Data Mining Practical Machine Learning Tools and Techniques

Statistics for Spatial Data.

Preface 1. Spatial concepts and notions 2. Ecological and spatial processes 3. Points, lines and graphs 4. Spatial analysis of complete point location data 5. Contiguous units analysis 6. Spatial analysis of sample data 7. Spatial relationship and multiscale analysis 8. Spatial autocorrelation and inferential tests 9. Spatial partitioning: spatial clusters and boundary detection 10. Spatial diversity analysis 11. Spatio-temporal analysis 12. Closing comments and future directions References Index.

Spatial Analysis A Guide for Ecologists

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

The importance of niche vs. neutral assembly mechanisms in structuring tropical tree communities remains an important unsettled question in community ecology [Bell G (2005) Ecology 86:1757-1770]. There is ample evidence that species distributions are determined by soils and habitat factors at landscape ( 0.5 million individual trees of 1,400 species and 10 essential plant nutrients, we used Monte Carlo simulations of species distributions to test plant-soil associations against null expectations based on dispersal assembly. We found that the spatial distributions of 36-51% of tree species at these sites show strong associations to soil nutrient distributions. Neutral dispersal assembly cannot account for these plant-soil associations or the observed niche breadths of these species. These results indicate that belowground resource availability plays an important role in the assembly of tropical tree communities at local scales and provide the basis for future investigations on the mechanisms of resource competition among tropical tree species.

/pdf/soil-nutrients-influence-spatial-distributions-of-tropical-bplfe4l8rd.pdf

Soil nutrients influence spatial distributions of tropical tree species

http://mathbio.sas.upenn.edu/Papers/Plotkin_aggregation_JTB2000.pdf

Species-area curves, spatial aggregation, and habitat specialization in tropical forests.

Intrusion Detection Systems (IDS) are increasingly adopting machine learning (ML)-based approaches to detect threats in computer networks due to their ability to learn underlying threat patterns/features. However, ML-based models are susceptible to adversarial attacks, attacks wherein slight perturbations of the input features, cause misclassifications. We propose a method that uses active learning and generative adversarial networks to evaluate the threat of adversarial attacks on ML-based IDS. Existing adversarial attack methods require a large amount of training data or assume knowledge of the IDS model itself (e.g., loss function), which may not be possible in real-world settings. Our method overcomes these limitations by demonstrating the ability to compromise an IDS using limited training data and assuming no prior knowledge of the IDS model other than its binary classification (i.e., benign or malicious). Experimental results demonstrate the ability of our proposed model to achieve a 98.86% success rate in bypassing the IDS model using only 25 labeled data points during model training. The knowledge gained by compromising the ML-based IDS, can be integrated into the IDS in order to enhance its robustness against similar ML-based adversarial attacks.

https://dl.acm.org/doi/pdf/10.1145/3395352.3402618

Generative adversarial attacks against intrusion detection systems using active learning

In this paper, we propose a scalable algorithm to allocate honeypots over an attack graph. We formulate a two-person zero-sum strategic game between the network defender and an attacker. This formulation captures the importance of different nodes inside the network. The game mode accounts for the cost associated with different defense strategies as well as the cost paid by the attacker. Moreover, this game model considers a practical threat model with respect to the available information about the attacker to the network defender. Nash equilibrium defense strategies are analytically characterized and studied for a special game. The complexity of a general game is discussed and a scalable algorithm is proposed to obtain honeypots allocation strategy in large scale networks. Finally, samples of our numerical results are shown to verify our findings.

Honeypot Allocation over Attack Graphs in Cyber Deception Games

We propose several generalized linear models (GLMs) to predict the number of successful cyber intrusions (or “intrusions”) into an organization’s computer network, where the rate at which intrusion...

Statistical models for the number of successful cyber intrusions

Increasingly cyber-attacks are sophisticated and occur rapidly, necessitating the use of machine learning techniques for detection at machine speed. However, the use of machine learning techniques in cyber security requires the extraction of features from the raw network traffic. Thus, subject matter expertise is essential to analyze the network traffic and extract optimum features to detect a cyber-attack.
Consequently, we propose a novel machine learning algorithm for malicious network traffic detection using only the bytes of the raw network traffic. The feature vector in our machine learning method is a structure containing the headers and a variable number of payload bytes. We propose a 1D-Convolutional Neural Network (1D-CNN) and Feed Forward Network for detection of malicious packets using raw network bytes.

Nandi O. Leslie

Papers

Species-area curves, spatial aggregation, and habitat specialization in tropical forests.

Generative adversarial attacks against intrusion detection systems using active learning

Honeypot Allocation over Attack Graphs in Cyber Deception Games

Statistical models for the number of successful cyber intrusions

Machine learning raw network traffic detection