scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Cyber Security in IoT-Based Cloud Computing: A Comprehensive Survey

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 16
Author(s):  
Waqas Ahmad ◽  
Aamir Rasool ◽  
Abdul Rehman Javed ◽  
Thar Baker ◽  
Zunera Jalil
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Deep Learning ◽  
Cyber Security ◽  
Industrial Applications ◽  
Future Research ◽  
Cloud Infrastructure ◽  
Security Issues ◽  
Comprehensive Survey ◽  
Security Concerns

Cloud computing provides the flexible architecture where data and resources are dispersed at various locations and are accessible from various industrial environments. Cloud computing has changed the using, storing, and sharing of resources such as data, services, and applications for industrial applications. During the last decade, industries have rapidly switched to cloud computing for having more comprehensive access, reduced cost, and increased performance. In addition, significant improvement has been observed in the internet of things (IoT) with the integration of cloud computing. However, this rapid transition into the cloud raised various security issues and concerns. Traditional security solutions are not directly applicable and sometimes ineffective for cloud-based systems. Cloud platforms’ challenges and security concerns have been addressed during the last three years, despite the successive use and proliferation of multifaceted cyber weapons. The rapid evolution of deep learning (DL) in the artificial intelligence (AI) domain has brought many benefits that can be utilized to address industrial security issues in the cloud. The findings of the proposed research include the following: we present a comprehensive survey of enabling cloud-based IoT architecture, services, configurations, and security models; the classification of cloud security concerns in IoT into four major categories (data, network and service, applications, and people-related security issues), which are discussed in detail; we identify and inspect the latest advancements in cloud-based IoT attacks; we identify, discuss, and analyze significant security issues in each category and present the limitations from a general, artificial intelligence and deep learning perspective; we provide the technological challenges identified in the literature and then identify significant research gaps in the IoT-based cloud infrastructure to highlight future research directions to blend cybersecurity in cloud.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Recognition and Work in the Platform Economy: a Normative Reconstruction

2021 ◽  
Author(s):  
Max Visser ◽  
Thomas C. Arnold
Keyword(s):  
Working Conditions ◽  
Critical Discussion ◽  
Future Research ◽  
The Past ◽  
Recognition Theory ◽  
Normative Reconstruction ◽  
Human Side ◽  
Platform Economy

AbstractThe rise of the platform economy in the past two decades (and neoliberal capitalist expansion and crises more in general), have on the whole negatively affected working conditions, leading to growing concerns about the “human side” of organizations. To address these concerns, the purpose of this paper is to apply Axel Honneth’s recognition theory and method of normative reconstruction to working conditions in the platform economy. The paper concludes that the ways in which platform organizations function constitutes a normative paradox, promising flexibility and autonomy while at the same time creating working conditions that undercut these promises. The paper ends with a critical discussion of Honneth’s approach, possible supplementing ideas and further lines of future research.

Next Generation Information-Based Infrastructures

2013 ◽  
pp. 304-317
Author(s):  
Eric Luiijf
Keyword(s):  
Information And Communication Technologies ◽  
Cyber Security ◽  
Smart Cities ◽  
Communication Technologies ◽  
Road Transport ◽  
Transport Infrastructure ◽  
Next Generation ◽  
Tightly Coupled ◽  
Information And Communication ◽  
Ict Security

Advancements of information and communication technologies (ICT) cause infrastructure owners to augment current infrastructures with such ICT. The creation of more efficient and effective end-user services provides economical benefits and increases customer satisfaction. Concurrently, ICT advancements allow governmental and industrial sectors to develop complete new infrastructures and infrastructure services, the so called Next Generation Infrastructures (NGI). NGI will offer new services to society, end-users and the supply-chain of organisations and linked, dependent infrastructural services. For over fifty years, the introduction of new ICT-based services and infrastructures has been tightly coupled with failures in ICT-security. This chapter on NGI discusses the root causes of these security failures. Based on historical experiences, this chapter predicts threats and cyber security failures alike for the envisioned NGI such as smart (energy) grids, smart road transport infrastructure, smart cities, and e-health. This prediction will become reality unless fundamental changes in the approach to security of ICT-based and ICT-controlled infrastructures are taken.

scholarly journals Deep Learning for Generic Object Detection: A Survey

2019 ◽  
Vol 128 (2) ◽  
pp. 261-318 ◽  
Author(s):  
Li Liu ◽  
Wanli Ouyang ◽  
Xiaogang Wang ◽  
Paul Fieguth ◽  
Jie Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Evolution ◽  
Feature Representation ◽  
Future Research ◽  
Context Modeling ◽  
Feature Representations ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Object Feature

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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 A Survey of Deep Learning Methods for Cyber Security

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 122 ◽  
Author(s):  
Daniel Berman ◽  
Anna Buczak ◽  
Jeffrey Chavis ◽  
Cherita Corbett
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Generative Adversarial Networks ◽  
Restricted Boltzmann Machines ◽  
Domain Names ◽  
Survey Paper ◽  
Security Applications ◽  
Adversarial Networks ◽  
Network Intrusions ◽  
Broad Array

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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