Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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Experimental And Quasi Experimental Designs For Research

International Journal of Computer and Applications

http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Subhedar%20-%20Image%20Steganography%20-%202014.pdf

Current status and key issues in image steganography

Distributed denial of service (DDoS) resilience in cloud

A learning model to detect maliciousness of portable executable using integrated feature set

Cloud computing is an internet based pay as use service which provides three layered services (Software as a Service, Platform as a Service and Infrastructure as a Service) to its consumers on demand. These on demand service facilities provide to its consumers in multitenant environment but as facility increases complexity and security problems also increase. Here all the resources are at one place in data centers. Cloud uses public and private APIs (Application Programming Interface) to provide services to its consumers in multitenant environment. In this environment Distributed Denial of Service attack (DDoS), especially HTTP, XML or REST based DDoS attacks may be very dangerous and may provide very harmful effects for availability of services and all consumers will get affected at the same time. One other reason is that because the cloud computing users make their request in XML then send this request using HTTP protocol and build their system interface with REST protocol such as Amazon EC2 or Microsoft Azure. So the threaten coming from distributed REST attacks are more and easy to implement by the attacker, but to security expert very difficult to resolve. So to resolve these attacks this paper introduces a comber approach for security services called filtering tree. This filtering tree has five filters to detect and resolve XML and HTTP DDoS attack.

A comber approach to protect cloud computing against XML DDoS and HTTP DDoS attack

The requirement of a Web crawler that downloads most relevant pages is still a major challenge in the field of information retrieval systems. The use of link analysis algorithms like page rank and other importance-metrics have shed a new approach in prioritizing the URL queue for downloading higher relevant pages. The combination of these metrics along with a new metric called association-metric has been proposed. The association-metric estimates the semantic content of the URL based on the domain dependent ontology, which in turn strengthens the metric that is used for prioritizing the URL queue. In addition, after downloading the page, the association metric plays important role in estimating the relevancy of the links in that page. The proposed new metric solves the major problem of finding the relevancy of the pages before the process of crawling, to an optimal level.

Ontology-based Web crawler

This paper discusses the various approaches used for identifying semantically similar concepts in an ontology. The purpose of this survey is to explore how these similarity computation methods could assist in ontology based query expansion. This query expansion method based on the similarity function is expected to improve the retrieval effectiveness of the ontology based Information retrieval models. Various similarity computation methods fall under three categories: Edge counting, information content and node based counting. The limitations of each of these approaches have been discussed in this paper.

A Survey of Semantic Similarity Methods for Ontology Based Information Retrieval

A combinatorial approach for protecting Web applications against SQL injection is discussed in this paper, which is a novel idea of incorporating the uniqueness of Signature based method and auditing method The major issue of web application security is the SQL Injection, which can give the attackers unrestricted access to the database that underlie Web applications and has become increasingly frequent and serious From signature based method standpoint of view, it present a detection mode for SQL injection using pair wise sequence alignment of amino acid code formulated from wab application form parameter sent via web server On the other hand from the Auditing based method standpoint of view, it analyzes the transaction to find out the malicious access In signature based method It uses an approach called Hirschberg algorithm, it is a divide and conquer approach to reduce the time and space complexity This system was able to stop all of the successful attacks and did not generate any false positives

Gnanasekaran Aghila

Papers

A learning model to detect maliciousness of portable executable using integrated feature set

A comber approach to protect cloud computing against XML DDoS and HTTP DDoS attack

Ontology-based Web crawler

A Survey of Semantic Similarity Methods for Ontology Based Information Retrieval

Combinatorial Approach for Preventing SQL Injection Attacks