Person: GÜRSUN, Gonca
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Gonca
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GÜRSUN
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ArticlePublication Metadata only On characterizing sectoral interactions via connections between employees in professional online social networks(Elsevier, 2018-12) Ayvaz, Demet; Gürsun, Gonca; Özlale, Ümit; Economics; Computer Science; GÜRSUN, Gonca; ÖZLALE, Ümit; Ayvaz, DemetThe collaboration among individuals is essential to maximize economic efficiency. Today most of the technological and economical advancements require multidisciplinary efforts. Therefore promoting interaction and knowledge sharing between industry sectors within a country is more crucial than ever. One main platform for such communication is business-oriented online social networks where thousands of professionals from various sectors connect with each other. These social networks provide a way of disseminating the latest information in technology and business. Our goal in this paper is to analyze the connectivity patterns of individuals in a business-oriented social network as a tool to understand how industry sectors are represented and interact with each other in such online platforms. To do that, we collect profiles of thousands of employees from a professional online social network. Then, first, we analyze the structural properties of the network and report its characteristics in comparison with the non-professional ones. Second, we map each employee to the sector she works in and study the connectivity patterns within each sector separately. We find that the connectivity patterns within sectors vary and the employees within a sector do not necessarily form densely connected communities. Third, we investigate the relationship between sectors via the connectivity of their employees and identify the main social clusters of sectors. We show that there are significant similarities between social connectivity and the economic transactions between sectors.ArticlePublication Metadata only Routing-aware partitioning of the internet address space for server ranking in CDNs(Elsevier, 2017-07) Gürsun, Gonca; Computer Science; GÜRSUN, GoncaThe goal of Content Delivery Networks (CDNs) is to serve content to end-users with high performance. In order to do that, a CDN measures the latency on the paths from its servers to users and then selects a best available server for each user. For large CDNs, monitoring paths from thousands of servers to millions of users is a challenging task due to its size. In this paper, we address this problem and propose a framework to scale the task of path monitoring. Simply stated, the goal of our framework is clustering IP addresses (clients) such that in each cluster the choice of best available server is same (or similar). Then, finding a best available server for one client in a given cluster will be sufficient to assign that server to the rest of the clients in the cluster. To achieve this goal, first we introduce two distance metrics to compute how similar the server choices of any given two clients. Second, we use a clustering method that is based on interdomain routing information. We evaluate the goodness of our clusters by using the metrics we introduce. We show that there is a strong correlation between the similarity in how two destination clients are routed to in the Internet and the similarity in their server selections. Finally, we show how to choose representative clients from each cluster so that it is sufficient to learn the latencies from the CDN servers to the representative and find a best available server accordingly for the rest of the clients in the same cluster.Conference ObjectPublication Metadata only On context-aware DDoS attacks using deep generative networks(IEEE, 2018-10) Gürsun, Gonca; Şensoy, Murat; Kandemir, Melih; Computer Science; GÜRSUN, Gonca; ŞENSOY, Murat; KANDEMİR, MalihDistributed Denial of Service (DDoS) attacks continue to be one of the most severe threats in the Internet. The intrinsic challenge in preventing DDoS attacks is to distinguish them from legitimate flash crowds since two have many traffic characteristics in common. Today most DDoS detection techniques focus on finding parametric differences between the patterns in attack and legitimate traffic. However, such techniques are very sensitive to the threshold values set on the parameters and more importantly legitimate traffic features might be mimicked by smart attackers to generate requests that look like flash crowds. In this paper, we propose a framework for training networks for such smart attacks. Our framework is based on Deep Generative Network models and our contributions are two-fold.We first show that legitimate traffic features can be mimicked without explicitly modeling their distributions. Second, we introduce the concept of context-aware DDoS attacks. We show that an attacker can generate traffic that looks similar to flash crowds to be undetected for long periods of time. However, the ability of generating such attacks is constrained by the budget of the attacker. A context-aware attacker is the one that can intelligently use its budget to maximize the damage in the victim network. Our study provides a framework for training networks for such DDoS attack scenarios.ArticlePublication Open Access On spectral analysis of the Internet delay space and detecting anomalous routing paths(TÜBİTAK, 2019) Gürsun, Gonca; Computer Science; GÜRSUN, GoncaLatency is one of the most critical performance metrics for a wide range of applications. Therefore, it is important to understand the underlying mechanisms that give rise to the observed latency values and diagnose the ones that are unexpectedly high. In this paper, we study the Internet delay space via robust principal component analysis (RPCA). Using RPCA, we show that the delay space, i.e. the matrix of measured round trip times between end hosts, can be decomposed into two components: the estimated latency between end hosts with respect to the current state of the Internet and the inflation on the paths between the end hosts. Using this decomposition, first we study the wellknown low-dimensionality phenomena of the delay space and ask what properties of the end hosts define the dimensions. Second, using the decomposition, we develop a filtering method to detect the paths that experience unexpected latencies and identify routing anomalies. We show that our filter successfully identifies an anomalous route even when its observed latency is not obviously high in magnitude.