CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions

The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and data are strong defense systems against malicious bots. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication process to confirm that the user is a human hence, access is granted. This paper provides an in-depth survey on CAPTCHAs and focuses on two main things: (1) a detailed discussion on various CAPTCHA types along with their advantages, disadvantages, and design recommendations, and (2) an in-depth analysis of different CAPTCHA breaking techniques. The survey is based on over two hundred studies on the subject matter conducted since 2003 to date. The analysis reinforces the need to design more attack-resistant CAPTCHAs while keeping their usability intact. The paper also highlights the design challenges and open issues related to CAPTCHAs. Furthermore, it also provides useful recommendations for breaking CAPTCHAs

Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain

Implementation of Captcha as Graphical Passwords For Multi Security

To validate human users, passwords play a vital role in computer security. Graphical passwords offer more security than text-based passwords, this is due to the reason that the user replies on graphical passwords. Normal users choose regular or unforgettable passwords which can be easy to guess and are prone to Artificial Intelligence problems. Many harder to guess passwords involve more mathematical or computational complications. To counter these hard AI problems a new Captcha technology known as, Captcha as Graphical Password (CaRP), from a novel family of graphical password systems has been developed. CaRP is both a Captcha and graphical password scheme in one. CaRP mainly helps in hard AI problems and security issues like online guess attacks, relay attacks, and shoulder-surfing attacks if combined with dual view technologies. Pass-points, a new methodology from CaRP, addresses the image hotspot problem in graphical password systems which lead to weak passwords. CaRP also implements a combination of images or colors with text which generates session passwords, that helps in authentication because with session passwords every time a new password is generated and is used only once. To counter shoulder surfing, CaRP provides cheap security and usability and thus improves online security. CaRP is not a panacea; however, it gives protection and usability to some online applications for improving online security

Human-Aided Artificial Intelligence: Or, How to Run Large Computations in Human Brains? Towards a Media Sociology of Machine Learning

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions

Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.Web of Science20221art. no.

Artificial Intelligence in Computer Networks : Role of AI in Network Security

Artificial Intelligence (AI) in computer networks has been emerging for the last decade, there are revolutionary inventions that have created automation and digitalization in the fields of the Internet. The layout of computer networks works in layers of topologies with the help of AI, a virtual layer of software has been added that runs predictive algorithms of Artificial Neural Networks (ANNs) with the help of Machine Learning (ML) and Deep Learning (DL). This thesis describes the relation between AI algorithms and duplication of human cognitive behavior in emerging technologies. The advantages of AI in computer networks include automation, digitalization, Internet of Things (IoT), centralization of data, etc. At the same time, the biggest disadvantage is the ethical violation of privacy and the security of data. It is further discussed in the thesis that Artificial Intelligence uses many security protocols, including Next-Generation Firewalls, to prevent security violations. The Software Network Analysis (SNA) and Software Defined Networks (SDN) play an important role in Artificial Intelligence in computer Networks. This thesis aims to analyze the relationship between the development of AI algorithms and the duplication of the human cognitive behavior in various emerging technologies. Software Network Analysis (SNA) and Software Defined Networks (SDN) are critical components of computer network artificial intelligence. The purpose of this dissertation is to investigate the relationship between AI algorithms and network security. The thesis analyzes 2 main aspects, the role of Artificial Intelligence in Computer Networks and how Artificial Intelligence is helping in securing computer networks to deal with the modern network threats. Security today has become one of the main concerns, everyday a production networks receives arounds thousands of attacks of different scales, and proper network security measures are not configured and taken, a lot can be compromised. Network virtualization, Cloud Computing, has seen exponentially growth in few past years, because of the trend of less human interaction, and minimizing of doing repeated tasks over and over. Data in today’s world is now more important than it has been in decades earlier, this is because today everything is moving towards digitalization, proper Information Security policies are derived and implemented all over the world to ensure the protection of Data. Europe has its own General Data Protection Regulation (GDPR) which ensures that every company who deals with data is to implement certain measures to ensure the data is protected which also involves implementing the right network security measures so that the right people have the access to the sensitive information. This thesis covers the overall impact of Artificial Intelligence in Computer Networks and Network Security

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud