U.N.I.Q TECHNOLOGIES

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ABOUT US: User Login module provides the authentication of the user. It  checks whether the user is the correct person to access the resources checking the username and password (entered by the user) by comparing it with the information stored in the database.If new image is given to thEsystem is given by the INPUT FACE IMAGE. Here we use webcam for capturing device. Image Storage:mechanismsare enabled to recordsystem events, distinct evidence of legitimateactivities and intrusions will be manifested in the audit data [4].Because of the large amount of audit records and the variety of systemfeatures, efficient and intelligent data analysis tools are required to discoverthe behavior of system activities. KDD99Cup [5] dataset and theDefense Advanced Research Projects Agency (DARPA) datasets providedby MIT Lincoln Laboratory [6] are widely used as training andtesting datasets for the evaluation of IDSs [4], [7]–[9]. An evolutionaryneural network is introduced in [10] and networks for each specificsystem-call-level audit data (e.g., ps, su, ping, etc.) are evolved. Parikhand Chen [11] discussed a classification system using several sets ofneural networks for specific classes and also proposed a technique ofcost minimization in the intrusion-detectionproblems.Datamining generally refers to the process of extracting usefulrules from large stores of data. The recent rapid development in datamining contributes to developing wide variety of algorithms suitablefor network-intrusion-detection problems. Intrusion detection can bethought of as a classification problem:banking, trading stocks and foreign exchange, and online auction
have been developed. However, due to the open society of the Internet,
the security of our computer systems and data is always at risk.
The extensive growth of the Internet has prompted network intrusion
detection to become a critical component of infrastructure protection
mechanisms. Network intrusion detection can be defined as identifying
a set of malicious actions that threaten the integrity, confidentiality, and
availability of a network resource [1], [2].
Intrusion detection is traditionally divided into two categories, i.e.,
misuse detection and anomaly detection. Misuse detection mainly
searches for specific patterns or sequences of programs and user behaviors
that match well-known intrusion scenarios. While, anomaly detection
develops models of normal network behaviors, and new intrusions
are detected by evaluating significant deviations from the normal behavior.
The advantage of anomaly detection

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