Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Coherence in discourse One of the pervasive illusions which persists in the analysis of language is that we understand the meaning of a linguistic message solely on the basis of the words and structure of the sentence(s) used to convey that message. We certainly rely on the syntactic structure and lexical items used in a linguistic message to arrive at an interpretation, but it is a mistake to think that we operate only with this literal input to our understanding. We can recognise, for example, when a writer has produced a perfectly grammatical sentence from which we can derive a literal interpretation, but which we would not claim to have understood, simply because we need more information. Extract (1), the first sentence of a novel, may provide an illustration of this point. (1) Within five minutes, or ten minutes, no more than that, three of the others had called her on the telephone to ask her if she had heard that something had happened out there. (Tom Wolfe, The Right Stuff , Bantam Books, 1981) The novelist is, of course, leading his reader to read on and find out just what the first sentence, though literally complete, has only partially described. At the opposite extreme, we can point to linguistic messages which are not presented in sentences and consequently can't be discussed in terms of syntactic well-formedness, but which are readily interpreted.

Discourse Analysis: Coherence in the interpretation of discourse

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

/pdf/recent-progress-of-using-knowledge-graph-for-cybersecurity-gv3ag2m9.pdf

Recent Progress of Using Knowledge Graph for Cybersecurity

A language independent stemmer has always been looked for. Single N-gram tokenization technique works well, however, it often generates stems that start with intermediate characters, rather than initial ones. We present a novel technique that takes the concept of N gram stemming one step ahead and compare our method with an established algorithm in the field, Porter's Stemmer. Results indicate that our N gram stemmer is not inferior to Porter's linguistic stemmer.

Generation, implementation, and appraisal of an N-gram-based stemming algorithm

Using Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network

Suffix stripping is a problem of removing morphological suffixes from a word to get the stem. We present suffix stripping as an unconstrained optimization problem. Free from linguistic or morphological knowledge, a simple algorithm is being developed. Superiority of the algorithm over an established technique for English language is being demonstrated.



Suffix stripping ist der Prozess des systematischen Entfernens von Suffixen um zum Stamm zu gelangen. Wir prasentieren Suffix Stripping als ein Optimierungs problem ohne Nebenbedingungen. Ein einfacher Algorithmus jenseits linguistischen oder morphologischen Wissens wird entwickelt. Damit wird der Vorrang des Algorithmus vor einer Technik der englischen Sprache demonstriert.

A simple algorithm for the problem of suffix stripping

Corpus based stemming has been devised to develop stemmers targeting language independent environment. These stemmers are applicable to all languages based on Latin script. In the present work, we exploit the English corpus for coded words of Devanagari script. We use the technique of Romanization and the stemmer is being tested over 100 randomly chosen Hindi words. We show that this approach has a direct application to the standardization of regional languages. For instance, we standardize the Kumauni language.

http://www.dline.info/jcl/fulltext/v5n4/2.pdf

A Devanagari Script based Stemmer

Machine learning (ML) is the latest buzzword growing rapidly across the world, and ML possesses massive potential in numerous domains. ML technology is a subset of Artificial Intelligence (AI) and empowers digital machines with the ability to learn without being explicitly programmed, i.e., the capability to learn from past experiences. Since the last decade, ML technology has been used in various domains because it possesses numerous interesting characteristics such as adaptability, robustness, learnability, and its ability to take instant actions against unexpected challenges. The traditional cybersecurity systems are built on rules, attack signatures, and fixed algorithms. Thus, the systems can act only upon the ‘knowledge' fed to them and human intervention is continually required for the proper functioning of traditional cybersecurity systems. On the other hand, ML technology can recognize various patterns from past experiences and is capable of predicting or detecting future attacks based on seen or unseen data. The ML technology is capable of handling massive real-time network data which allows various issues present in conventional cybersecurity systems to be overcome. In the present chapter, various issues related to the applications of ML in cybersecurity have been discussed. The effectiveness of applying ML technology in cybersecurity affairs has been thoroughly investigated. The contemporary challenges being faced by researchers in the realm have been identified and discussed. The current chapter presents available datasets and algorithms for the successful implementation of ML technology in the domain of cybersecurity. The datasets are also compared across various parameters. Finally, applications of ML practices by three renowned businesses, Facebook, Microsoft, and Google are explored. 

Applications of Machine Learning Techniques in the Realm of Cybersecurity

In the present work an attempt is being made to reduce the Maximum Flow Network Interdiction Problem (MFNIP) in to the Subset Sum Problem so as to get some algorithms solvable in polynomial time. Previously developed algorithms are either applicable to some special cases of MFNIP or they do not have a constant performance guarantee. Our reduction has paved the way towards the development of fully polynomial time approximation schemes for Maximum Flow Network Interdiction Problem.

/pdf/reduction-of-maximum-flow-network-interdiction-problem-step-39cvfv81ac.pdf

Reduction of Maximum Flow Network Interdiction Problem: Step towards the Polynomial Time Solutions

The Bayesian model for prediction problems requires setting up the prior/hyper prior structures that go through the process of integration. However, these formulated integrals are not tractable analytically. Moreover, application of Markov Chain Monte Carlo (MCMC) methods to solve these integralsare slow in nature, especially if the parameter space is high dimensional. The key idea behind the Bayesian inference is to marginalize over unknown parameters, rather than make point estimation. This technique avoids severe over-fitting problems and allows direct model comparison. In this paper, we presented a model using variational Bayesian inference for identification and interpretation of Non Standard Words in Bengali news corpus. The variational methods extend the practicality of Bayesian inference to complex Bayesian models and "medium sized" data sets. The Variational Bayesian inference aims to approximate posterior distribution by a simpler distribution for which marginalization is tractable.

http://www.dline.info/jcl/fulltext/v5n4/1.pdf

Bhagwati Prasad Pande

Papers

A simple algorithm for the problem of suffix stripping

A Devanagari Script based Stemmer

Applications of Machine Learning Techniques in the Realm of Cybersecurity

Reduction of Maximum Flow Network Interdiction Problem: Step towards the Polynomial Time Solutions

Identification and Interpretation of NSWs Using Variational Bayesian Inference in Bengali News Corpus