Data Mining - Concepts and Techniques.

Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.

Copyright protection for the electronic distribution of text documents : Identification and protection of multimedia information

Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, disabilities, and sexual or political orientation. In this manuscript, we discuss some of the limitations present in the current reasoning about fairness and in methods that deal with it, and describe some work done by the authors to address them. More specifically, we show how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios. We describe how optimal transport theory can be leveraged to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes, overcoming the limitation of most approaches that approximate fairness desiderata by imposing constraints on the lower order moments or other functions of those distributions. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria, and that enjoys strong theoretical guarantees. We introduce an approach to learn fair representations that can generalize to unseen tasks. Finally, we describe a technique that accounts for legal restrictions about the use of sensitive attributes.

Fairness in Machine Learning

Proceedings of world academy of science, engineering and technology, vol 8

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

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A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset.  The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.

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Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA

Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process This paper introduced Spark-Chi-SVM model for intrusion detection In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform We used KDD99 to train and test the model In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data

/pdf/intrusion-detection-model-using-machine-learning-algorithm-1tp6hqf2jb.pdf

Intrusion detection model using machine learning algorithm on Big Data environment

In the study of content authentication and tamper detection of digital text documents, there are very limited techniques available for content authentication of text documents using digital watermarking techniques. A novel intelligent text zero watermarking approach based on probabilistic patterns has been proposed in this paper for content authentication and tamper detection of English text documents. In the proposed approach, Markov model of order THREE and letter-based was constructed and abbreviated as LNMZW3 for text analysis and utilizes the interrelationship between contents of given text to generate the watermark. However, we can extract this watermark later using extraction and detection algorithms to identify the status of text document such as authentic, or tampered. The proposed approach was implemented using PHP Programming language with Net Beans IDE 7.0. Furthermore, the effectiveness and feasibility of our LNMZW3 approach has proved and compared with other recent approaches with experiments using five datasets of varying lengths and different volumes of attacks. Results show that the proposed approach is always detects tampering attacks occurred randomly on text even when the tampering volume is low, mid or high. Comparative results with the recent approaches shows that the our LNMZW3 approach provides added value under random insertion and deletion attacks in terms of performance, watermark robustness and watermark security. However, it is provide worst enhancement under reorder attacks. General Terms Digital Watermarking, Watermark Robustness, Information Hiding, Probabilistic Patterns.

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Fadl Mutaher Ba-Alwi

Papers

Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA

Intrusion detection model using machine learning algorithm on Big Data environment

Content Authentication of English Text via Internet using Zero Watermarking Technique and Markov Model

Automated Discovery of Hierarchical Ripple-Down Rules (HRDRs).

Discovery of Production Rules with Fuzzy Hierarchy.