Digital Forensics to Intelligent Forensics

Irons, Alastair; Lallie, Harjinder Singh

doi:10.3390/fi6030584

Cited by 51 publications

(31 citation statements)

References 20 publications

(22 reference statements)

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“…While they do not specify the particular implementation of such system, the presented concepts are in accordance with the architecture of the TARZAN platform. Analogously, Irons and Lallie [26] discussed the shortcomings of the current analysis methods and suggested to use more intelligent techniques and demonstrated the possible application of artificial intelligence (AI) to computer forensics. The TARZAN platform can easily integrate the AI investigative methods because the underlying components provide rich libraries of various AI algorithms.…”

Section: Discussionmentioning

confidence: 99%

TARZAN: An Integrated Platform for Security Analysis

Rychlý

Ryšavý

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-In this paper, we present the TARZAN platform, an integrated platform for analysis of digital data from security incidents. The platform serves primarily as a middleware between data sources and data processing applications, however, it also provides several supporting services and a runtime environment for the applications. The supporting services, such as a data storage, a resource and application registry, a synchronization service, and a distributed computing platform, are utilized by the TARZAN applications for various security-oriented analyses on the integrated data ranging from an IT security incident detection to inference analyses of data from social networks or cryptocurrency transactions. To cope with a large amount of distributed data, both streamed in real-time and stored, and for the need of a large scale distributed computing, the platform has been designed as a big data processing system ensuring reliable, scalable, and cost-effective solution. The platform is demonstrated on the case of a security analysis of network traffic.

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Section: Discussionmentioning

confidence: 99%

TARZAN: An Integrated Platform for Security Analysis

Rychlý

Ryšavý

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…Automation approaches have been adopted into digital forensic processes to increase the speed and decrease the time required to identify a relevant evidence [34]. According to [35] it is necessary to enhance the use of the resources available. Intelligent techniques and tools could enhance the investigation in terms of efficiency and time when applied to digital investigations.…”

Section: Artificial Intelligence In Image Steganalysismentioning

confidence: 99%

Untitled

2016

IJNSA

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Embedding a message in media files, also known as steganography, is a common approach to hide secret information. It has been exploited by some criminals to confidentially exchange messages. As a countermeasure, tools have been developed in order to detect hidden information form digital media such as text, image, audio or video files. However the efficiency and performance of previous approaches still have room for improvement. In this research, we focus on algorithm design for better efficiency of hidden message detection from PNG files. We employ three classic AI approaches including neural network, fuzzy logic, and genetic algorithm and evaluate their efficiency and performance in controlled experiments. Finally we introduce our message detection system for PNG files based on LSB approach and present its usability in different case scenarios.

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“…People need to have confidence that their data are secure, and networks and services they use are secure and reliable, while the societies and the state need to be sure that the tools of the Internet are not misused for criminal activities. The growth in extent and complexity of cybercrime combined with the lack of time and resources in addressing cybercrime, and the need to confront cybercrime in near real time raises a need to process available digital evidences on the Internet using computational intelligence techniques such as Natural Language Processing (NLP) [1].…”

Section: Introductionmentioning

confidence: 99%

Open Class Authorship Attribution of Lithuanian Internet Comments using One-Class Classifier

Venčkauskas

Karpavičius

Damaševičius

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Internet can be misused by cyber criminals as a platform to conduct illegitimate activities (such as harassment, cyber bullying, and incitement of hate or violence) anonymously. As a result, authorship analysis of anonymous texts in Internet (such as emails, forum comments) has attracted significant attention in the digital forensic and text mining communities. The main problem is a large number of possible of authors, which hinders the effective identification of a true author. We interpret open class author attribution as a process of expert recommendation where the decision support system returns a list of suspected authors for further analysis by forensics experts rather than a single prediction result, thus reducing the scale of the problem. We describe the task formally and present algorithms for constructing the suspected author list. For evaluation we propose using a simple Winner-Takes-All (WTA) metric as well as a set of gain-discount model based metrics from the information retrieval domain (mean reciprocal rank, discounted cumulative gain and rank-biased precision). We also propose the List Precision (LP) metric as an extension of WTA for evaluating the usability of the suspected author list. For experiments, we use our own dataset of Internet comments in Lithuanian language and consider the use of language-specific (Lithuanian) lexical features together with general lexical features derived from English language. For classification we use one-class Support Vector Machine (SVM) classifier. The results of experiments show that the usability of open class author attribution can be improved considerably by using a set of language-specific lexical features together with general lexical features, while the proposed method can be used to reduce the number of suspected authors thus alleviating the work of forensic linguists.

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Digital Forensics to Intelligent Forensics

Cited by 51 publications

References 20 publications

TARZAN: An Integrated Platform for Security Analysis

TARZAN: An Integrated Platform for Security Analysis

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Open Class Authorship Attribution of Lithuanian Internet Comments using One-Class Classifier

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