Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions

This study reviews and analyses the research landscape for intrusion detection systems (IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the gap in this pivotal research area. The focus is on articles related to the keywords ‘deep learning’, ‘intrusion’ and ‘attack’ and their variations in four major databases, namely Web of Science, ScienceDirect, Scopus and the Institute of Electrical and Electronics Engineers’ Xplore. These databases are sufficiently broad to cover the technical literature. The dataset comprises 68 articles. The largest proportion (72.06%; 49/68) relates to articles that develop an approach for evaluating or identifying intrusion detection techniques using the DL approach. The second largest proportion (22.06%; 15/68) relates to studying/applying articles to the DL area, IDSs or other related issues. The third largest proportion (5.88%; 4/68) discusses frameworks/models for running or adopting IDSs. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology’s utility, authors’ recommendations and substantial analysis. Then, a result analysis mapping for new directions is discussed. Three phases are designed to meet the demands of detecting distributed denial-of-service attacks with a high accuracy rate. This study provides an extensive resource background for researchers who are interested in IDSs based on DL

A Systematic Review of Blockchain Research and Applications in Business

Blockchain, well-known as the Distributed Ledger Technology (DLT) behind cryptocurrencies such as Bitcoin, has attracted plenty of attention from both practitioners and academics and has begun to revolutionize the way businesses handle their day-to-day operations. DLT leads to many interesting new research topics in different business fields. In this study, we applied the systematic mapping method to review existing articles related to blockchain applications and research in different business areas (accounting, finance and banking, information systems, marketing and supply chain). Our goal is to understand the current applications and research status related to blockchain, so that we can identify research gaps and better directions for future research. Following the recommended steps of the systematic mapping method, we extracted fifty primary papers from several scientific databases. Our findings suggest that despite the attention received, blockchain research still has a long way to go. Several future research directions are discussed

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Initial Observations on Query Based Sampling in Distributed CLIR

Cross Language Information Retrieval (CLIR) enables people to search information written in different languages from their query languages. Information can be retrieved either from a single cross lingual collection or from a variety of dis-tributed cross lingual sources. This paper pre-sents initial results exploring the effectiveness of distributed CLIR using query-based sampling techniques, which to the best of our knowledge has not been investigated before. In distributed retrieval with multiple databases, query-based sampling provides a simple and effective way for acquiring accurate resource descriptions which helps to select which databases to search. Obser-vations from our initial experiments show that the negative impact of query-based sampling on cross language search may not be as great as it is on monolingual retrieval