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    ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์ž ์žฌ ๋„คํŠธ์›Œํฌ ๋งˆ์ด๋‹

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2023. 2. ๊ถŒํƒœ๊ฒฝ.์›น ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์˜ ํญ๋ฐœ์ ์ธ ๋ฐœ๋‹ฌ๋กœ ์‚ฌ์šฉ์ž๋“ค์€ ์˜จ๋ผ์ธ ์ƒ์—์„œ ํญ๋„“๊ฒŒ ์—ฐ๊ฒฐ๋˜๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ ์ƒ์—์„œ, ์‚ฌ์šฉ์ž๋“ค์€ ์„œ๋กœ์—๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›์œผ๋ฉฐ ์˜์‚ฌ ๊ฒฐ์ •์— ๊ทธ๋“ค์˜ ๊ฒฝํ—˜๊ณผ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€ํ‘œ์ ์ธ ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์ธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์€ ์‚ฌ์šฉ์ž์™€ ํ”Œ๋žซํผ ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ์šฉ์ž์˜ ๊ตฌ๋งค๋Š” ์‚ฌ์šฉ์ž์™€ ์ƒํ’ˆ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ, ์‚ฌ์šฉ์ž์˜ ์ฒดํฌ์ธ์€ ์‚ฌ์šฉ์ž์™€ ์žฅ์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ๋‚˜ํƒ€๋‚ด์ง„๋‹ค. ์—ฌ๊ธฐ์— ํ–‰๋™์˜ ์‹œ๊ฐ„๊ณผ ๋ ˆ์ดํŒ…, ํƒœ๊ทธ ๋“ฑ์˜ ์ •๋ณด๊ฐ€ ํฌํ•จ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ํ”Œ๋žซํผ์—์„œ ์ •์˜๋œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ๊ทธ๋ž˜ํ”„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ž ์žฌ ๋„คํŠธ์›Œํฌ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜์˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์˜ ๊ฒฝ์šฐ ํŠน์ • ์žฅ์†Œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ์ฒดํฌ์ธ ํ˜•์‹์œผ๋กœ ๋งŽ์€ ํฌ์ŠคํŠธ๊ฐ€ ๋งŒ๋“ค์–ด์ง€๋Š”๋ฐ, ์‚ฌ์šฉ์ž์˜ ์žฅ์†Œ ๋ฐฉ๋ฌธ์€ ์‚ฌ์šฉ์ž ๊ฐ„์— ์‚ฌ์ „์— ์กด์žฌํ•˜๋Š” ์นœ๊ตฌ ๊ด€๊ณ„์— ์˜ํ•ด ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฐ›๋Š”๋‹ค. ์‚ฌ์šฉ์ž ํ™œ๋™ ๋„คํŠธ์›Œํฌ์˜ ์ €๋ณ€์— ์ž ์žฌ๋œ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์€ ํ™œ๋™ ์˜ˆ์ธก์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ํ™œ๋™ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด์— ์—ฐ๊ตฌ๋˜์—ˆ๋˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋‘ ์‚ฌ์šฉ์ž๊ฐ€ ๋™์‹œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ํ–‰์œ„์ธ co-visitation์„ ์ค‘์ ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๊ฑฐ๋‚˜, ๋„คํŠธ์›Œํฌ ์ž„๋ฒ ๋”ฉ ๋˜๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œํ˜„ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ฃผ๊ธฐ์ ์ธ ๋ฐฉ๋ฌธ์ด๋‚˜ ์žฅ๊ฑฐ๋ฆฌ ์ด๋™ ๋“ฑ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ํŒจํ„ด์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ํ–‰๋™ ํŒจํ„ด์„ ๋” ์ž˜ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด, ANES๋Š” ์‚ฌ์šฉ์ž ์ปจํ…์ŠคํŠธ ๋‚ด์—์„œ ์‚ฌ์šฉ์ž์™€ ๊ด€์‹ฌ ์ง€์ (POI) ๊ฐ„์˜ ์ธก๋ฉด(Aspect) ์ง€ํ–ฅ ๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•œ๋‹ค. ANES๋Š” User-POI ์ด๋ถ„ ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™์„ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ธก๋ฉด์œผ๋กœ ๋‚˜๋ˆ„๊ณ , ๊ฐ๊ฐ์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ–‰๋™ ํŒจํ„ด์„ ์ถ”์ถœํ•˜๋Š” ์ตœ์ดˆ์˜ ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ์‹ค์ œ LBSN ๋ฐ์ดํ„ฐ์—์„œ ์ˆ˜ํ–‰๋œ ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์—์„œ, ANES๋Š” ๊ธฐ์กด์— ์ œ์•ˆ๋˜์—ˆ๋˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ด์ปค๋จธ์Šค์˜ ๋ฆฌ๋ทฐ ์‹œ์Šคํ…œ์—์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ๋Šฅ๋™์ ์ธ ํŒ”๋กœ์šฐ/ํŒ”๋กœ์ž‰ ๋“ฑ์˜ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ ๋„ ํ”Œ๋žซํผ์— ์˜ํ•ด ์„œ๋กœ์˜ ์ •๋ณด๋ฅผ ์ฃผ๊ณ ๋ฐ›๊ณ  ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‚ฌ์šฉ์ž๋“ค์˜ ํ–‰๋™ ํŠน์„ฑ์€ ๋ฆฌ๋ทฐ ์ŠคํŒธ์— ์˜ํ•ด ์‰ฝ๊ฒŒ ์•…์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฆฌ๋ทฐ ์ŠคํŒธ์€ ์‹ค์ œ ์‚ฌ์šฉ์ž์˜ ์˜๊ฒฌ์„ ์ˆจ๊ธฐ๊ณ  ํ‰์ ์„ ์กฐ์ž‘ํ•˜์—ฌ ์ž˜๋ชป๋œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋‚˜๋Š” ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ์—์„œ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌ์ „ ๊ณต๋ชจ์„ฑ(Collusiveness)์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ฐพ๊ณ , ์ด๋ฅผ ์ŠคํŒธ ํƒ์ง€์— ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•์ธ SC-Com์„ ์ œ์•ˆํ•œ๋‹ค. SC-Com์€ ํ–‰๋™์˜ ๊ณต๋ชจ์„ฑ์œผ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ๊ณต๋ชจ ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ํ•ด๋‹น ์ ์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ฒด ์‚ฌ์šฉ์ž๋ฅผ ์œ ์‚ฌํ•œ ์‚ฌ์šฉ์ž๋“ค์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ๋กœ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ๊ทธ ํ›„ ์ŠคํŒธ ์œ ์ €์™€ ์ผ๋ฐ˜ ์œ ์ €๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ์— ์ค‘์š”ํ•œ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜์˜ ํŠน์ง•์„ ์ถ”์ถœํ•˜์—ฌ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋ถ„๋ฅ˜๊ธฐ์˜ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. SC-Com์€ ๊ณต๋ชจ์„ฑ์„ ๊ฐ–๋Š” ์ŠคํŒธ ์œ ์ €์˜ ์ง‘ํ•ฉ์„ ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ง€ํ•œ๋‹ค. ์‹ค์ œ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•œ ์‹คํ—˜์—์„œ, SC-Com์€ ๊ธฐ์กด ๋…ผ๋ฌธ๋“ค ๋Œ€๋น„ ์ŠคํŒธ ํƒ์ง€์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์œ„ ๋…ผ๋ฌธ์—์„œ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๋œ ์•”์‹œ์  ์—ฐ๊ฒฐ๋ง ํƒ์ง€ ๋ชจ๋ธ์€ ๋ ˆ์ด๋ธ”์ด ์—†๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ๋„ ์‚ฌ์ „์— ์—ฐ๊ฒฐ๋˜์—ˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์‚ฌ์šฉ์ž๋“ค์„ ์˜ˆ์ธกํ•˜๋ฏ€๋กœ, ์‹ค์‹œ๊ฐ„ ์œ„์น˜ ๋ฐ์ดํ„ฐ๋‚˜, ์•ฑ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์—ฌ ๊ด‘๊ณ  ์ถ”์ฒœ ์‹œ์Šคํ…œ์ด๋‚˜, ์•…์„ฑ ์œ ์ € ํƒ์ง€ ๋“ฑ์˜ ๋ถ„์•ผ์—์„œ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63๋ฐ•

    Combating User Misbehavior on Social Media

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    Social media encourages user participation and facilitates userโ€™s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media โ€” spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

    Predicting Fraud Apps Using Hybrid Learning Approach: A Survey

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    Each individual in the planet are mobile phone users in fact smart-phone users with android applications. So, due to this attractiveness and well-known concept there will be a hasty growth in mobile technology. And in addition in information mining, mining the required information from a fastidious application is exceptionally troublesome. Consolidating these two ideas of ranking frauds in android market and taking out required information is gone exceptionally tough.The mobile phone Apps has developed at massive speed in some years; as for march 2017, there are nearby 2.8 million Apps at google play and 2.2 Apps at Google Apps store. In addition, there are over 400,000 self-governing app developers all fighting for the attention of the same potential clients. The Google App Store saw 128,000 new business apps alone in 2014 and the mobile gaming category alone has contest to the tune of almost 300,000 apps. Here the major need to make fraud search in Apps is by searching the high ranked applications up to 30-40 which may be ranked high in some time or the applications which are in those high ranked lists should be confirmed but this is not applied for thousands of applications added per day. So, go for wide examination by applying some procedure to every application to judge its ranking. Discovery of ranking fraud for mobile phone applications, require a flawless, fraud less and result that show correct application accordingly provide ranking; where really make it occur by searching fraud of applications. They create fraud of App by ranked high the App by methods using such human water armies and bot farms; where they create fraud by downloading application through different devices and provide fake ratings and reviews. So, extract critical data connecting particular application such as review which was called comments and lots of other information, to mine and place algorithm to identify fakeness in application rank

    Security Enhancements in Voice Over Ip Networks

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    Voice delivery over IP networks including VoIP (Voice over IP) and VoLTE (Voice over LTE) are emerging as the alternatives to the conventional public telephony networks. With the growing number of subscribers and the global integration of 4/5G by operations, VoIP/VoLTE as the only option for voice delivery becomes an attractive target to be abused and exploited by malicious attackers. This dissertation aims to address some of the security challenges in VoIP/VoLTE. When we examine the past events to identify trends and changes in attacking strategies, we find that spam calls, caller-ID spoofing, and DoS attacks are the most imminent threats to VoIP deployments. Compared to email spam, voice spam will be much more obnoxious and time consuming nuisance for human subscribers to filter out. Since the threat of voice spam could become as serious as email spam, we first focus on spam detection and propose a content-based approach to protect telephone subscribers\u27 voice mailboxes from voice spam. Caller-ID has long been used to enable the callee parties know who is calling, verify his identity for authentication and his physical location for emergency services. VoIP and other packet switched networks such as all-IP Long Term Evolution (LTE) network provide flexibility that helps subscribers to use arbitrary caller-ID. Moreover, interconnecting between IP telephony and other Circuit-Switched (CS) legacy telephone networks has also weakened the security of caller-ID systems. We observe that the determination of true identity of a calling device helps us in preventing many VoIP attacks, such as caller-ID spoofing, spamming and call flooding attacks. This motivates us to take a very different approach to the VoIP problems and attempt to answer a fundamental question: is it possible to know the type of a device a subscriber uses to originate a call? By exploiting the impreciseness of the codec sampling rate in the caller\u27s RTP streams, we propose a fuzzy rule-based system to remotely identify calling devices. Finally, we propose a caller-ID based public key infrastructure for VoIP and VoLTE that provides signature generation at the calling party side as well as signature verification at the callee party side. The proposed signature can be used as caller-ID trust to prevent caller-ID spoofing and unsolicited calls. Our approach is based on the identity-based cryptography, and it also leverages the Domain Name System (DNS) and proxy servers in the VoIP architecture, as well as the Home Subscriber Server (HSS) and Call Session Control Function (CSCF) in the IP Multimedia Subsystem (IMS) architecture. Using OPNET, we then develop a comprehensive simulation testbed for the evaluation of our proposed infrastructure. Our simulation results show that the average call setup delays induced by our infrastructure are hardly noticeable by telephony subscribers and the extra signaling overhead is negligible. Therefore, our proposed infrastructure can be adopted to widely verify caller-ID in telephony networks

    Gamification of community policing : SpamCombat

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    The purpose of this paper is two-fold. First, it seeks to introduce the conceptual prototype of SpamCombat, a Web application that helps combat spam through gamification of community policing. Second, it attempts to evaluate SpamCombat by identifying factors that can potentially drive usersโ€™ behavioral intention to adopt. A questionnaire seeking quantitative and qualitative responses was administered to 120 participants. The results indicate that behavioral intention to adopt SpamCombat is generally promising. Most participants appreciated the novelty of SpamCombat in supporting community policing to promote a spam-free cyber space. However, participants felt that using SpamCombat could be time-consuming

    Combating User Misbehavior on Social Media

    Get PDF
    Social media encourages user participation and facilitates userโ€™s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media โ€” spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

    Trust and Credibility in Online Social Networks

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    Increasing portions of people's social and communicative activities now take place in the digital world. The growth and popularity of online social networks (OSNs) have tremendously facilitated online interaction and information exchange. As OSNs enable people to communicate more effectively, a large volume of user-generated content (UGC) is produced daily. As UGC contains valuable information, more people now turn to OSNs for news, opinions, and social networking. Besides users, companies and business owners also benefit from UGC as they utilize OSNs as the platforms for communicating with customers and marketing activities. Hence, UGC has a powerful impact on users' opinions and decisions. However, the openness of OSNs also brings concerns about trust and credibility online. The freedom and ease of publishing information online could lead to UGC with problematic quality. It has been observed that professional spammers are hired to insert deceptive content and promote harmful information in OSNs. It is known as the spamming problem, which jeopardizes the ecosystems of OSNs. The severity of the spamming problem has attracted the attention of researchers and many detection approaches have been proposed. However, most existing approaches are based on behavioral patterns. As spammers evolve to evade being detected by faking normal behaviors, these detection approaches may fail. In this dissertation, we present our work of detecting spammers by extracting behavioral patterns that are difficult to be manipulated in OSNs. We focus on two scenarios, review spamming and social bots. We first identify that the rating deviations and opinion deviations are invariant patterns in review spamming activities since the goal of review spamming is to insert deceptive reviews. We utilize the two kinds of deviations as clues for trust propagation and propose our detection mechanisms. For social bots detection, we identify the behavioral patterns among users in a neighborhood is difficult to be manipulated for a social bot and propose a neighborhood-based detection scheme. Our work shows that the trustworthiness of a user can be reflected in social relations and opinions expressed in the review content. Besides, our proposed features extracted from the neighborhood are useful for social bot detection

    The Dynamics of Multi-Modal Networks

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    The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains
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