The Experts below are selected from a list of 198 Experts worldwide ranked by ideXlab platform
Heba Hakh - One of the best experts on this subject based on the ideXlab platform.
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Users profiling using clickstream data analysis and classification
Conference on Computational Complexity, 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Bashar Alshboul, Hossam Faris, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
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CCC - Users Profiling Using Clickstream Data Analysis and Classification
2016 Cybersecurity and Cyberforensics Conference (CCC), 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Hossam Faris, Bashar Al-shboul, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
Wedyan Alswiti - One of the best experts on this subject based on the ideXlab platform.
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Users profiling using clickstream data analysis and classification
Conference on Computational Complexity, 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Bashar Alshboul, Hossam Faris, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
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CCC - Users Profiling Using Clickstream Data Analysis and Classification
2016 Cybersecurity and Cyberforensics Conference (CCC), 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Hossam Faris, Bashar Al-shboul, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
Gene Tsudik - One of the best experts on this subject based on the ideXlab platform.
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assentication User de authentication and lunchtime attack mitigation with seated posture biometric
Applied Cryptography and Network Security, 2018Co-Authors: Tyler Kaczmarek, Ercan Ozturk, Gene TsudikAbstract:Biometric techniques are often used as an extra security factor in authenticating human Users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify Users, which typically involves some User burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well-suited for continuous and more unobtrusive operation. One important application domain for biometrics is de-authentication: a means of quickly detecting absence of a previously-Authenticated User and immediately terminating that User’s secure sessions. De-authentication is crucial for mitigating so-called Lunchtime Attacks, whereby an insider adversary takes over an Authenticated state of a careless User who leaves her computer.
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Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric
arXiv: Cryptography and Security, 2017Co-Authors: Tyler Kaczmarek, Ercan Ozturk, Gene TsudikAbstract:Biometric techniques are often used as an extra security factor in authenticating human Users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify Users, which typically involves some User burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously Authenticated User and immediately terminating that User's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) Authenticated state of a careless User who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human User's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via User experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of Users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of Users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, Users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques.
Hossam Faris - One of the best experts on this subject based on the ideXlab platform.
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Users profiling using clickstream data analysis and classification
Conference on Computational Complexity, 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Bashar Alshboul, Hossam Faris, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
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CCC - Users Profiling Using Clickstream Data Analysis and Classification
2016 Cybersecurity and Cyberforensics Conference (CCC), 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Hossam Faris, Bashar Al-shboul, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
Jafar Alqatawna - One of the best experts on this subject based on the ideXlab platform.
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Users profiling using clickstream data analysis and classification
Conference on Computational Complexity, 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Bashar Alshboul, Hossam Faris, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
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CCC - Users Profiling Using Clickstream Data Analysis and Classification
2016 Cybersecurity and Cyberforensics Conference (CCC), 2016Co-Authors: Wedyan Alswiti, Jafar Alqatawna, Hossam Faris, Bashar Al-shboul, Heba HakhAbstract:The nature of today's online communication and the emergence of online social networks have introduced a great challenge related to the identification and the verification of Users in web environments. The assumption that every User interact uniquely with a web site provides a baseline for studying User identification based on historical records of User's interactions with specific web site. In this paper, we proposed an approach for using clickstream data to identify Users based on their navigational behavior. The study investigates using server-side clickstream data collected from previous Users' interaction to create a behavioral profile per User. User's profile can be used to identify possible future interactions, which can be associated with distinguishing Authenticated User from malicious User. To accomplish this identification, the ability to gather a historical record for User's navigation in a specific web site, is investigated. The goal of the collected data is to use it for training a model that is able to identify if a future interaction can be associated with a certain User.
