Data Mining Practical Machine Learning Tools and Techniques

Social Network Analysis

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Series Editor's Introduction Preface Acknowledgments 1. The Idea of Agent-Based Modeling 1.1 Agent-Based Modeling 1.2 Some Examples 1.3 The Features of Agent-Based Modeling 1.4 Other Related Modeling Approaches 2. Agents, Environments, and Timescales 2.1 Agents 2.2 Environments 2.3 Randomness 2.4 Time 3. Using Agent-Based Models in Social Science Research 3.1 An Example of Developing an Agent-Based Model 3.2 Verification: Getting Rid of the Bugs 3.3 Validation 3.4 Techniques for Validation 3.5 Summary 4. Designing and Developing Agent-Based Models 4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments 4.2 Using NetLogo to Build Models 4.3 Building the Collectivities Model Step by Step 4.4 Planning an Agent-Based Model Project 4.5 Reporting Agent-Based Model Research 4.6 Summary 5. Advances in Agent-Based Modeling 5.1 Geographical Information Systems 5.2 Learning 5.3 Simulating Language Resources Glossary References Index About the Author

What are agent-based models?

Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

/pdf/a-survey-on-the-internet-of-things-iot-forensics-challenges-3w6jzejgc2.pdf

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

The identification of fault proneness at an early stage of software development is an imperative aspect to consider in order to reduce efforts in maintaining the quality of software products.

Impact of Interaction in the Combined Metrics Approach for Fault Prediction

Language processing of Sanskrit presents various challenges in the field of computational linguistics. Prosodical, orthographic and inflectional complexities encountered in Sanskrit texts makes it difficult to apply linguistic analysis methods relevant for western European languages. The inadequacy of contemporary computational approaches in the analysis of Sanskrit language is vivdly apparent. In this exposition, we focus on the challenge of learning syntactic and semantic similarities in a rich Sanskrit literature. We present a simple yet effective approach of representing Sanskrit words in a continuous vector space. We utilise word embeddings in similarity, compositionality and visualization tasks to test its efficacy. Experiments show that our method produces interpretable vector offsets exhibiting shared relationships.

Representing Contexual Relations with Sanskrit Word Embeddings

The incredible growth in the cloud applications and services reflects a positive swing in the thought processes of the business decision makers for cloud adoption. However, ever-evolving security and privacy issues continue to influence the decision makers to delay the cloud adoption. In this integrationist exposition, the previous publications are enriched and enhanced to holistically analyze different threats to cloud computing to conceptualize a three-dimensional model of cloud security assurance. These three dimensions, namely Security Solution, Security Operation, and Security Compliance, are interwoven to address the top threats to cloud computing, which are identified and reported by the cloud security alliance (CSA) research group in their latest and previous reports. The model will help practitioners to design and implement a security assurance system for a cloud ecosystem to strengthen trust in the cloud and accelerate its adoption to bring agility and velocity in cloud applications and services delivery in a cost-effective way.

Top Threats to Cloud: A Three-Dimensional Model of Cloud Security Assurance

The use of online social networks has become a standard medium of social interactions and information spreading. Due to the significant amount of data available online, social network analysis has become apropos to the researchers of diverse domains to study and analyse innovative patterns, friendships, and relationships. Message dissemination through these networks is a complex and dynamic process. Moreover, the presence of reciprocal links intensify the whole process of propagation and expand the chances of reaching to the target node. We therefore empirically investigated the relative importance of reciprocal relationships in the directed social networks affecting information spreading. Since the dynamics of the information diffusion has considerable qualitative similarities with the spread of infections, we analysed six different variants of the Susceptible–Infected (SI) epidemic spreading model to evaluate the effect of reciprocity. By analysing three different directed networks on different network metrics using these variants, we establish the dominance of reciprocal links as compared to the non-reciprocal links. This study also contributes towards a closer examination of the subtleties responsible for maintaining the network connectivity.

https://www.mdpi.com/2078-2489/9/6/136/pdf

Impact of Reciprocity in Information Spreading Using Epidemic Model Variants

Interaction between nodes in a complex network showing the property of homophily tends to produce community structure in the network. The detection of these communities is of immense financial and informational value. For this purpose, we propose a semi-supervised community detection algorithm, inspired by genetic genealogy and based on Label Propagation Algorithm, that detects communities in the network by taking into account the propagation of influence from different community centers identified in the network. Analysis of our proposed algorithm showed improved performance in detecting communities in real social networks.

Rinkaj Goyal

Papers

Impact of Interaction in the Combined Metrics Approach for Fault Prediction

Representing Contexual Relations with Sanskrit Word Embeddings

Top Threats to Cloud: A Three-Dimensional Model of Cloud Security Assurance

Impact of Reciprocity in Information Spreading Using Epidemic Model Variants

Influence propagation based community detection in complex networks