The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform

Tien D Phan - One of the best experts on this subject based on the ideXlab platform.

  • a language model for Compromised User analysis
    Network Operations and Management Symposium, 2018
    Co-Authors: Nur A Zincirheywood, Tien D Phan
    Abstract:

    Identifying Compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this paper, we explore a language model that is based on artificial neural networks to differentiate the writing styles of different Users on short text messages. In doing so, our aim is to be able to identify Compromised User accounts. Our results indicate that we can learn the language model on one dataset and can generalize it to different datasets with approximately 85% accuracy without any modifications to the language model.

  • NOMS - A language model for Compromised User analysis
    NOMS 2018 - 2018 IEEE IFIP Network Operations and Management Symposium, 2018
    Co-Authors: A. Nur Zincir-heywood, Tien D Phan
    Abstract:

    Identifying Compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this paper, we explore a language model that is based on artificial neural networks to differentiate the writing styles of different Users on short text messages. In doing so, our aim is to be able to identify Compromised User accounts. Our results indicate that we can learn the language model on one dataset and can generalize it to different datasets with approximately 85% accuracy without any modifications to the language model.

Sajid Anwar - One of the best experts on this subject based on the ideXlab platform.

  • Compromised User credentials detection in a digital enterprise using behavioral analytics
    Future Generation Computer Systems, 2019
    Co-Authors: Saleh Shah, Feras Al-obeidat, Francis Chow, Babar Shah, Fernando Moreira, Adnan Amin, Sajid Anwar
    Abstract:

    Abstract In today’s digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of next-generation data security or cybercrime which is continually increasing issue due to the digital transformation four essential pillars—cloud computing, big data analytics, social and mobile computing. Data transformation-driven enterprises should ready to handle this next-generation data security problem, in particular, the Compromised User credential (CUC). When an intruder or cybercriminal develops trust relationships as a legitimate account holder and then gain privileged access to the system for misuse. Many state-of-the-art risk mitigation tools are being developed, such as encrypted and secure password policy, authentication, and authorization mechanism. However, the CUC has become more complex and increasingly critical to the digital transformation process of the enterprise’s database by a cybercriminal, we propose a novel technique that effectively detects CUC at the enterprise-level. The proposed technique is learning from the User’s behavior and builds a knowledge base system (KBS) which observe changes in the User’s operational behavior. For that reason, a series of experiments were carried out on the dataset that collected from a sensitive database. All empirical results are validated through well-known evaluation measures, such as (i) accuracy, (ii) sensitivity, (iii) specificity, (iv) prudence accuracy, (v) precision, (vi) f-measure, and (vii) error rate. The experiments show that the proposed approach obtained weighted accuracy up to 99% and overall error of about 1%. The results clearly demonstrate that the proposed model efficiently can detect CUC which may keep an organization safe from major damage in data through cyber-attacks.

  • A prudent based approach for Compromised User credentials detection
    Cluster Computing, 2018
    Co-Authors: Adnan Amin, Feras Al-obeidat, Babar Shah, Sajid Anwar, Asad Masood Khattak, Awais Adnan
    Abstract:

    Compromised User credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting Compromised legitimate Users’ credentials. This is because cyber-criminals can gain access to legitimate Users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the User’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about Compromised Users’ activities for decision or policy makers as to which User is more critical and requires more consideration as compared to less crucial User based prevalence value.

  • Compromised User Credentials Detection Using Temporal Features: A Prudent Based Approach
    Proceedings of the 9th International Conference on Computer and Automation Engineering - ICCAE '17, 2017
    Co-Authors: Adnan Amin, Babar Shah, Sajid Anwar, Asad Masood Khattak
    Abstract:

    This study exposes a serious and rapidly growing cyber threat of Compromised legitimate User credentials which is very effective for cyber-criminals to gain trusted relationships with the account owners. Such a Compromised User's credentials ultimately result in damage incurred by the attacker at large-scale. Moreover, the detection of Compromised legitimate User activities is crucial in competitive and sensitive organizations because wrong data is more difficult to clean from the database. The proposed study presents a novel approach to detect Compromised Users' activity in a live database. Our approach uses a composition of prudence analysis, ripple down rules (RDR) and simulated experts (SE) to detect and identify accounts that experience a sudden change in behavior. We collected data from a sensitive running database for a period of Six months and evaluate the proposed technique. The results show that this combined model can fully detect outlier User's activity and can provide useful information for the concerned decision maker.

A. Nur Zincir-heywood - One of the best experts on this subject based on the ideXlab platform.

  • NOMS - A language model for Compromised User analysis
    NOMS 2018 - 2018 IEEE IFIP Network Operations and Management Symposium, 2018
    Co-Authors: A. Nur Zincir-heywood, Tien D Phan
    Abstract:

    Identifying Compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this paper, we explore a language model that is based on artificial neural networks to differentiate the writing styles of different Users on short text messages. In doing so, our aim is to be able to identify Compromised User accounts. Our results indicate that we can learn the language model on one dataset and can generalize it to different datasets with approximately 85% accuracy without any modifications to the language model.

Nur A Zincirheywood - One of the best experts on this subject based on the ideXlab platform.

  • a language model for Compromised User analysis
    Network Operations and Management Symposium, 2018
    Co-Authors: Nur A Zincirheywood, Tien D Phan
    Abstract:

    Identifying Compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this paper, we explore a language model that is based on artificial neural networks to differentiate the writing styles of different Users on short text messages. In doing so, our aim is to be able to identify Compromised User accounts. Our results indicate that we can learn the language model on one dataset and can generalize it to different datasets with approximately 85% accuracy without any modifications to the language model.

Adnan Amin - One of the best experts on this subject based on the ideXlab platform.

  • Compromised User credentials detection in a digital enterprise using behavioral analytics
    Future Generation Computer Systems, 2019
    Co-Authors: Saleh Shah, Feras Al-obeidat, Francis Chow, Babar Shah, Fernando Moreira, Adnan Amin, Sajid Anwar
    Abstract:

    Abstract In today’s digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of next-generation data security or cybercrime which is continually increasing issue due to the digital transformation four essential pillars—cloud computing, big data analytics, social and mobile computing. Data transformation-driven enterprises should ready to handle this next-generation data security problem, in particular, the Compromised User credential (CUC). When an intruder or cybercriminal develops trust relationships as a legitimate account holder and then gain privileged access to the system for misuse. Many state-of-the-art risk mitigation tools are being developed, such as encrypted and secure password policy, authentication, and authorization mechanism. However, the CUC has become more complex and increasingly critical to the digital transformation process of the enterprise’s database by a cybercriminal, we propose a novel technique that effectively detects CUC at the enterprise-level. The proposed technique is learning from the User’s behavior and builds a knowledge base system (KBS) which observe changes in the User’s operational behavior. For that reason, a series of experiments were carried out on the dataset that collected from a sensitive database. All empirical results are validated through well-known evaluation measures, such as (i) accuracy, (ii) sensitivity, (iii) specificity, (iv) prudence accuracy, (v) precision, (vi) f-measure, and (vii) error rate. The experiments show that the proposed approach obtained weighted accuracy up to 99% and overall error of about 1%. The results clearly demonstrate that the proposed model efficiently can detect CUC which may keep an organization safe from major damage in data through cyber-attacks.

  • A prudent based approach for Compromised User credentials detection
    Cluster Computing, 2018
    Co-Authors: Adnan Amin, Feras Al-obeidat, Babar Shah, Sajid Anwar, Asad Masood Khattak, Awais Adnan
    Abstract:

    Compromised User credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting Compromised legitimate Users’ credentials. This is because cyber-criminals can gain access to legitimate Users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the User’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about Compromised Users’ activities for decision or policy makers as to which User is more critical and requires more consideration as compared to less crucial User based prevalence value.

  • Compromised User Credentials Detection Using Temporal Features: A Prudent Based Approach
    Proceedings of the 9th International Conference on Computer and Automation Engineering - ICCAE '17, 2017
    Co-Authors: Adnan Amin, Babar Shah, Sajid Anwar, Asad Masood Khattak
    Abstract:

    This study exposes a serious and rapidly growing cyber threat of Compromised legitimate User credentials which is very effective for cyber-criminals to gain trusted relationships with the account owners. Such a Compromised User's credentials ultimately result in damage incurred by the attacker at large-scale. Moreover, the detection of Compromised legitimate User activities is crucial in competitive and sensitive organizations because wrong data is more difficult to clean from the database. The proposed study presents a novel approach to detect Compromised Users' activity in a live database. Our approach uses a composition of prudence analysis, ripple down rules (RDR) and simulated experts (SE) to detect and identify accounts that experience a sudden change in behavior. We collected data from a sensitive running database for a period of Six months and evaluate the proposed technique. The results show that this combined model can fully detect outlier User's activity and can provide useful information for the concerned decision maker.