Data Mining Instant Messaging Communications to Perform Author Identification for Cybercrime Investigations

Orebaugh, Angela; Allnutt, Jeremy E.

doi:10.1007/978-3-642-11534-9_10

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Furthermore, the authorship or messaging patterns can be used for entity resolution. An Instant Messaging (IM) authorship analysis framework is proposed by Orebaugh et al [57,58] to determine the entity of a cyber-criminal (i.e., the author of messages). Their main motivation is that people have patterns and they repeat these patterns.…”

Section: Matching Profiles For Online Social Networkmentioning

confidence: 99%

User characterization for online social networks

Tuna

Akbas

Aksoy

et al. 2016

Soc. Netw. Anal. Min.

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Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are helpful for user characterization as online users may not always reveal their true identity or attributes. We especially focused on user attribute determination such as gender, age, etc.; user behavior analysis such as motives for deception; mental models that are indicators of user behavior; user categorization such as bots vs. humans; and entity matching on different social networks. We believe our summary of analysis of user characterization will provide important insights to researchers and better services to online users

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Section: Matching Profiles For Online Social Networkmentioning

confidence: 99%

User characterization for online social networks

Tuna

Akbas

Aksoy

et al. 2016

Soc. Netw. Anal. Min.

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“…Based on the above observations, three types of features are developed to identify the level of offensiveness, which leveraged from authorship analysis research on cybercrime investigation [20][21][22][23][24][25]: style features, structural features, and content-specific features. Style features and structural features capture users' language patterns, while contentspecific features help to identify abnormal contents in users' conversations.…”

Section: B) Additional Features Extracted From Users' Lanuage Profilesmentioning

confidence: 99%

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

Chen

Zhou

Zhu

et al. 2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

402

331

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Abstract-Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content, and user-level offensiveness evaluation is still an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives/profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyberbullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24% and recall of 94.34% in sentence offensive detection, as well as precision of 77.9% and recall of 77.8% in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media.

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“…A variety of computer-aided statistical methods are used to analyze text to determine the most plausible author of a piece of text in authorship identification. Orebaugh & Allnutt (2010) present an Instant Messaging (IM) authorship analysis framework to determine the identity of an author. The proposed framework is composed of data pre-processing, feature extraction, and classification stages.…”

Section: Authorship Analysismentioning

confidence: 99%

“…Author writeprints are created with stylometric features extracted from IM messages and statistical methods are used to analyze and evaluate the writeprints. Similar to the study by Orebaugh & Allnutt (2010), their framework includes developing a stylometric feature set, pre-processing the data, creating writeprints and creating PCA (principal components analysis) visualizations of writeprints. They use a 356-dimensional vector as the feature set including lexical, syntactic, and structural features.…”

Section: Authorship Analysismentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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Data Mining Instant Messaging Communications to Perform Author Identification for Cybercrime Investigations

Cited by 9 publications

References 18 publications

User characterization for online social networks

User characterization for online social networks

Detecting Offensive Language in Social Media to Protect Adolescent Online Safety

A Survey of Social Network Forensics

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