Multi-modal Fusion for Flasher Detection in a Mobile Video Chat Application

ABSTRACT This paper investigates the development of accurate and efficient classifiers to identify misbehaving users (i.e., "flashers") in a mobile video chat application. Our analysis is based on video session data collected from a mobile client that we built that connects to a popular random video chat service. We show that prior imagebased classifiers designed for identifying normal and misbehaving users in online video chat systems perform poorly on mobile video chat data. We present an enhanced image-based classifier that improves classification performance on mobile data. More importantly, we demonstrate that incorporating multi-modal mobile sensor data from accelerometer and the camera state (front/back) along with audio can significantly improve the overall image-based classification accuracy. Our work also shows that leveraging multiple image-based predictions within a session (i.e., temporal modality) has the potential to further improve the classification performance. Finally, we show that the cost of classification in terms of running time can be significantly reduced by employing a multilevel cascaded classifier in which high-complexity features and further image-based predictions are not generated unless needed

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Sarp Net: A Secure, Anonymous, Reputation-Based, Peer-To-Peer Network

Since the advent of Napster, the idea of peer-to-peer (P2P) architectures being applied to file-sharing applications has become popular, spawning other P2P networks like Gnutella, Morpheus, Kazaa, and BitTorrent. This growth in P2P development has nearly eradicated the idea of the traditional client-server structure in the file-sharing model, now placing emphasizes on faster query processing, deeper levels of decentralism, and methods to protect against copyright law violation. SARP Net is a secure, anonymous, decentralized, P2P overlay network that is designed to protect the activity of its users in its own file-sharing community. It is secure in the fact that public-key encryption is used to guard eavesdroppers during messages. The protocol guarantees user anonymity by incorporating message hopping from node to node to prevent any network observer from pinpointing the origin of any file query or shared-file source. To further enhance the system\u27s security, a reputation scheme is incorporated to police nodes from malicious activity, maintain the overlay\u27s topology, and enforce rules to protect node identity

Social media as a data gathering tool for international business qualitative research: opportunities and challenges

Lusophone African (LA) multinational enterprises (MNEs) are becoming a significant pan-African and global economic force regarding their international presence and influence. However, given the extreme poverty and lack of development in their home markets, many LA enterprises seeking to internationalize lack resources and legitimacy in international markets. Compared to higher income emerging markets, Lusophone enterprises in Africa face more significant challenges in their internationalization efforts. Concomitantly, conducting significant international business (IB) research in these markets to understand these MNEs internationalization strategies can be a very daunting task. The fast-growing rise of social media on the Internet, however, provides an opportunity for IB researchers to examine new phenomena in these markets in innovative ways. Unfortunately, for various reasons, qualitative researchers in IB have not fully embraced this opportunity. This article studies the use of social media in qualitative research in the field of IB. It offers an illustrative case based on qualitative research on internationalization modes of LAMNEs conducted by the authors in Angola and Mozambique using social media to identify and qualify the population sample, as well as interact with subjects and collect data. It discusses some of the challenges of using social media in those regions of Africa and suggests how scholars can design their studies to capitalize on social media and corresponding data as a tool for qualitative research. This article underscores the potential opportunities and challenges inherent in the use of social media in IB-oriented qualitative research, providing recommendations on how qualitative IB researchers can design their studies to capitalize on data generated by social media.https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406https://doi.org/10.1080/15475778.2019.1634406Accepted manuscriptPublished versio

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Towards trustworthy social computing systems

The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system. In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende Popularität sozialer Medien führt zu umfangreichen Missbrauch mit negativen Folgen für die nachhaltige Funktionalität und verringerter Qualität des Services. Der Missbrauch wird maßgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswürdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefälschte (Sybil) Identitäten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der Identitäten zu erhalten und den Service zu missbrauchen. Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren Identitäten einschränkt. (2) Unter Ausnutzung des Aktivitätsverlaufs von Identitäten: Wir präsentieren zwei Ansätze, einen anwendbar für die Erkennung von Unregelmäßigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanständiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil Identitäten ist. Beide gezeigten Ansätze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen

Protectbot: A Chatbot to Protect Children on Gaming Platforms

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats

Implementation and Evaluation of A Low-Cost Intrusion Detection System For Community Wireless Mesh Networks

Rural Community Wireless Mesh Networks (WMN) can be great assets to rural communities, helping them connect to the rest of their region and beyond. However, they can be a liability in terms of security. Due to the ad-hoc nature of a WMN, and the wide variety of applications and systems that can be found in such a heterogeneous environment there are multiple points of intrusion for an attacker. An unsecured WMN can lead to privacy and legal problems for the users of the network. Due to the resource constrained environment, traditional Intrusion Detection Systems (IDS) have not been as successful in defending these wireless network environments, as they were in wired network deployments. This thesis proposes that an IDS made up of low cost, low power devices can be an acceptable base for a Wireless Mesh Network Intrusion Detection System. Because of the device's low power, cost and ease of use, such a device could be easily deployed and maintained in a rural setting such as a Community WMN. The proposed system was compared to a standard IDS solution that would not cover the entire network, but had much more computing power but also a higher capital cost as well as maintenance costs. By comparing the low cost low power IDS to a standard deployment of an open source IDS, based on network coverage and deployment costs, a determination can be made that a low power solution can be feasible in a rural deployment of a WMN

Scalable and Timely Detection of Cyberbullying in Online Social Networks

The exponential growth of popularity of online social networks in the last decade has unfortunately paved the way for the threat of cyberbullying to rise to an unprecedented level. So a research that provides insights into the analysis of cyberbullying incidents and building a system that is highly scalable and responsive is of unparalleled need. This dissertation gathers insights into cyberbullying incidents in video and image-based social networks (Vine and Instagram respectively) and then presents a system solution that makes use of the gained insights to improve efficiency and efficacy of cyberbullying detection. First, it presents detailed analyses of cyberbullying incidents in the Vine social network by collecting data and labeling them by CrowdFlower. Second, it performs a thorough investigation of the differentiating factors of cyberbullying in online social networks. Third, it implements a highly scalable and responsive system solution for cyberbullying detection along with a comprehensive evaluation of its performances in terms of timeliness and scalability against a highly popular online social network. Fourth, it outlines design, implementation and preliminary user experience analysis of an android application, BullyAlert, that was developed to enable guardians to get adaptive notifications for cyberbullying based on their individual subjective tolerance levels. Finally, it shows that using textual and video feature greatly improves cyberbullying detection classifier's performances