Seminar report on "Intrusion Detection System"

In highly networked modern world, conventional techniques of network security such as user authentication, cryptography and intrusion prevention techniques like firewalls are not enough, owing to formulations of new attacks. Intrusion detection systems are becoming an important need for today’s networks. Anomaly detection is used as a part of intrusion detection systems, which in turn use certain data mining techniques. Data mining techniques can be applied to the network data to detect possible intrusions. The foremost step in application of data mining techniques is the selection of appropriate features from the data.

An intrusion is someone attempting to break into or misuse the system. An intrusion detection system (IDS) for short, attempts to detect an intruder breaking into the system or a legitimate user misusing system resources. The IDS will run constantly on the system, working away in the background, and only notifying the user when it detects something it considers suspicious or illegal. Whether the user appreciates that notification depends on how well the users have configured the intrusion detection system.

Note that there are types of potential intruders:
Outside Intruders: Most people perceive the outside world to be the largest threat to their security. The media scare over “hacker’ coming in over the Internet has only heightened this perception.
Inside Intruders: FBI studies have revealed that 80% of intrusions and attacks come from within organizations.

Book: Intrusion Detection by Rebecca Gurley Bace
Web resources: Guide to Intrusion Detection and Prevention Systems (IDPS) 
The Role of Intrusion Detection Systems
Download Power Point Presentation (PPT): Intrusion Detection & Network Forensics

Seminar report on "Digital Watermarking Applications and Advantages"

Digital watermarking is defined as the imperceptibly altering a work in order to embed information about that work. In the recent years copyright protection of digital content became a serious problem due to rapid development in technology. Watermarking is one of the alternatives to copyright-protection problem.

Digital watermarking can be classified as visible and invisible. The visible watermarks are viewable to the normal eye such as bills, company logos and television channel logos etc. This type of watermarks is easily viewable without any mathematical calculation but these embedded watermarks can be destroyed easily. In the case of invisible watermarks, the locations in which the watermark is embedded are secret, only the authorized persons extract the watermark. Some mathematical calculations are required to retrieve the watermark. This kind of watermarks is not viewable by an ordinary eye. Invisible watermarks are more secure and robust than visible watermarks.

The main characteristics of Digital watermark are:
Robustness: The watermark should be able to withstand after normal signal processing operations such as image cropping, transformation, compression etc.
Imperceptibility: The watermarked image should look like same as the original image to the normal eye. The viewer cannot detect that watermark is embedded in it.
Security: An unauthorized person cannot detect, retrieve or modify the embedded watermark.

Depending on the ability of the watermark to withstand normal signal processing operations, digital watermarking can be categorized as robust, fragile and semi-fragile watermarking. Robust watermarks are detectable even after some image processing operations has been performed on the watermarked image such as image scaling, bending, cropping, and so on. Robust watermarks are mainly used for copyright protection. Fragile watermarks became invalid even if a slight modification is done to the watermarked image. Fragile watermarks are mainly used for authentication purpose. Semi-fragile watermarks allow some acceptable distortion to the watermarked image. Beyond this acceptance level if any modification is done to the watermarked image, the watermark will not be detected.

Books: Digital Watermarking and Steganography: Fundamentals and Techniques by Frank Y.
Web resources: Digital Watermarking
Download Power Point Presentation (PPT): Digital Watermarking

Seminar report on "Security in Mobile Database Systems"

The importance of databases in modern businesses and governmental institutions is huge and still growing. Many mission-critical applications and business processes rely on databases. These databases contain data of different degree of importance and confidentiality, and are accessed by a wide variety of users. Integrity violations for a database can have serious impact on business processes; disclosure of confidential data in some cases has the same effect. Traditional database security provides techniques and strategies to handle such problems with respect to database servers in a non-mobile context.

For many businesses applications are going mobile that means using enterprise data in a mobile context, thus using a mobile DBMS. With these new developments the business data of an enterprise can be made available to an even larger number of users and a wider range of applications than before.

To work on business data anytime and anywhere is the major goal pursued by developing mobility support in database context. The confidentiality of mission- critical data must be ensured, even though most mobile devices do not provide a secure environment for storage of such data.
Security requirements that apply to a central company database should apply similarly and in an appropriate manner to the parts of the database replicated on mobile devices in the field. A mobile database security infrastructure is needed to accomplish this goal. When developing such an infrastructure we can benefit from the results of traditional database security work. But we also need to adapt the existing techniques and strategies to the mobile context, and we need to develop new ones that attack certain issues specific to use of database systems in a mobile environment.

Book: Mobile Database Systems by Vijay Kumar

Web resources: Mobile database
Download Power Point Presentation (PPT): Database Systems and Security Research – Past, Present and Future
Mobile Database Systems

Seminar report on "Information or Data Visualizing Techniques"

Visualization is the graphical presentation of information, with the goal of providing the viewer with a qualitative understanding of the information contents. Information can be in any form, like data, processes, relations, or concepts. Graphical presentation may entail manipulation of graphical entities (points, lines, shapes, images, text) and attributes (color, size, position, shape). Understanding of information involves detection, measurement, and comparison. It can be further enhanced via interactive techniques and providing the information from multiple views and with multiple techniques.

One of the active research and challenging task is representing and making sense of multidimensional data, partially due to the three- dimensional space we live in. Visualization techniques are powerful sense-making tools that support knowledge workers in their decision-making activities by stimulating visual thinking. While most of the scientific, engineering, and business data is multi-dimensional; i.e. datasets contain typically more than three attributes of data.

Characteristics of Data:

• Numeric, symbolic (or mix)
• Scalar, vector, or complex structure
• Various units
• Discrete or continuous
• Spatial, quantity, category, temporal, relational, structural
• Accurate or approximate
• Dense or sparse
• Ordered or non-ordered
• Disjoint or overlapping
• Binary, enumerated, multilevel
• Independent or dependent
• Multidimensional
• Single or multiple sets
• May have similarity or distance metric
• May have intuitive graphical representation (e.g. temperature with color)
• Has semantics which may be crucial in graphical consideration

The purpose of information visualization is the insights into data that it provides. Diversity analysis has got a great significance in economic and other branches of social science. Visualizing the Diversity Analysis gives significant importance in identifying the profitability of the commodities as well as the countries. The concentration measures such as entropyand Herfinahl-Hirschman Index (HHI) gives the significant results in identifying the Diversity nature of the countries as well as the commodities. We visualized the commodity exportdata along with the Concentration Measures using star coordinates.

Books: The craft of information visualization: readings and reflections By Benjamin B. Bederson, Ben Shneiderman
Web resources: Graphics & Visualization
Visualization and Graphics at UC Davis
Download Power Point Presentation (PPT): Scientific Data Visualization

Seminar report on "Privacy Preserving Data Mining"

Explosive growth in data storing and data processing technologies has led to the creation of large databases that record unprecedented amount of information. Consequently, with the increase in data storage and processing, concerns about information privacy have emerged. Data mining, with its promise to efficiently discover valuable non-obvious information from large databases, is particularly sensitive to privacy concerns. In recent years, data mining has also endeavored to become compatible with privacy.

Organizations provide assurance of individual privacy and data will be used only for the a well defined purpose. However, it is the common practice of an organization to use individual data for secondary purpose. By secondary purpose, it means that data is being used for which they were not collected initially. Many organizations sell the data to other organizations, which use these data for their own purposes. Thus, the data gets exposed to a number of parties including collectors, owners, users and miners; the privacy of individual is being questioned. Fruitful research has been produced by different researchers on the topic of privacy preserving data mining (PPDM). PPDM deals with the problem of learning accurate models over aggregate data, while protecting privacy at the level of individual records.

Recent research in the area of privacy preserving data mining has devoted much effort to determine a trade-off between privacy and the need for knowledge discovery, which is crucial in order to improve decision-making processes and other human activities. Mainly, three approaches are being adopted for privacy preserving data mining namely, heuristic based, cryptographic based and reconstruction based. Heuristic based techniques are mainly adopted in centralized database scenario, whereas cryptographic based technique finds its application in distributed environment. However, reconstruction based algorithms are well accepted in both centralized as well as the distributed environment.

Book: Privacy Preserving Data Mining (Advances in Information Security) by Jaideep Vaidya , Chris Clifton, Michael Zhu
Web resources: Mobile Privacy Preserving Data Mining
Download Power Point Presentation (PPT): Privacy Preserving Data Mining: Challenges & Opportunities

Seminar report on "Palmprint Authentication Application"

Reliable user authentication is becoming an increasingly important task in the Web-enabled world. The consequences of an insecure authentication system in a corporate or enterprise environment can be catastrophic, and may include loss of confidential information, denial of service, and compromised data integrity. The value of reliable user authentication is not limited to just computer or network access. Palmprint authentication is regarded as one of the efficient biometric trait. Palm consists of the reliable features like palm lines, ridges, texture, etc. Before extracting the features, we need to pre-process and segment the image to obtain the Region of Interest (ROI). While capturing the palm image we also get the fingers and other parts like wrist etc., Segmentation of the palm region from the captured image is an important step before extracting the features. Here texture features are being extracted. They consist of wrinkles, which are different from principle lines in that they are thinner and more irregular.

Texture features are being extracted using a technique, namely 2D-Log Gabor filter which has been used to extract the Iris features earlier. Same method can be applied to extract palmprint features and False acceptance rate as well as Genuine acceptance rate can be calculated to evaluate the performance of the biometric system.

Book: Palmprint Authentication by Zhang, D.D.
Web resources: Intramodal Palmprint Authentication
Download Power Point Presentation (PPT): Authentication in Security

Seminar report on "Computational Methods for Bankruptcy Prediction"

“Bankruptcy” is a legally declared inability or impairment of ability of an individual or organizations to pay their creditors. Creditors may file a bankruptcy petition against a debtor in an effort to recoup a portion of what they are owed. Bankruptcy prediction is very important because it serves two main purposes under the bankruptcy law. First, bankruptcy law gives creditors some payment on their debts if a debtor (the one who owes the debt) can afford to pay them. Second, bankruptcy law gives debtors a fresh start, by cancelling many of their debts, through an order of the court called a discharge. If an organization is not willing to get into such an adverse circumstances, then one can have sound bankruptcy prediction techniques in place.

Bankruptcy prediction has become increasingly important over the last few decades. The number of corporate bankruptcies has been growing ever since the economical depression of 1930. Bankruptcy prediction is a classification problem, with two classes: bankrupt or non-bankrupt (healthy). Over the past few decades financial crisis was observed in some emerging sectors like banking. Bankruptcy prediction of banks has been an extensively researched area since late 1960s. Bankruptcy can affect all the areas where the sufferers are creditors, auditors, stock holders and senior management. So, they are all interested in predicting the bankruptcy. Researchers used CAMELS rating for prediction in the early days. But, because of its in effectiveness they moved towards other theoretical models.
Recently researchers reported the effective use of soft computing techniques toward bankruptcy prediction in banks. Refer to the following resources for further studies.

Books: Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction by Stewart Jones, David A. Hensher
Web resources: Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results
Hybrid and ensemble-based soft computing techniques in bankruptcy
Download Power Point Presentation (PPT): Bankruptcy, Reorganization, and Liquidation