make it hard to identify the use of such controversial attributes, and there is no strong rule against the usage of such algorithms with such attributes. This process could result in denial of service or a privilege to an individual based on his race, religion or sexual orientation, right now this situation can be avoided by the high ethical standards maintained by the data mining company. The collected data is being made anonymous so that, the obtained data and the obtained patterns cannot be traced back to an individual. It might look as if this poses no threat to one¶s privacy, actually many extra information can be inferred by the application by combining two separate unscrupulous data from the . c (the analysis step of the Knowledge Discovery in Databases process, or KDD), a relatively young and interdisciplinary field of computer science,[1][2] is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management.[3] With recent tremendous technical advances in processing power, storage capacity, and interconnectivity of computer technology, data mining is seen as an increasingly important tool by modern business to transform unprecedented quantities of digital data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery. The growing consensus that data mining can bring real value has led to an explosion in demand for novel data mining technologies.[4] The related ÷ ÷÷, ÷ and ÷ refer to the use of data mining techniques to sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations. Data mining commonly involves four classes of tasks:[18] Op

Association rule learning ± Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

Clustering ± is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Op Classification ± is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbor, naive Bayesian classification, neural networks and vector machines. Op Regression ± Attempts to find a function which models the data with the least error.

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