Directed Research Project

Detecting Malicious Usage of Online Social Network APIs from Network Flows

While online social networks (OSNs) provided Application Programming Interfaces (APIs) to enable the development of OSN applications, some of these applications, unfortunately, can be malicious. They can be running on the devices for OSN users throughout the Internet, causing security, privacy, and liability concerns to the network service providers of these OSN users. In this paper, we study how a network service provider may inspect its network traffic to detect network flows from malicious OSN applications.

FR-WARD: Fast Retransmit as a Wary but Ample Response to Distributed Denial-of-Service Attacks from the Internet of Things

While the Internet of Things (IoT) becomes increasingly popular and ubiquitous, IoT devices often remain unprotected and can be exploited to launch large-scale distributed denial-of-service (DDoS) attacks. One could attempt to employ traditional DDoS defense solutions, but these solutions are hardly suitable in IoT environments since they seldom consider the resource constraints of IoT devices.

 Online, Victim-driven Generation of DDoS-Filtering Rules

Distributed Denial-of-Service (DDoS) attacks continue to pose a significant threat to the availability of Internet services, which are increasingly poorly equipped to face the growing scale and frequency of such attacks. Moreover, since attackers continue to discover and quickly exploit new attack vectors, the variety of DDoS attack types continues to grow, posing yet another obstacle to those seeking to defend against these attacks.

Directive-Based Programming for High-Performance FPGA Computing

Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations from several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing (HPC), primarily due to their programming complexity and difficulties in optimizing performance. In this Directed Research Project, we present a directive-based, high-level optimization framework for HPC with FPGAs, which is built on top of an OpenACC-to-FPGA translation framework called OpenARC.

Ontology-based Data Integration and Modeling

The enormous amount of data currently available on the World Wide Web, the deep-web (online databases), the emerging Semantic Web, and knowledge bases presents the challenging task of effectively integrating these sources of information. Even more difficult is ensuring that differences in semantics between each resource are properly preserved, if not exploited. This problem is known in both the Database Systems and Artificial Intelligence community as information integration.

Characterizing Peer-level Performance of BitTorrent

BitTorrent is one of the most popular Peer-to-Peer (P2P) content distribution applications over the Internet that significantly contributes in network traffic. In BitTorrent, a file is divided into segments and participating peers contribute their outgoing bandwidth by providing their available segments to other peers while obtaining their missing peers from others. Characterization of BitTorrent is useful in determining its performance bottlenecks as well as its impact on the network.

Characterizing Peer-level Performance of BitTorrent

Abstract

BitTorrent is one of the most popular Peer-to-Peer (P2P) content distribution applications over the Internet that significantly contributes in network traffic. In BitTorrent, a file is divided into segments and participating peers contribute their outgoing bandwidth by providing their available segments to other peers while obtaining their missing peers from others. Characterization of BitTorrent is useful in determining its performance bottlenecks as well as its impact on the network.

A semi-automatic framework for mining ERP patterns

Event-related potentials (ERP) are created by averaging across segments of Electroencephalogram(EEG) data in different trials and time-locking to stimulus events or response. ERP data is a mixture of artifacts, noise, and components that are related to brain operations. In this paper, we propose a semi-automatic framework for mining ERP patterns. These patterns will be used as pattern elements for Neural ElectroMagnetic Ontology (NEMO) development.

Securing the Smart Home via a Two-Mode Security Framework

The growth of Internet of Things (IoT) faces the growing concern of cyber-attacks toward IoT. Unfortunately, the constrained resources of IoT devices and their networks make many traditional attack detection methods less effective or even not applicable. In this paper, we present a framework for a smart home environment that considers the unique properties of IoT networks. This framework utilizes a two-mode adaptive security model to enable users to balance performance, security, and overhead.

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