The Era of Advanced Machine Learning and Deep Learning Algorithms for Malware Detection

2022 ◽  
pp. 59-73
Author(s):  
Kwok Tai Chui ◽  
Patricia Ordóñez de Pablos ◽  
Miltiadis D. Lytras ◽  
Ryan Wen Liu ◽  
Chien-wen Shen
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Essential Element ◽  
Future Research ◽  
Detection Algorithms ◽  
Computing Services ◽  
Digital Era ◽  
Future Research Directions

Software has been the essential element to computers in today's digital era. Unfortunately, it has experienced challenges from various types of malware, which are designed for sabotage, criminal money-making, and information theft. To protect the gadgets from malware, numerous malware detection algorithms have been proposed. In the olden days there were shallow learning algorithms, and in recent years there are deep learning algorithms. With the availability of big data for training of model and affordable and high-performance computing services, deep learning has demonstrated its superiority in many smart city applications, in terms of accuracy, error rate, etc. This chapter intends to conduct a systematic review on the latest development of deep learning algorithms for malware detection. Some future research directions are suggested for further exploration.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

scholarly journals Graphene-Based Thermal Interface Materials: An Application-Oriented Perspective on Architecture Design

Polymers ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1201 ◽  
Author(s):  
Le Lv ◽  
Wen Dai ◽  
Aijun Li ◽  
Cheng-Te Lin
Keyword(s):  
Thermal Conductivity ◽  
High Performance ◽  
Electronic Devices ◽  
Point Of View ◽  
Future Research ◽  
Thermal Interface Materials ◽  
Research Directions ◽  
Thermal Interface ◽  
Future Research Directions ◽  
Interface Materials

With the increasing power density of electrical and electronic devices, there has been an urgent demand for the development of thermal interface materials (TIMs) with high through-plane thermal conductivity for handling the issue of thermal management. Graphene exhibited significant potential for the development of TIMs, due to its ultra-high intrinsic thermal conductivity. In this perspective, we introduce three state-of-the-art graphene-based TIMs, including dispersed graphene/polymers, graphene framework/polymers and inorganic graphene-based monoliths. The advantages and limitations of them were discussed from an application point of view. In addition, possible strategies and future research directions in the development of high-performance graphene-based TIMs are also discussed.

Managing Human Resources in Family Businesses

2014 ◽  
pp. 96-112 ◽  
Author(s):  
Santiago Gutiérrez-Broncano ◽  
Mercedes Rubio-Andrés ◽  
Pedro Jiménez Estévez
Keyword(s):  
Human Capital ◽  
Human Resources ◽  
High Performance ◽  
Negative Impact ◽  
Business Owners ◽  
Family Businesses ◽  
Future Research ◽  
Research Directions ◽  
Positive Effects ◽  
Future Research Directions

Although a lot of research has been carried out in the field of family businesses in recent years, not much of it has focused on human resource management. After compiling the major studies, both negative aspects (e.g. nepotism) and positive ones (e.g. employee commitment) have been identified. Therefore, the authors propose high-performance human resources practices to reduce the negative impact of family in business and boost the positive effects, increase their human capital, and achieve a competitive advantage in this field. Finally, the authors provide key insights for practitioners, family business owners, and managers, and they propose future research directions.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

Music Onset Detection

2010 ◽  
pp. 297-316
Author(s):  
Ruohua Zhou ◽  
Josh D Reiss
Keyword(s):  
Performance Measures ◽  
Essential Role ◽  
General Scheme ◽  
Future Research ◽  
Onset Detection ◽  
Time Frequency ◽  
Detection Algorithms ◽  
Music Signal ◽  
Wide Range ◽  
Future Research Directions

Music onset detection plays an essential role in music signal processing and has a wide range of applications. This chapter provides a step by step introduction to the design of music onset detection algorithms. The general scheme and commonly-used time-frequency analysis for onset detection are introduced. Many methods are reviewed, and some typical energy-based, phase-based, pitch-based and supervised learning methods are described in detail. The commonly used performance measures, onset annotation software, public database and evaluation methods are introduced. The performance difference between energy-based and pitch-based method is discussed. The future research directions for music onset detection are also described.

A review on the mechanical properties of textile structural composite T-beams

2019 ◽  
Vol 90 (5-6) ◽  
pp. 710-727 ◽  
Author(s):  
Yiwei Ouyang ◽  
Xianyan Wu
Keyword(s):  
Mechanical Properties ◽  
High Performance ◽  
Raw Materials ◽  
Research Progress ◽  
Future Research ◽  
Research Directions ◽  
Structural Composite ◽  
Delamination Resistance ◽  
Application Potential ◽  
Future Research Directions

In order to review the most effective ways to improve the mechanical properties of composite T-beams and further increase their application potential, research progress on the mechanical properties of textile structural composite T-beams was summarized based on two-dimensional (2-D) ply structure composite T-beams, delamination resistance enhanced 2-D ply structure T-beams, and three-dimensional (3-D) textile structural composite T-beams; future research directions for composite T-beams were also considered. From existing literature, the research status and application bottlenecks of 2-D ply structure composite T-beams and T-beams with enhanced delamination resistance performance were described, as were the specific classification, research progress, and mechanical properties of 3-D textile structural composite T-beams. In addition, the superior mechanical properties of 3-D braided textile structural composite T-beams, specifically their application potential based on excellent delamination resistance capacity, were highlighted. Future research directions for composite T-beams, that is, the applications of high-performance raw materials, locally enhanced design, structural blending enhancement, functionality, and intelligence are presented in this review.

scholarly journals Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

2020 ◽  
Author(s):  
Md Nazmus Saadat ◽  
Muhammad Shuaib
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Language Processing ◽  
Future Research ◽  
Learning In Context ◽  
Postgraduate Students ◽  
History Of ◽  
Learning Platforms ◽  
Future Research Directions ◽  
Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

scholarly journals Virtual bargaining: a theory of social decision-making

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130487 ◽  
Author(s):  
Jennifer B. Misyak ◽  
Nick Chater
Keyword(s):  
Decision Making ◽  
Essential Element ◽  
Social Contexts ◽  
Future Research ◽  
Research Directions ◽  
Social Decision ◽  
Future Research Directions ◽  
Empirical Tests ◽  
Social Decision Making ◽  
Vast Range

An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium concept in game theory, are sometimes too broad and at other times ‘overlook’ good solutions to fundamental social dilemmas and coordination problems. The authors propose a new theory of social decision-making—virtual bargaining—in which individuals decide among a set of moves on the basis of what they would agree to do if they could openly bargain. The core principles of a formal account are outlined (vis-à-vis the notions of ‘feasible agreement’ and explicit negotiation) and further illustrated with the introduction of a new game, dubbed the ‘Boobytrap game’ (a modification on the canonical Prisoner's Dilemma paradigm). In the first empirical data of how individuals play the Boobytrap game, participants' experimental choices accord well with a virtual bargaining perspective, but do not match predictions from a standard Nash account. Alternative frameworks are discussed, with specific empirical tests between these and virtual bargaining identified as future research directions. Lastly, it is proposed that virtual bargaining underpins a vast range of human activities, from social decision-making to joint action and communication.

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