OS and Networks Commons^™

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Kno.e.sis Publications

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new …

Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng

Kno.e.sis Publications

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and …

A Novel Approach For Classifying Gene Expression Data Using Topic Modeling, Soon Jye Kho, Himi Yalamanchili, Michael L. Raymer, Amit Sheth

Kno.e.sis Publications

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA inclustering as well as in classification of cancer and healthy tissues using lung cancer …

A Semantics-Based Measure Of Emoji Similarity, Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

Kno.e.sis Publications

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test …

Identifying Depressive Disorder In The Twitter Population, Goonmeet Bajaj, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth

Kno.e.sis Publications

Depression is a highly prevalent public health challenge and a major cause of disability across the globe.

• Annually 6.7% of Americans (that is, more than 16 million).
• Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires.
• Large temporal gaps and cognitive bias.

Social media provides a method for learning users' feelings, emotions, behaviors, and decisions in real-time.

Preliminary Investigation Of Walking Motion Using A Combination Of Image And Signal Processing, Bradley Schneider, Tanvi Banerjee

Kno.e.sis Publications

We present the results of analyzing gait motion in first-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera as well as validated using the ground truth dataset. Our preliminary results indicate that the extraction of activity from the video …

Analyzing Clinical Depressive Symptoms In Twitter, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Tanvi Banerjee, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

350 million people are suffering from clinical depression worldwide.

What Motivates High School Students To Take Precautions Against The Spread Of Influenza? A Data Science Approach To Latent Modeling Of Compliance With Preventative Practice, William L. Romine, Tanvi Banerjee, William R. Folk, Lloyd H. Barrow

Kno.e.sis Publications

– This study focuses on a central question: What key behavioral factors influence high school students’ compliance with preventative measures against the transmission of influenza? We use multilevel logistic regression to equate logit measures for eight precautions to students’ latent compliance levels on a common scale. Using linear regression, we explore the efficacy of knowledge of influenza, affective perceptions about influenza and its prevention, prior illness, and gender in predicting compliance. Hand washing and respiratory etiquette are the easiest precautions for students, and hand sanitizer use and keeping the hands away from the face are the most difficult. Perceptions of …

Sim Card Forensics: Digital Evidence, Nada Ibrahim, Nuha Al Naqbi, Farkhund Iqbal, Omar Alfandi

Annual ADFSL Conference on Digital Forensics, Security and Law

With the rapid evolution of the smartphone industry, mobile device forensics has become essential in cybercrime investigation. Currently, evidence forensically-retrieved from a mobile device is in the form of call logs, contacts, and SMSs; a mobile forensic investigator should also be aware of the vast amount of user data and network information that are stored in the mobile SIM card such as ICCID, IMSI, and ADN. The aim of this study is to test various forensic tools to effectively gather critical evidence stored on the SIM card. In the first set of experiments, we compare the selected forensic tools in …

Assessing The Gap: Measure The Impact Of Phishing On An Organization, Brad Wardman

Annual ADFSL Conference on Digital Forensics, Security and Law

Phishing has become one of the most recognized words associated with cybercrime. As more organizations are being targeted by phishing campaigns, there are more options within the industry to deter such attacks. However, there is little research into how much damage these campaigns are causing organizations. This paper will show how financial organizations can be impacted by phishing and present a method for accurately quantifying resultant monetary losses. The methodology presented in this paper can be adapted to other organizations in order to quantify phishing losses across industries.

Keywords: phishing, cybercrime, economics

Wban Security Management In Healthcare Enterprise Environments, Karina Bahena, Manghui Tu

Annual ADFSL Conference on Digital Forensics, Security and Law

As healthcare data are pushed online, consumers have raised big concerns on the breach of their personal information. Law and regulations have placed businesses and public organizations under obligations to take actions to prevent such data breaches. Various vulnerabilities have been identified in healthcare enterprise environments, in which the Wireless Body Area Networks (WBAN) remains to be a major vulnerability, which can be easily taken advantage of by determined adversaries. Thus, vulnerabilities of WBAN systems and the effective countermeasure mechanisms to secure WBAN are urgently needed. In this research, first, the architecture of WBAN system has been explored, and the …

Forensics Analysis Of Privacy Of Portable Web Browsers, Ahmad Ghafarian

Annual ADFSL Conference on Digital Forensics, Security and Law

Web browser vendors offer a portable web browser option which is considered as one of the features that provides user privacy. Portable web browser is a browser that can be launched from a USB flash drive without the need for its installation on the host machine. Most popular web browsers have portable versions of their browsers as well. Portable web browsing poses a great challenge to computer forensic investigators who try to reconstruct the past browsing history, in case of any computer incidence. This research examines various sources in the host machine such as physical memory, temporary, recent, event files, …

Reverse Engineering A Nit That Unmasks Tor Users, Matthew Miller, Joshua Stroschein, Ashley Podhradsky

Annual ADFSL Conference on Digital Forensics, Security and Law

This paper is a case study of a forensic investigation of a Network Investigative Technique (NIT) used by the FBI to deanonymize users of a The Onion Router (Tor) Hidden Service. The forensic investigators were hired by the defense to determine how the NIT worked. The defendant was ac- cused of using a browser to access illegal information. The authors analyzed the source code, binary files and logs that were used by the NIT. The analysis was used to validate that the NIT collected only necessary and legally authorized information. This paper outlines the publicly available case details, how the …

Malware In The Mobile Device Android Environment, Diana Hintea, Robert Bird, Andrew Walker

Annual ADFSL Conference on Digital Forensics, Security and Law

exploit smartphone operating systems has exponentially expanded. Android has become the main target to exploit due to having the largest install base amongst the smartphone operating systems and owing to the open access nature in which application installations are permitted. Many Android users are unaware of the risks associated with a malware infection and to what level current malware scanners protect them. This paper tests how efficient the currently available malware scanners are. To achieve this, ten representative Android security products were selected and tested against a set of 5,560 known and categorized Android malware samples. The tests were carried …

Forensic Analysis Of Smartphone Applications For Privacy Leakage, Diana Hintea, Chrysanthi Taramonli, Robert Bird, Rezhna Yusuf

Annual ADFSL Conference on Digital Forensics, Security and Law

Smartphone and tablets are personal devices that have diffused to near universal ubiquity in recent years. As Smartphone users become more privacy-aware and -conscious, research is needed to understand how “leakage” of private information (personally identifiable information – PII) occurs. This study explores how leakage studies in Droid devices should be adapted to Apple iOS devices. The OWASP Zed Attack Proxy (ZAP) is examined for 50 apps in various categories. This study confirms that: (1) most apps transmit unencrypted sensitive PII, (2) SSL is used by some recipient websites, but without corresponding app compliance with SSL, and (3) most apps …

Inferring Previously Uninstalled Applications From Residual Partial Artifacts, Jim Jones, Tahir Khan, Kathryn Laskey, Alex Nelson, Mary Laamanen, Douglas White

Annual ADFSL Conference on Digital Forensics, Security and Law

In this paper, we present an approach and experimental results to suggest the past presence of an application after the application has been uninstalled and the system has remained in use. Current techniques rely on the recovery of intact artifacts and traces, e.g., whole files, Windows Registry entries, or log file entries, while our approach requires no intact artifact recovery and leverages trace evidence in the form of residual partial files. In the case of recently uninstalled applications or an instrumented infrastructure, artifacts and traces may be intact and complete. In most cases, however, digital artifacts and traces are al- …

One-Time Pad Encryption Steganography System, Michael J. Pelosi, Gary Kessler, Michael Scott S. Brown

Annual ADFSL Conference on Digital Forensics, Security and Law

In this paper we introduce and describe a novel approach to adaptive image steganography which is combined with One-Time Pad encryption, and demonstrate the software which implements this methodology. Testing using the state-of-the-art steganalysis software tool StegExpose concludes the image hiding is reliably secure and undetectable using reasonably-sized message payloads (≤25% message bits per image pixel; bpp). Payload image file format outputs from the software include PNG, BMP, JP2, JXR, J2K, TIFF, and WEBP. A variety of file output formats is empirically important as most steganalysis programs will only accept PNG, BMP, and possibly JPG, as the file inputs.

Keywords: …

Applying Grounded Theory Methods To Digital Forensics Research, Ahmed Almarzooqi, Andrew Jones, Richard Howley

Annual ADFSL Conference on Digital Forensics, Security and Law

Deciding on a suitable research methodology is challenging for researchers. In this paper, grounded theory is presented as a systematic and comprehensive qualitative methodology in the emergent field of digital forensics research. This paper applies grounded theory in a digital forensics research project undertaken to study how organisations build and manage digital forensics capabilities. This paper gives a step-by-step guideline to explain the procedures and techniques of using grounded theory in digital forensics research. The paper gives a detailed explanation of how the three grounded theory coding methods (open, axial, and selective coding) can be used in digital forensics research. …

Covert6: A Tool To Corroborate The Existence Of Ipv6 Covert Channels, Raymond A. Hansen, Lourdes Gino, Dominic Savio

Annual ADFSL Conference on Digital Forensics, Security and Law

Covert channels are any communication channel that can be exploited to transfer information in a manner that violates the system’s security policy. Research in the field has shown that, like many communication channels, IPv4 and the TCP/IP protocol suite have been susceptible to covert channels, which could be exploited to leak data or be used for anonymous communications. With the introduction of IPv6, researchers are acutely aware that many vulnerabilities of IPv4 have been remediated in IPv6. However, a proof of concept covert channel system was demonstrated in 2006. A decade later, IPv6 and its related protocols have undergone major …

Acceleration Of Statistical Detection Of Zero-Day Malware In The Memory Dump Using Cuda-Enabled Gpu Hardware, Igor Korkin, Iwan Nesterow

Annual ADFSL Conference on Digital Forensics, Security and Law

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we will present new detection methods which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows’ memory content using a new method of Shannon Entropy calculation; methods of …

Using Computer Behavior Profiles To Differentiate Between Users In A Digital Investigation, Shruti Gupta, Marcus Rogers

Annual ADFSL Conference on Digital Forensics, Security and Law

Most digital crimes involve finding evidence on the computer and then linking it to a suspect using login information, such as a username and a password. However, login information is often shared or compromised. In such a situation, there needs to be a way to identify the user without relying exclusively on login credentials. This paper introduces the concept that users may show behavioral traits which might provide more information about the user on the computer. This hypothesis was tested by conducting an experiment in which subjects were required to perform common tasks on a computer, over multiple sessions. The …

Current Challenges And Future Research Areas For Digital Forensic Investigation, David Lillis, Brett A. Becker, Tadhg O’Sullivan, Mark Scanlon

Annual ADFSL Conference on Digital Forensics, Security and Law

Given the ever-increasing prevalence of technology in modern life, there is a corresponding increase in the likelihood of digital devices being pertinent to a criminal investigation or civil litigation. As a direct consequence, the number of investigations requiring digital forensic expertise is resulting in huge digital evidence backlogs being encountered by law enforcement agencies throughout the world. It can be anticipated that the number of cases requiring digital forensic analysis will greatly increase in the future. It is also likely that each case will require the analysis of an increasing number of devices including computers, smartphones, tablets, cloud-based services, Internet …

Forensic Analysis Of Ares Galaxy Peer-To-Peer Network, Frank Kolenbrander, Nhien-An Le-Khac, Tahar Kechadi

Annual ADFSL Conference on Digital Forensics, Security and Law

Child Abuse Material (CAM) is widely available on P2P networks. Over the last decade several tools were made for 24/7 monitoring of peer-to-peer (P2P) networks to discover suspects that use these networks for downloading and distribution of CAM. For some countries the amount of cases generated by these tools is so great that Law Enforcement (LE) just cannot handle them all. This is not only leading to backlogs and prioritizing of cases but also leading to discussions about the possibility of disrupting these networks and sending warning messages to potential CAM offenders. Recently, investigators are reporting that they are creating …

Keynote Speaker, Chuck Easttom

Annual ADFSL Conference on Digital Forensics, Security and Law

Conference Keynote Speaker, Chuck Easttom

Semantic, Cognitive, And Perceptual Computing: Paradigms That Shape Human Experience, Amit P. Sheth, Pramod Anantharam, Cory Henson

Kno.e.sis Publications

Unlike machine-centric computing, in which efficient data processing takes precedence over contextual tailoring, human-centric computation provides a personalized data interpretation that most users find highly relevant to their needs. The authors show how semantic, cognitive, and perceptual computing paradigms work together to produce actionable information.

A Study Of Social Web Data On Buprenorphine Abuse Using Semantic Web Technology, Raminta Daniulaityte, Amit P. Sheth

Kno.e.sis Publications

The Specific Aims of this application are to use a paradigmatic approach that combines Semantic Web technology, Natural Language Processing and Machine Learning techniques to:

1) Describe drug users’ knowledge, attitudes, and behaviors related to the non-medical use of Suboxone and Subutex as discussed on Web-based forums.
2) Identify and describe temporal patterns of non-medical use of Suboxone and Subutex as discussed on Web-based forums.

The research was carried out by an interdisciplinary team of members of the Center for Interventions, Treatment and Addictions Research (CITAR) and the Ohio Center of Excellence in Knowledge- enabled Computing (Kno.e.sis) at Wright State …

Building The Web Of Knowledge With Smart Iot Applications, Amelie Gyrard, Pankesh Patel, Amit P. Sheth, Martin Serrano

Kno.e.sis Publications

The Internet of Things (IoT) is experiencing fast adoption because of its positive impact to change all aspects of our lives, from agriculture in rural areas, to health and wellness, to smart home and smart-x applications in cities. The development of IoT applications and deployment of smart IoT-based solutions is just starting; smart IoT applications will modify our physical world and our interaction with cyber spaces, from how we remotely control appliances at home to how we care for patients or elderly persons. The massive deployment of IoT devices represents a tremendous economic impact and at the same time offers …

Co-Evolution Of Rdf Datasets, Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour, Sören Auer, Maria-Esther Vidal

Kno.e.sis Publications

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, …

Intent Classification Of Short-Text On Social Media, Hemant Purohit, Guozhu Dong, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis …

Feedback-Driven Radiology Exam Report Retrieval With Semantics, Sarasi Lalithsena, Luis Tari, Anna Von Reden, Benjamin Wilson, Brian J. Kolowitz, John Kalafut, Steven Gustafson, Amit P. Sheth

Kno.e.sis Publications

Clinical documents are vital resources for radiologists to have a better understanding of patient history. The use of clinical documents can complement the often brief reasons for exams that are provided by physicians in order to perform more informed diagnoses. With the large number of study exams that radiologists have to perform on a daily basis, it becomes too time-consuming for radiologists to sift through each patient's clinical documents. It is therefore important to provide a capability that can present contextually relevant clinical documents, and at the same time satisfy the diverse information needs among radiologists from different specialties. In …