Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees

Christo Panchev, P. Dobrev, J. Nicholson

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Recently, mobile devices such as smartphones and tablets have emerged as one of the most popular forms of communication. This trend raises the question about the security of the private data and communication of the people using those devices. With increased computational resources and versatility the number of security threats on such devices is growing rapidly. Therefore, it is vital for security specialists to find adequate anti-measures against the threats. Machine Learning approaches with their ability to learn from and adapt to their environments provide a promising approach to modelling and protecting against security threats on mobile devices. This paper presents a comparative study and implementation of Decision Trees and Neural Network models for the detection of port scanning showing the differences between the responses on a desktop platform and a mobile device and the ability of the Neural Network model to adapt to the different environment and computational resource available on a mobile platform.
Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings
EditorsValeri Mladenov, Chrisina Jayne, Lazaros Iliadis
PublisherSpringer Verlag
Pages175-182
ISBN (Print)978-3-319-11070-7
DOIs
Publication statusPublished - 2014

Fingerprint

Decision trees
Mobile devices
Neural networks
Smartphones
Communication
Learning systems
Scanning

Bibliographical note

This paper is not available on the repository

Keywords

  • Intrusion Detection Port Scanning Cascade Correlation Neural Networks Decision Trees Android Mobile devices

Cite this

Panchev, C., Dobrev, P., & Nicholson, J. (2014). Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. In V. Mladenov, C. Jayne, & L. Iliadis (Eds.), Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings (pp. 175-182). Springer Verlag. https://doi.org/10.1007/978-3-319-11071-4_17

Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. / Panchev, Christo; Dobrev, P.; Nicholson, J.

Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings. ed. / Valeri Mladenov; Chrisina Jayne; Lazaros Iliadis. Springer Verlag, 2014. p. 175-182.

Research output: Chapter in Book/Report/Conference proceedingChapter

Panchev, C, Dobrev, P & Nicholson, J 2014, Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. in V Mladenov, C Jayne & L Iliadis (eds), Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings. Springer Verlag, pp. 175-182. https://doi.org/10.1007/978-3-319-11071-4_17
Panchev C, Dobrev P, Nicholson J. Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. In Mladenov V, Jayne C, Iliadis L, editors, Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings. Springer Verlag. 2014. p. 175-182 https://doi.org/10.1007/978-3-319-11071-4_17
Panchev, Christo ; Dobrev, P. ; Nicholson, J. / Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings. editor / Valeri Mladenov ; Chrisina Jayne ; Lazaros Iliadis. Springer Verlag, 2014. pp. 175-182
@inbook{cc5c2232c6e5480fbde21246ff17eca4,
title = "Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees",
abstract = "Recently, mobile devices such as smartphones and tablets have emerged as one of the most popular forms of communication. This trend raises the question about the security of the private data and communication of the people using those devices. With increased computational resources and versatility the number of security threats on such devices is growing rapidly. Therefore, it is vital for security specialists to find adequate anti-measures against the threats. Machine Learning approaches with their ability to learn from and adapt to their environments provide a promising approach to modelling and protecting against security threats on mobile devices. This paper presents a comparative study and implementation of Decision Trees and Neural Network models for the detection of port scanning showing the differences between the responses on a desktop platform and a mobile device and the ability of the Neural Network model to adapt to the different environment and computational resource available on a mobile platform.",
keywords = "Intrusion Detection Port Scanning Cascade Correlation Neural Networks Decision Trees Android Mobile devices",
author = "Christo Panchev and P. Dobrev and J. Nicholson",
note = "This paper is not available on the repository",
year = "2014",
doi = "10.1007/978-3-319-11071-4_17",
language = "English",
isbn = "978-3-319-11070-7",
pages = "175--182",
editor = "Valeri Mladenov and Chrisina Jayne and Lazaros Iliadis",
booktitle = "Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings",
publisher = "Springer Verlag",
address = "Austria",

}

TY - CHAP

T1 - Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees

AU - Panchev, Christo

AU - Dobrev, P.

AU - Nicholson, J.

N1 - This paper is not available on the repository

PY - 2014

Y1 - 2014

N2 - Recently, mobile devices such as smartphones and tablets have emerged as one of the most popular forms of communication. This trend raises the question about the security of the private data and communication of the people using those devices. With increased computational resources and versatility the number of security threats on such devices is growing rapidly. Therefore, it is vital for security specialists to find adequate anti-measures against the threats. Machine Learning approaches with their ability to learn from and adapt to their environments provide a promising approach to modelling and protecting against security threats on mobile devices. This paper presents a comparative study and implementation of Decision Trees and Neural Network models for the detection of port scanning showing the differences between the responses on a desktop platform and a mobile device and the ability of the Neural Network model to adapt to the different environment and computational resource available on a mobile platform.

AB - Recently, mobile devices such as smartphones and tablets have emerged as one of the most popular forms of communication. This trend raises the question about the security of the private data and communication of the people using those devices. With increased computational resources and versatility the number of security threats on such devices is growing rapidly. Therefore, it is vital for security specialists to find adequate anti-measures against the threats. Machine Learning approaches with their ability to learn from and adapt to their environments provide a promising approach to modelling and protecting against security threats on mobile devices. This paper presents a comparative study and implementation of Decision Trees and Neural Network models for the detection of port scanning showing the differences between the responses on a desktop platform and a mobile device and the ability of the Neural Network model to adapt to the different environment and computational resource available on a mobile platform.

KW - Intrusion Detection Port Scanning Cascade Correlation Neural Networks Decision Trees Android Mobile devices

U2 - 10.1007/978-3-319-11071-4_17

DO - 10.1007/978-3-319-11071-4_17

M3 - Chapter

SN - 978-3-319-11070-7

SP - 175

EP - 182

BT - Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings

A2 - Mladenov, Valeri

A2 - Jayne, Chrisina

A2 - Iliadis, Lazaros

PB - Springer Verlag

ER -