Reverse engineering of file systems is indispensable for tool testing, accurate evidence acquisition, and correct interpretation of data structures by law enforcement in criminal investigations. This position paper examines emerging techno-legal challenges from the practice of reverse engineering for law enforcement purposes. We demonstrate that this new context creates uncertainties about the legality of tools and methods used for evidence acquisition and the compliance of law enforcement with obligations to protect intellectual property and confidential information. Further identified are gaps between legal provisions and practice related to disclosure and peer-review of sensitive digital forensic methodology, trade secrets in investigations, and governmental vulnerability disclosure. It is demonstrated that reverse engineering of file systems is insufficiently addressed by legislators, which results in a lack of file system interpretation and validation information for law enforcement and their dependence on tools. Outlined are recommendations for further developments of digital forensic regulation.

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.

We have applied the generalized and universal distance measure NCD-Normalized Compression Distance-to the problem of determining the types of file fragments via example. A corpus of files that can be redistributed to other researchers in the field was developed and the NCD algorithm using k-nearest-neighbor as a classification algorithm was applied to a random selection of file fragments. The experiment covered circa 2000 fragments from 17 different file types. While the overall accuracy of the n-valued classification only improved the prior probability of the class from approximately 6% to circa 50% overall, the classifier reached accuracies of 85%-100% for the most successful file types.

Despite several years of intensive study, intrusion detection systems still suffer from a key deficiency: A high rate of false alarms. To counteract this, this paper proposes to visualise the state of the computer system such that the operator can determine whether a violation has taken place. To this end a very simple anomaly detection inspired log reduction scheme is combined with graph visualisation, and applied to the log of a webserver with the intent of detecting patterns of benign and malicious (or suspicious) accesses. The combination proved to be effective. The visualisation of the output of the anomaly detection system counteracted its high rate of false alarms, while the anomaly based log reduction helped reduce the log data to manageable proportions. The visualisation was more successful in helping identifying benign accesses than malicious accesses. All the types of malicious accesses present in the log data were found. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Despite several years of intensive study, intrusion detection systems still suffer from two key deficiencies: Low detection rates and a high rate of false alarms.

Many different demands can be made of intrusion detection systems. An important requirement is that an intrusion detection system be effective ; that is, it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This article demonstrates that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate P(Intrusion***Alarm) , we have to achieve a (perhaps in some cases unattainably) low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.

Many different demands can be made of intrusion detection systems. An important requirement is that it be effective i.e. that it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This paper aims to demonstrate that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate, P(Intrusion|Alarm), we have to achieve a perhaps unattainably low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.