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

Data Mining Practical Machine Learning Tools and Techniques

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

The Grid 2: Blueprint for a New Computing Infrastructure

Summary With the tremendous growth of network-based services and sensitive information on networks, network security is getting more and more importance than ever. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types posses a serious problem for their detection. The human labelling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, we apply one of the efficient data mining algorithms called naive bayes for anomaly based network intrusion detection. Experimental results on the KDD cup’99 data set show the novelty of our approach in detecting network intrusion. It is observed that the proposed technique performs better in terms of false positive rate, cost, and computational time when applied to KDD’99 data sets compared to a back propagation neural network based approach.

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Network intrusion detection using naïve bayes

Cloud computing is an architecture for providing computing service via the internet on demand and pay per use access to a pool of shared resources namely networks, storage, servers, services and applications, without physically acquiring them. So it saves managing cost and time for organizations. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied etc. Cloud computing is a completely internet dependent technology where client data is stored and maintain in the data center of a cloud provider like Google, Amazon, Salesforce.som and Microsoft etc. Limited control over the data may incur various security issues and threats which include data leakage, insecure interface, sharing of resources, data availability and inside attacks. There are various research challenges also there for adopting cloud computing such as well managed service level agreement (SLA), privacy, interoperability and reliability. This research paper outlines what cloud computing is, the various cloud models and the main security risks and issues that are currently present within the cloud computing industry. This research paper also analyzes the key research and challenges that presents in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate.

Cloud Computing: Security Issues and Research Challenges

A Hybrid Intelligent Approach for Network Intrusion Detection

This paper applies discriminative multinomial Naive Bayes with various filtering analysis in order to build a network intrusion detection system. For our experimental analysis, we used the new NSL-KDD dataset, which is considered as a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. We perform 2 class classifications with 10-fold cross validation for building our proposed model. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system.

Discriminative multinomial Naïve Bayes for network intrusion detection

The web services, a novel paradigm in software technology, have innovative mechanism for rendering services over diversified environment. They promise to allow businesses to adapt rapidly to changes in the business environment and the needs of different customers. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Consequently, assessment of the quality of web services is of paramount importance in selecting a web service for an application. In this paper, we employed well-known classification models viz., back propagation neural network (BPNN), probabilistic neural network (PNN), group method of data handling (GMDH), classification and regression trees (CART), TreeNet, support vector machine (SVM) and ID3 decision tree (J48) to predict the quality of a web service based on a set of quality attributes. The experiments are carried out on the QWS dataset. We applied 10-fold cross-validation to test the efficacy of the models. The J48 and TreeNet techniques outperformed all other techniques by yielding an average accuracy of 99.72%. We also performed feature selection and found that web-services relevance function (WSRF) is the most significant attribute in determining the quality of a web service. Later, we performed feature selection without WSRF and found that Reliability, Throughput, Successability, Documentation and ResponseTime are the most important attributes in that order. Moreover, the set of 'if-then' rules yielded by J48 and CART can be used as an expert system for web-services classification.

Manas Ranjan Patra

Papers

Network intrusion detection using naïve bayes

Cloud Computing: Security Issues and Research Challenges

A Hybrid Intelligent Approach for Network Intrusion Detection

Discriminative multinomial Naïve Bayes for network intrusion detection

Web-services classification using intelligent techniques