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2023-03-31
Ankita, D, Khilar, Rashmita, Kumar, M. Naveen.  2022.  Accuracy Analysis for Predicting Human Behaviour Using Deep Belief Network in Comparison with Support Vector Machine Algorithm. 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). :1–5.
To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines \$(\textbackslashmathrmp=0.55)(\textbackslashtextPiˆ0.05)\$. The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.
2020-07-16
Yousef, Muhammad, Torad, Mohamed A..  2019.  A Treatise On Conversational AI Agents: Learning From Humans’ Behaviour As A Design Outlook. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1—4.

Engineering a successful conversational AI agent is a tough process, and requires the consideration of achieving an effective communication between its various endpoints. In this paper, we present our perspective for designing an efficient conversational agent according to our belief that the existence of a centralized learning module that is capable of analyzing and understanding humans' behaviour from day one, and acting upon this behaviour is a must.

2020-07-03
Dinama, Dima Maharika, A’yun, Qurrota, Syahroni, Achmad Dahlan, Adji Sulistijono, Indra, Risnumawan, Anhar.  2019.  Human Detection and Tracking on Surveillance Video Footage Using Convolutional Neural Networks. 2019 International Electronics Symposium (IES). :534—538.

Safety is one of basic human needs so we need a security system that able to prevent crime happens. Commonly, we use surveillance video to watch environment and human behaviour in a location. However, the surveillance video can only used to record images or videos with no additional information. Therefore we need more advanced camera to get another additional information such as human position and movement. This research were able to extract those information from surveillance video footage by using human detection and tracking algorithm. The human detection framework is based on Deep Learning Convolutional Neural Networks which is a very popular branch of artificial intelligence. For tracking algorithms, channel and spatial correlation filter is used to track detected human. This system will generate and export tracked movement on footage as an additional information. This tracked movement can be analysed furthermore for another research on surveillance video problems.

2018-07-18
Gurulian, Iakovos, Markantonakis, Konstantinos, Akram, Raja Naeem, Mayes, Keith.  2017.  Artificial Ambient Environments for Proximity Critical Applications. Proceedings of the 12th International Conference on Availability, Reliability and Security. :5:1–5:10.

In the field of smartphones a number of proposals suggest that sensing the ambient environment can act as an effective anti-relay mechanism. However, existing literature is not compliant with industry standards (e.g. EMV and ITSO) that require transactions to complete within a certain time-frame (e.g. 500ms in the case of EMV contactless payments). In previous work the generation of an artificial ambient environment (AAE), and especially the use of infrared light as an AAE actuator was shown to have high success rate in relay attacks detection. In this paper we investigate the application of infrared as a relay attack detection technique in various scenarios, namely, contactless transactions (mobile payments, transportation ticketing, and physical access control), and continuous Two-Factor Authentication. Operating requirements and architectures are proposed for each scenario, while taking into account industry imposed performance requirements, where applicable. Protocols for integrating the solution into the aforementioned scenarios are being proposed, and formally verified. The impact on the performance is assessed through practical implementation. Proposed protocols are verified using Scyther, a formal mechanical verification tool. Finally, additional scenarios, in which this technique can be applied to prevent relay or other types of attacks, are discussed.

Merrill, Nick, Curran, Max T., Chuang, John.  2017.  Is the Future of Authenticity All In Our Heads?: Moving Passthoughts From the Lab to the World Proceedings of the 2017 New Security Paradigms Workshop. :70–79.

Passthoughts, in which a user thinks a secret thought to log in to services or devices, provides two factors of authentication (knowledge and inherence) in a single step. Since its proposal in 2005, passthoughts enjoyed a number of successful empirical studies. In this paper, we renew the promise of passthoughts authentication, outlining the main challenges that passthoughts must overcome in order to move from the lab to the real world. We propose two studies, which seek different angles at the fundamental questions we pose. Further, we propose it as a fruitful case study for thinking about what authentication can, and should, be expected to do, as it pushes up against questions of what sorts of "selves" authentication systems must be tasked with recognizing. Through this discussion, we raise novel possibilities for authentication broadly, such as "organic passwords" that change naturally over time, or systems that reject users who are not acting quite "like themselves."

Yin, Delina Beh Mei, Omar, Shariman, Talip, Bazilah A., Muklas, Amalia, Norain, Nur Afiqah Mohd, Othman, Abu Talib.  2017.  Fusion of Face Recognition and Facial Expression Detection for Authentication: A Proposed Model. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication. :21:1–21:8.

The paper presents a novel model of hybrid biometric-based authentication. Currently, the recognition accuracy of a single biometric verification system is often much reduced due to many factors such as the environment, user mode and physiological defects of an individual. Apparently, the enrolment of static biometric is highly vulnerable to impersonation attack. Due to the fact of single biometric authentication only offers one factor of verification, we proposed to hybrid two biometric attributes that consist of physiological and behavioural trait. In this study, we utilise the static and dynamic features of a human face. In order to extract the important features from a face, the primary steps taken are image pre-processing and face detection. Apparently, to distinguish between a genuine user or an imposter, the first authentication is to verify the user's identity through face recognition. Solely depending on a single modal biometric is possible to lead to false acceptance when two or more similar face features may result in a relatively high match score. However, it is found the False Acceptance Rate is 0.55% whereas the False Rejection Rate is 7%. By reason of the security discrepancies in the mentioned condition, therefore we proposed a fusion method whereby a genuine user will select a facial expression from the seven universal expression (i.e. happy, sad, anger, disgust, surprise, fear and neutral) as enrolled earlier in the database. For the proof of concept, it is proven in our results that even there are two or more users coincidently have the same face features, the selected facial expression will act as a password to be prominently distinguished a genuine or impostor user.

Das, Sauvik, Laput, Gierad, Harrison, Chris, Hong, Jason I..  2017.  Thumprint: Socially-Inclusive Local Group Authentication Through Shared Secret Knocks. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. :3764–3774.

Small, local groups who share protected resources (e.g., families, work teams, student organizations) have unmet authentication needs. For these groups, existing authentication strategies either create unnecessary social divisions (e.g., biometrics), do not identify individuals (e.g., shared passwords), do not equitably distribute security responsibility (e.g., individual passwords), or make it difficult to share or revoke access (e.g., physical keys). To explore an alternative, we designed Thumprint: inclusive group authentication with a shared secret knock. All group members share one secret knock, but individual expressions of the secret are discernible. We evaluated the usability and security of our concept through two user studies with 30 participants. Our results suggest that (1) individuals who enter the same shared thumprint are distinguishable from one another, (2) that people can enter thumprints consistently over time, and (3) that thumprints are resilient to casual adversaries.

Mohsin, J. K., Han, Liangxiu, Hammoudeh, Mohammad, Hegarty, Rob.  2017.  Two Factor Vs Multi-factor, an Authentication Battle in Mobile Cloud Computing Environments. Proceedings of the International Conference on Future Networks and Distributed Systems. :39:1–39:10.

Mobile devices offer a convenient way of accessing our digital lives and many of those devices hold sensitive data that needs protecting. Mobile and wireless communications networks, combined with cloud computing as Mobile Cloud Computing (MCC), have emerged as a new way to provide a rich computational environment for mobile users, and business opportunities for cloud providers and network operators. It is the convenience of the cloud service and the ability to sync across multiple platforms/devices that has become the attraction to cloud computing. However, privacy, security and trust issues may still be a barrier that impedes the adoption of MCC by some undecided potential users. Those users still need to be convinced of the security of mobile devices, wireless networks and cloud computing. This paper is the result of a comprehensive review of one typical secure measure-authentication methodology research, spanning a period of five years from 2012–2017. MCC capabilities for sharing distributed resources is discussed. Authentication in MCC is divided in to two categories and the advantages of one category over its counterpart are presented, in the process of attempting to identify the most secure authentication scheme.

Abidin, Aysajan, Argones Rúa, Enrique, Peeters, Roel.  2017.  Uncoupling Biometrics from Templates for Secure and Privacy-Preserving Authentication. Proceedings of the 22Nd ACM on Symposium on Access Control Models and Technologies. :21–29.

Biometrics are widely used for authentication in several domains, services and applications. However, only very few systems succeed in effectively combining highly secure user authentication with an adequate privacy protection of the biometric templates, due to the difficulty associated with jointly providing good authentication performance, unlinkability and irreversibility to biometric templates. This thwarts the use of biometrics in remote authentication scenarios, despite the advantages that this kind of architectures provides. We propose a user-specific approach for decoupling the biometrics from their binary representation before using biometric protection schemes based on fuzzy extractors. This allows for more reliable, flexible, irreversible and unlinkable protected biometric templates. With the proposed biometrics decoupling procedures, biometric metadata, that does not allow to recover the original biometric template, is generated. However, different biometric metadata that are generated starting from the same biometric template remain statistically linkable, therefore we propose to additionally protect these using a second authentication factor (e.g., knowledge or possession based). We demonstrate the potential of this approach within a two-factor authentication protocol for remote biometric authentication in mobile scenarios.

Weidman, Jake, Grossklags, Jens.  2017.  I Like It, but I Hate It: Employee Perceptions Towards an Institutional Transition to BYOD Second-Factor Authentication. Proceedings of the 33rd Annual Computer Security Applications Conference. :212–224.

The continued acceptance of enhanced security technologies in the private sector, such as two-factor authentication, has prompted significant changes of organizational security practices. While past work has focused on understanding how users in consumer settings react to enhanced security measures for banking, email, and more, little work has been done to explore how these technological transitions and applications occur within organizational settings. Moreover, while many corporations have invested significantly to secure their networks for the sake of protecting valuable intellectual property, academic institutions, which also create troves of intellectual property, have fallen behind in this endeavor. In this paper, we detail a transition from a token-based, two-factor authentication system within an academic institution to an entirely digital system utilizing employee-owned mobile devices. To accomplish this, we first conducted discussions with staff from the Information Security Office to understand the administrative perspective of the transition. Second, our key contribution is the analysis of an in-depth survey to explore the perceived benefits and usability of the novel technological requirements from the employee perspective. In particular, we investigate the implications of the new authentication system based on employee acceptance or opposition to the mandated technological transition, with a specific focus on the utilization of personal devices for workplace authentication.

Soni, Preeti, Ali, Rifaqat, Pal, Arup Kumar.  2017.  A Two-factor Based Remote User Authentication Scheme Using ElGamal Cryptosystem. Proceedings of the ACM Workshop on Internet of Things (IoT) Security: Issues and Innovations. :3:1–3:6.

Remote user authentication is an essential process to provide services securely during accessing on-line applications where its aim is to find out the legitimacy of an user. The traditional password based remote user authentication is quite popular and widely used but such schemes are susceptible to dictionary attack. To enhance the system security, numerous password based remote user authentication schemes using smartcard have been submitted. However, most of the schemes proposed are either computationally expensive or vulnerable to several kinds of known attacks. In this paper, the authors have developed a two factor based remote user authentication scheme using ElGamal cryptosystem. The validity of the proposed scheme is also confirmed through BAN logic. Besides that authors have done security analysis and compared with related schemes which proclaim that the proposed scheme is able to resist against several kinds of known attacks effectively. The proposed scheme is also simulated with AVISPA tool and expected outcome is achieved where it ensures that the scheme is secured against some known attacks. Overall, the presented scheme is suitable, secure and applicable in any real time applications.

2017-12-20
Williams, N., Li, S..  2017.  Simulating Human Detection of Phishing Websites: An Investigation into the Applicability of the ACT-R Cognitive Behaviour Architecture Model. 2017 3rd IEEE International Conference on Cybernetics (CYBCONF). :1–8.

The prevalence and effectiveness of phishing attacks, despite the presence of a vast array of technical defences, are due largely to the fact that attackers are ruthlessly targeting what is often referred to as the weakest link in the system - the human. This paper reports the results of an investigation into how end users behave when faced with phishing websites and how this behaviour exposes them to attack. Specifically, the paper presents a proof of concept computer model for simulating human behaviour with respect to phishing website detection based on the ACT-R cognitive architecture, and draws conclusions as to the applicability of this architecture to human behaviour modelling within a phishing detection scenario. Following the development of a high-level conceptual model of the phishing website detection process, the study draws upon ACT-R to model and simulate the cognitive processes involved in judging the validity of a representative webpage based primarily around the characteristics of the HTTPS padlock security indicator. The study concludes that despite the low-level nature of the architecture and its very basic user interface support, ACT-R possesses strong capabilities which map well onto the phishing use case, and that further work to more fully represent the range of human security knowledge and behaviours in an ACT-R model could lead to improved insights into how best to combine technical and human defences to reduce the risk to end users from phishing attacks.