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Reducible polytopes

- Yost, David

Repeats in graphs and digraphs

- Miller, Mirka, Nguyen, Minh Hoang, Simanjuntak, Rinovia

A survey on the connectivity of cages

- Miller, Mirka, Lin, Yuqing

Literature on image segmentation based on split - and - Merge techniques

- Faruquzzaman, A. B. M., Paiker, Nafize, Arafat, Jahidul, Ali, Mortuza, Sorwar, Golam

  • Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Ali, Mortuza , Sorwar, Golam
  • Date: 2008
  • Type: Text , Conference proceedings , Conference paper
  • Relation: ICITA 2008, Cairns, Qld., 23-26 June, ICITA, published in Proceedings of 5th International Conference on Information Technology and Application pp. 120-125.
  • Full Text: false
  • Reviewed:
  • Description: Image segmentation is a feverish issue due to drastically increasing the use of computer and the Internet. Various algorithms have been invented on this aspect. Among them, split-and-merge (SM) algorithm is highly lucrative now-a-days due to its simplicity and effectiveness in the sector of image processing. Numerous researchers have performed their research work on this algorithm to triumph over its drawbacks for its sustainable and competent implementation. This paper has consolidated the useful consideration and proposal of various researchers to formulate a strong base of knowledge for the future researcher. It has also tinted few unsettled drawbacks of SM algorithm which will open the casement of brainstorming as well as persuade them for future research on SM algorithm, thereby allow SM algorithm to attain a globally optimal algorithm for image segmentation.
  • Description: 5th International Conference on Information Technology and Applications, ICITA 2008

A multiobjective state transition algorithm for single machine scheduling

- Zhou, Xiaojun, Hanoun, Samer, Gao, David, Nahavandi, Saeid

Mining malware to detect variants

- Azab, Ahmad, Layton, Robert, Alazab, Mamoun, Oliver, Jonathan

  • Authors: Azab, Ahmad , Layton, Robert , Alazab, Mamoun , Oliver, Jonathan
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 5th Cybercrime and Trustworthy Computing Conference, CTC 2014; Aukland, New Zealand; 24th-25th November 2014 p. 44-53
  • Full Text: false
  • Reviewed:
  • Description: Cybercrime continues to be a growing challenge and malware is one of the most serious security threats on the Internet today which have been in existence from the very early days. Cyber criminals continue to develop and advance their malicious attacks. Unfortunately, existing techniques for detecting malware and analysing code samples are insufficient and have significant limitations. For example, most of malware detection studies focused only on detection and neglected the variants of the code. Investigating malware variants allows antivirus products and governments to more easily detect these new attacks, attribution, predict such or similar attacks in the future, and further analysis. The focus of this paper is performing similarity measures between different malware binaries for the same variant utilizing data mining concepts in conjunction with hashing algorithms. In this paper, we investigate and evaluate using the Trend Locality Sensitive Hashing (TLSH) algorithm to group binaries that belong to the same variant together, utilizing the k-NN algorithm. Two Zeus variants were tested, TSPY-ZBOT and MAL-ZBOT to address the effectiveness of the proposed approach. We compare TLSH to related hashing methods (SSDEEP, SDHASH and NILSIMSA) that are currently used for this purpose. Experimental evaluation demonstrates that our method can effectively detect variants of malware and resilient to common obfuscations used by cyber criminals. Our results show that TLSH and SDHASH provide the highest accuracy results in scoring an F-measure of 0.989 and 0.999 respectively. © 2014 IEEE.

Skype Traffic Classification Using Cost Sensitive Algorithms

- Azab, Azab, Layton, Robert, Alazab, Mamoun, Watters, Paul

  • Authors: Azab, Azab , Layton, Robert , Alazab, Mamoun , Watters, Paul
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Proceedings - 4th Cybercrime and Trustworthy Computing Workshop, CTC 2013 p. 14-21
  • Full Text: false
  • Reviewed:
  • Description: Voice over IP (VoIP) technologies such as Skype are becoming increasingly popular and widely used in different organisations, and therefore identifying the usage of this service at the network level becomes very important. Reasons for this include applying Quality of Service (QoS), network planning, prohibiting its use in some networks and lawful interception of communications. Researchers have addressed VoIP traffic classification from different viewpoints, such as classifier accuracy, building time, classification time and online classification. This previous research tested their models using the same version of a VoIP product they used for training the model, giving generalizability only to that version of the product. This means that as new VoIP versions are released, these classifiers become obsolete. In this paper, we address if this approach is applicable to detecting new, untrained, versions of Skype. We suggest that using cost-sensitive classifiers can help to improve the accuracy of detecting untrained versions, by testing compared to other algorithms. Our experiment demonstrates promising preliminary results to detect Skype version 4, by building a cost sensitive classifier on Skype version 3, achieving an F-measure score of 0.57. This is a drastic improvement from not using cost sensitivity, which scores an F-measure of 0. This approach may be enhanced to improve the detection results and extended to improve detection for other applications that change protocols from version to version.
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