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Utökad sökning > "information security" > Högskolan i Halmstad

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1.
  • Karresand, M., et al. (författare)
  • Disk Cluster Allocation Behavior in Windows and NTFS
  • 2020
  • Ingår i: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153. ; 5:1, s. 248-258
  • Tidskriftsartikel (refereegranskat)abstract
    • The allocation algorithm of a file system has a huge impact on almost all aspects of digital forensics, because it determines where data is placed on storage media. Yet there is only basic information available on the allocation algorithm of the currently most widely spread file system; NTFS. We have therefore studied the NTFS allocation algorithm and its behavior empirically. To do that we used two virtual machines running Windows 7 and 10 on NTFS formatted fixed size virtual hard disks, the first being 64 GiB and the latter 1 TiB in size. Files of different sizes were written to disk using two writing strategies and the $Bitmap files were manipulated to emulate file system fragmentation. Our results show that files written as one large block are allocated areas of decreasing size when the files are fragmented. The decrease in size is seen not only within files, but also between them. Hence a file having smaller fragments than another file is written after the file having larger fragments. We also found that a file written as a stream gets the opposite allocation behavior, i. e. its fragments are increasing in size as the file is written. The first allocated unit of a stream written file is always very small and hence easy to identify. The results of the experiment are of importance to the digital forensics field and will help improve the efficiency of for example file carving and timestamp verification. © 2019, The Author(s).
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2.
  • Mahdavi, Ehsan, et al. (författare)
  • ITL-IDS : Incremental Transfer Learning for Intrusion Detection Systems
  • 2022
  • Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 253
  • Tidskriftsartikel (refereegranskat)abstract
    • Utilizing machine learning methods to detect intrusion into computer networks is a trending topic in information security research. The limitation of labeled samples is one of the challenges in this area. This challenge makes it difficult to build accurate learning models for intrusion detection. Transfer learning is one of the methods to counter such a challenge in machine learning topics. On the other hand, the emergence of new technologies and applications might bring new vulnerabilities to computer networks. Therefore, the learning process cannot occur all at once. Incremental learning is a practical standpoint to confront this challenge. This research presents a new framework for intrusion detection systems called ITL-IDS that can potentially start learning in a network without prior knowledge. It begins with an incremental clustering algorithm to detect clusters’ numbers and shape without prior assumptions about the attacks. The outcomes are candidates to transfer knowledge between other instances of ITL-IDS. In each iteration, transfer learning provides target environments with incremental knowledge. Our evaluation shows that this method can combine incremental and transfer learning to identify new attacks. © 2022 Published by Elsevier B.V.
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