2018
Leenen, L., Aschmann, M., Grobler, M., & van Heerden, A. (2018). Facing the Culture Gap in Operationalising Cyber within a Military Context. In Proceedings of the 13th International Conference on Cyber Warfare and Security (ICCWS 2018). Washington D.C., United States of America: Academic Conferences International.
@{566,
author = {Louise Leenen and Michael Aschmann and Marthie Grobler and Adelai van Heerden},
title = {Facing the Culture Gap in Operationalising Cyber within a Military Context},
abstract = {},
year = {2018},
journal = {Proceedings of the 13th International Conference on Cyber Warfare and Security (ICCWS 2018)},
month = {March 2018},
publisher = {Academic Conferences International},
address = {Washington D.C., United States of America},
}
Jideani, P., Leenen, L., Alexander, B., & Barnes, J. (2018). Towards an Electronic Retail Cybersecurity Framework. In International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD 2018). Durban, South Africa: IEEE.
@{565,
author = {Paul Jideani and Louise Leenen and B. Alexander and Jay Barnes},
title = {Towards an Electronic Retail Cybersecurity Framework},
abstract = {},
year = {2018},
journal = {International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD 2018)},
month = {6-7 August 2018},
publisher = {IEEE},
address = {Durban, South Africa},
}
van Vuuren, J. J., & Leenen, L. (2018). Cybersecurity Capability and Capacity Building for South Africa. In Proceedings of the 13th IFIP Human Choice and Computers Conference (HCC13). Poznan, Poland.
@{564,
author = {Joey van Vuuren and Louise Leenen},
title = {Cybersecurity Capability and Capacity Building for South Africa},
abstract = {},
year = {2018},
journal = {Proceedings of the 13th IFIP Human Choice and Computers Conference (HCC13)},
month = {September 2018},
address = {Poznan, Poland},
}
Mouton, F., Nottingham, A., Leenen, L., & Venter, H. (2018). Finite State Machine for the Social Engineering Attack Detection Model. African Research Journal (SAIEE), 109(2).
@article{563,
author = {Francois Mouton and Alastair Nottingham and Louise Leenen and Hein Venter},
title = {Finite State Machine for the Social Engineering Attack Detection Model},
abstract = {},
year = {2018},
journal = {African Research Journal (SAIEE)},
volume = {109},
month = {June 2018},
issue = {2},
publisher = {South African Institute of Electrical Engineers},
}
2017
Aschmann, M., Leenen, L., & van Vuuren, J. J. (2017). The Utilisation of the Deep Web for Military Counter Terrorist Operations. In Proceedings of the 12th International Conference on Cyber Warfare and Security (ICCWS 2017). Dayton, United States of America: Academic Conferences International.
@{574,
author = {Michael Aschmann and Louise Leenen and Joey van Vuuren},
title = {The Utilisation of the Deep Web for Military Counter Terrorist Operations},
abstract = {},
year = {2017},
journal = {Proceedings of the 12th International Conference on Cyber Warfare and Security (ICCWS 2017)},
month = {March 2017},
publisher = {Academic Conferences International},
address = {Dayton, United States of America},
}
Botes, F. H., Leenen, L., & De La Harpe, R. (2017). Ant Colony Induced Decision Trees for Intrusion Detection. In Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017). Dublin, Ireland: Academic Conferences International.
@{573,
author = {Frans Botes and Louise Leenen and Retha De La Harpe},
title = {Ant Colony Induced Decision Trees for Intrusion Detection},
abstract = {},
year = {2017},
journal = {Proceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017)},
month = {June 2017},
publisher = {Academic Conferences International},
address = {Dublin, Ireland},
}
van Vuuren, J. J., Leenen, L., Plint, G., Zaaiman, J., & Phahlamohlaka, J. (2017). Formulating the Building Blocks for National Cyberpower. International Journal of Cyber Warfare and Terrorism, 7(3).
@article{572,
author = {Joey van Vuuren and Louise Leenen and Graeme Plint and Jannie Zaaiman and Jackie Phahlamohlaka},
title = {Formulating the Building Blocks for National Cyberpower},
abstract = {},
year = {2017},
journal = {International Journal of Cyber Warfare and Terrorism},
volume = {7},
pages = {616-628},
issue = {3},
publisher = {IGI Global},
}
Mouton, F., Nottingham, A., Leenen, L., & Venter, H. (2017). Underlying Finite State Machine for the Social Engineering Attack Detection Model. In 2017 Information Security for South Africa (ISSA). Johannesburg, South Africa: IEEE. http://doi.org/10.1109/ISSA.2017.8251781
Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. In general, human operators are often highly susceptible to manipulation, and tend to be one of the weakest links in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy with respect to formal definitions, attack frameworks, examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been proven to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the interconnections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. In addition, it facilitates additional analysis on state transitions that are difficult to extract from the original flowchart based model.
@{571,
author = {Francois Mouton and Alastair Nottingham and Louise Leenen and Hein Venter},
title = {Underlying Finite State Machine for the Social Engineering Attack Detection Model},
abstract = {Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. In general, human operators are often highly susceptible to manipulation, and tend to be one of the weakest links in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy with respect to formal definitions, attack frameworks, examples of attacks and detection models. In order to formally address social engineering in a broad context, this paper proposes the underlying finite state machine of the Social Engineering Attack Detection Model (SEADM). The model has been proven to successfully thwart social engineering attacks utilising either bidirectional communication, unidirectional communication or indirect communication. Proposing and exploring the underlying finite state machine of the model allows one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a more abstract and extensible model that highlights the interconnections between task categories associated with different scenarios. The finite state machine is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. In addition, it facilitates additional analysis on state transitions that are difficult to extract from the original flowchart based model.},
year = {2017},
journal = {2017 Information Security for South Africa (ISSA)},
pages = {98-105},
month = {August 2017},
publisher = {IEEE},
address = {Johannesburg, South Africa},
doi = {10.1109/ISSA.2017.8251781},
}
Botes, F. H., Leenen, L., & De La Harpe, R. (2017). Ant Tree Miner Amyntas: Automatic, Cost-Based Feature Selection for Intrusion Detection. Journal of Information Warfare, 16(4).
Intrusion Detection Systems (IDSs) analyse network traffic to identify suspicious patterns which indicate the intention to compromise the system. Traditional detection methods are still the norm for commercial products promoting a rigid, manual, and static detection platform. This paper focuses on recent advances in machine learning by implementing the Ant Tree Miner Amyntas (ATMa) classifier within intrusion detection. The proposed ATMa use Ant Colony Optimisation and a cost-based evaluation function to automatically select features from a data set before inducing Decision Trees (DTs) that classify network data.
@article{569,
author = {Frans Botes and Louise Leenen and Retha De La Harpe},
title = {Ant Tree Miner Amyntas: Automatic, Cost-Based Feature Selection for Intrusion Detection},
abstract = {Intrusion Detection Systems (IDSs) analyse network traffic to identify suspicious patterns which indicate the intention to compromise the system. Traditional detection methods are still the norm for commercial products promoting a rigid, manual, and static detection platform. This paper focuses on recent advances in machine learning by implementing the Ant Tree Miner Amyntas (ATMa) classifier within intrusion detection. The proposed ATMa use Ant Colony Optimisation and a cost-based evaluation function to automatically select features from a data set before inducing Decision Trees (DTs) that classify network data.},
year = {2017},
journal = {Journal of Information Warfare},
volume = {16},
pages = {73-92},
issue = {4},
publisher = {ArmisteadTEC},
}
2016
Plint, G., van Vuuren, J. J., & Leenen, L. (2016). Building Blocks for National Cyberpower. In Proceedings of the 11th International Conference on Cyber Warfare and Security (ICCWS 2016). Boston, United States of America: Academic Conferences International.
@{578,
author = {Graeme Plint and Joey van Vuuren and Louise Leenen},
title = {Building Blocks for National Cyberpower},
abstract = {},
year = {2016},
journal = {Proceedings of the 11th International Conference on Cyber Warfare and Security (ICCWS 2016)},
month = {March 2016},
publisher = {Academic Conferences International},
address = {Boston, United States of America},
}
van Vuuren, J. J., Leenen, L., Grobler, M., Chan, K., & Khan, Z. (2016). Morphological Ontology Design Engineering: A Methodology to Model Ill-structured Problems. In Mixed Methods Research for Improved Scientific Study. Hershey, Pennsylvania, United States of America: IGI Global.
@inbook{577,
author = {Joey van Vuuren and Louise Leenen and Marthie Grobler and K.F. Chan and Z. Khan},
title = {Morphological Ontology Design Engineering: A Methodology to Model Ill-structured Problems},
abstract = {},
year = {2016},
journal = {Mixed Methods Research for Improved Scientific Study},
pages = {262-291},
publisher = {IGI Global},
address = {Hershey, Pennsylvania, United States of America},
}
Mouton, F., Leenen, L., & Venter, H. (2016). Social Engineering Attack Examples, Templates and Scenarios. Computers & Security, 59. http://doi.org/10.1016/j.cose.2016.03.004
@article{576,
author = {Francois Mouton and Louise Leenen and Hein Venter},
title = {Social Engineering Attack Examples, Templates and Scenarios},
abstract = {},
year = {2016},
journal = {Computers & Security},
volume = {59},
pages = {186-209},
month = {March 2016},
publisher = {Elsevier},
doi = {10.1016/j.cose.2016.03.004},
}
van Heerden, R., Chan, P., Leenen, L., & Theron, J. (2016). Using an Ontology for Network Attack Planning. International Journal of Cyber Warfare and Terrorism, 6(3).
@article{575,
author = {Renier van Heerden and Peter Chan and Louise Leenen and Jacques Theron},
title = {Using an Ontology for Network Attack Planning},
abstract = {},
year = {2016},
journal = {International Journal of Cyber Warfare and Terrorism},
volume = {6},
month = {July-September 2016},
issue = {3},
publisher = {IGI Global},
}
2015
van Vuuren, J. J., Leenen, L., Grobler, M., Chan, K., & Khan, Z. (2015). Modelling the Cybersecurity Environment Using Morphological Ontology Design Engineering. In Proceedings of the 10th International Conference on Cyber Warfare and Security (ICCWS 2015). Kruger National Park, South Africa: Academic Conferences International.
@{582,
author = {Joey van Vuuren and Louise Leenen and Marthie Grobler and K.F. Chan and Z. Khan},
title = {Modelling the Cybersecurity Environment Using Morphological Ontology Design Engineering},
abstract = {},
year = {2015},
journal = {Proceedings of the 10th International Conference on Cyber Warfare and Security (ICCWS 2015)},
month = {March 2015},
publisher = {Academic Conferences International},
address = {Kruger National Park, South Africa},
}
Aschmann, M., van Vuuren, J. J., & Leenen, L. (2015). Cyber Armies: The Unseen Military in the Grid. In Proceedings of the 10th International Conference on Cyber Warfare and Security (ICCWS 2015). Kruger National Park, South Africa: Academic Conferences International.
@{581,
author = {Michael Aschmann and Joey van Vuuren and Louise Leenen},
title = {Cyber Armies: The Unseen Military in the Grid},
abstract = {},
year = {2015},
journal = {Proceedings of the 10th International Conference on Cyber Warfare and Security (ICCWS 2015)},
month = {March 2015},
publisher = {Academic Conferences International},
address = {Kruger National Park, South Africa},
}
