283 research outputs found

    Flexpop: A popularity-based caching strategy for multimedia applications in information-centric networking

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    Information-Centric Networking (ICN) is the dominant architecture for the future Internet. In ICN, the content items are stored temporarily in network nodes such as routers. When the memory of routers becomes full and there is no room for a new arriving content, the stored contents are evicted to cope with the limited cache size of the routers. Therefore, it is crucial to develop an effective caching strategy for keeping popular contents for a longer period of time. This study proposes a new caching strategy, named Flexible Popularity-based Caching (FlexPop) for storing popular contents. The FlexPop comprises two mechanisms, i.e., Content Placement Mechanism (CPM), which is responsible for content caching, and Content Eviction Mechanism (CEM) that deals with content eviction when the router cache is full and there is no space for the new incoming content. Both mechanisms are validated using Fuzzy Set Theory, following the Design Research Methodology (DRM) to manifest that the research is rigorous and repeatable under comparable conditions. The performance of FlexPop is evaluated through simulations and the results are compared with those of the Leave Copy Everywhere (LCE), ProbCache, and Most Popular Content (MPC) strategies. The results show that the FlexPop strategy outperforms LCE, ProbCache, and MPC with respect to cache hit rate, redundancy, content retrieval delay, memory utilization, and stretch ratio, which are regarded as extremely important metrics (in various studies) for the evaluation of ICN caching. The outcomes exhibited in this study are noteworthy in terms of making FlexPop acceptable to users as they can verify the performance of ICN before selecting the right caching strategy. Thus FlexPop has potential in the use of ICN for the future Internet such as in deployment of the IoT technology

    Observing and Improving the Reliability of Internet Last-mile Links

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    People rely on having persistent Internet connectivity from their homes and mobile devices. However, unlike links in the core of the Internet, the links that connect people's homes and mobile devices, known as "last-mile" links, are not redundant. As a result, the reliability of any given link is of paramount concern: when last-mile links fail, people can be completely disconnected from the Internet. In addition to lacking redundancy, Internet last-mile links are vulnerable to failure. Such links can fail because the cables and equipment that make up last-mile links are exposed to the elements; for example, weather can cause tree limbs to fall on overhead cables, and flooding can destroy underground equipment. They can also fail, eventually, because cellular last-mile links can drain a smartphone's battery if an application tries to communicate when signal strength is weak. In this dissertation, I defend the following thesis: By building on existing infrastructure, it is possible to (1) observe the reliability of Internet last-mile links across different weather conditions and link types; (2) improve the energy efficiency of cellular Internet last-mile links; and (3) provide an incrementally deployable, energy-efficient Internet last-mile downlink that is highly resilient to weather-related failures. I defend this thesis by designing, implementing, and evaluating systems

    ์ •๋ณด ์ค‘์‹ฌ ๋„คํŠธ์›Œํ‚น์—์„œ์˜ ์ฝ˜ํ…ํŠธ ํƒ์ƒ‰ ๋ฐ ๋ฐ์ดํ„ฐ ์˜คํ”„๋กœ๋”ฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2015. 8. ๊ถŒํƒœ๊ฒฝ.ํ˜„์žฌ์˜ ์ธํ„ฐ๋„ท์€ ์ž์› ๊ณต์œ ๋ฅผ ๋ชฉ์ ์œผ๋กœ ํ˜ธ์ŠคํŠธ๊ฐ„ ํ†ต์‹  ํŒจ๋Ÿฌ๋‹ค์ž„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ์ง€๋งŒ, ์˜ค๋Š˜๋‚  ์ธํ„ฐ๋„ท ์‚ฌ์šฉ ํŒจํ„ด์€ ์ฝ˜ํ…ํŠธ ํš๋“์— ์ง‘์ค‘๋˜์–ด์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ, ๋Œ€๋ถ€๋ถ„์˜ ์ธํ„ฐ๋„ท ํŠธ๋ž˜ํ”ฝ์€ ๋น„๋””์˜ค ์„œ๋น„์Šค๋‚˜ P2P ํŒŒ์ผ ๊ณต์œ ์™€ ๊ฐ™์€ ์ฝ˜ํ…ํŠธ ํš๋“์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ํ•˜์ง€๋งŒ, ํ˜„์žฌ ์ธํ„ฐ๋„ท์˜ ๊ตฌ์กฐ์™€ ์‹ค์ œ ์‚ฌ์šฉ ํŒจํ„ด์˜ ๊ดด๋ฆฌ๋Š” ๋น„ํšจ์œจ์ ์ธ ์ฝ˜ํ…ํŠธ ์ „๋‹ฌ (์˜ˆ, ๋™์ผํ•œ ์ธ๊ธฐ์žˆ๋Š” ์ฝ˜ํ…ํŠธ์— ๋Œ€ํ•œ ์ค‘๋ณต๋œ ์ฝ˜ํ…ํŠธ ์ „์†ก)์„ ์•ผ๊ธฐํ•˜๊ณ  ์žˆ๊ณ , ์ด๋Š” ํŠธ๋ž˜ํ”ฝ ํญ๋ฐœ ๋ฌธ์ œ๋กœ ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์Šˆ๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด (i) ์ธํ„ฐ๋„ท ๊ตฌ์กฐ๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์„ค๊ณ„ํ•˜๊ฑฐ๋‚˜ (ii) ๋ฐ์ดํ„ฐ ์˜คํ”„๋กœ๋”ฉ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ํŠธ๋ž˜ํ”ฝ์„ ์ค„์ด๋ ค๋Š” ์‹œ๋„๋“ค์ด ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์ •๋ณด ์ค‘์‹ฌ ๋„คํŠธ์›Œํ‚น๊ณผ ์ •๋ณด ์ค‘์‹ฌ์˜ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ผ๋Š” ๋‘๊ฐ€์ง€ ์˜์—ญ์—์„œ์˜ ํŠธ๋ž˜ํ”ฝ ๊ฐ์†Œ ๊ธฐ๋ฒ•์— ๋Œ€ํ•ด ํƒ๊ตฌํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ์ •๋ณด ์ค‘์‹ฌ ๋„คํŠธ์›Œํ‚น์„ ์œ„ํ•œ ํŠธ๋ž˜ํ”ฝ ๊ฐ์†Œ ์ฝ˜ํ…ํŠธ ํƒ์ƒ‰ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ •๋ณด ์ค‘์‹ฌ ๋„คํŠธ์›Œํ‚น์€ ํŠธ๋ž˜ํ”ฝ ํญ๋ฐœ๊ณผ ๊ฐ™์€ ํ˜„์žฌ ์ธํ„ฐ๋„ท์˜ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ์ดˆ๊ธฐ ๋‹จ๊ณ„๋ถ€ํ„ฐ ์ธํ„ฐ๋„ท ๊ตฌ์กฐ๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์„ค๊ณ„ํ•˜์ž๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ •๋ณด ์ค‘์‹ฌ ๋„คํŠธ์›Œํ‚น์€ ๊ฐ€๊นŒ์ด์— ์กด์žฌํ•˜๋Š” ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ๋ฅผ ์ด์šฉํ•˜๊ฑฐ๋‚˜ ๋™์ผํ•œ ์ฝ˜ํ…ํŠธ ์ „์†ก์— ๋Œ€ํ•œ ์ค‘๋ณต๋œ ์ „์†ก์„ ์ค„์ด๋Š” ๊ฒƒ์„ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ํŠธ๋ž˜ํ”ฝ ๊ฐ์†Œ์™€ ๊ฐ™์€ ์ด๋“์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด์ „์˜ ์—ฐ๊ตฌ๋“ค์€ ์ด๋Ÿฌํ•œ ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐํšŒ์ฃผ์˜์  ์บ์‹œ ์ผ์น˜ ๋ฐฉ์‹์— ์˜์กด์„ ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์€ ์ฝ˜ํ…ํŠธ ์†Œ์Šค๋กœ ๊ฐ€๋Š” ๊ฒฝ๋กœ์— ์กด์žฌํ•˜๋Š” ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ๋งŒ ์ด์šฉํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋„คํŠธ์›Œํฌ ๊ณณ๊ณณ์— ์žˆ๋Š” ๋„คํŠธ์›Œํฌ ๋‚ด์žฌ ์ €์žฅ ๊ณต๊ฐ„์„ ์ถฉ๋ถ„ํžˆ ์ด์šฉํ•˜์ง€ ๋ชปํ•˜๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์ธ SCAN์€ ๋„คํŠธ์›Œํฌ์— ์‚ฐ์žฌ๋œ ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ๋ฅผ ๋” ์ž˜ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ฐ€๊นŒ์ด์— ์กด์žฌํ•˜๋Š” ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ๋ฅผ ํƒ์ƒ‰ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด SCAN์€ ๋ธ”๋ฃธ ํ•„ํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ฃผ๋ณ€ ๋ผ์šฐํ„ฐ๋“ค ์‚ฌ์ด์—์„œ ์บ์‹œ๋œ ์ฝ˜ํ…ํŠธ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ๊ตํ™˜ํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด SCAN์€ ๊ธฐํšŒ์ฃผ์˜์  ์บ์‹œ ์ผ์น˜ ๋ฐฉ์‹์˜ ๊ธฐ๋ฒ•์— ๋น„ํ•ด ํ‰๊ท  ํ™‰ ๊ฑฐ๋ฆฌ, ํŠธ๋ž˜ํ”ฝ ์–‘, ๋งํฌ๊ฐ„ ๋กœ๋“œ ๋ถ„๋ฐฐ์—์„œ ๋” ๋‚˜์€ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์ •๋ณด ์ค‘์‹ฌ์˜ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ํŠธ๋ž˜ํ”ฝ ์˜คํ”„๋กœ๋”ฉ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ฌด์„  ํ™˜๊ฒฝ์—์„œ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋Š” ๋ชจ๋ฐ”์ผ ํŠธ๋ž˜ํ”ฝ์€ ๋ชจ๋ฐ”์ผ ๋„คํŠธ์›Œํฌ ์ œ๊ณต์ž์—๊ฒŒ ์‹ฌ๊ฐํ•œ ์šฐ๋ ค๊ฐ€ ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠธ๋ž˜ํ”ฝ ํญ๋ฐœ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด, ํŠธ๋ž˜ํ”ฝ์„ ์…€๋ฃฐ๋Ÿฌ ๋„คํŠธ์›Œํฌ์—์„œ WiFi ํ•ซ์ŠคํŒŸ์ด๋‚˜ ํŽจํ† ์…€๊ณผ ๊ฐ™์€ ๋‹ค๋ฅธ ๋„คํŠธ์›Œํฌ๋กœ ์˜คํ”„๋กœ๋”ฉํ•˜๋ ค๋Š” ์—ฐ๊ตฌ๋“ค์ด ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด์˜ ์‹œ๋„์—์„œ ๋” ๋‚˜์•„๊ฐ€์„œ ๋ฐ์ดํ„ฐ ์˜คํ”„๋กœ๋”ฉ์„ ์œ„ํ•œ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ์˜ ์ž ์žฌ์  ์žฅ์ ์— ์ง‘์ค‘ํ•˜์—ฌ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ์ด์šฉํ•œ ๋ฐ์ดํ„ฐ ์˜คํ”„๋กœ๋”ฉ ํ”„๋ ˆ์ž„์›Œํฌ์ธ DOVE๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐ์ดํ„ฐ ์˜คํ”„๋กœ๋”ฉ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ฐจ๋Ÿ‰ ๋‚ด ๋ฐ์ดํ„ฐ ์„œ๋น„์Šค๋ฅผ ์œ„ํ•ด ํ•„์š”ํ•œ ์…€๋ฃฐ๋Ÿฌ ํŠธ๋ž˜ํ”ฝ์„ ๋น„์šฉ ํšจ๊ณผ๊ฐ€ ๋†’์€ ๋ฐฉ์‹์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. DOVE์—์„œ๋Š” ์˜คํ”„๋กœ๋”ฉ์„ ๋ชฉ์ ์œผ๋กœ ์ฐจ๋Ÿ‰ ์ด๋™ ๊ฒฝ๋กœ๋ฅผ ์ด์šฉํ•˜๊ณ , ๊ฒฝ์ œ์ ์ธ ๋น„์šฉ ์ ˆ๊ฐ์„ ๋ชฉ์ ์œผ๋กœ ์ฐจ๋Ÿ‰์—์„œ ์š”์ฒญ๋˜๋Š” ์ฝ˜ํ…ํŠธ ํŒŒ์ผ๋“ค์€ ์…€๋ฃฐ๋Ÿฌ ๋„คํŠธ์›Œํฌ ๋Œ€์‹  ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์ „๋‹ฌ๋œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์˜คํ”„๋กœ๋”ฉ ์œ„์น˜๋ฅผ ์„ ํƒํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์‹œ๊ณต๊ฐ„์  ์ง‘ํ•ฉ ๋ฎ๊ฐœ ๋ฌธ์ œ๋กœ ๋งŒ๋“ค๊ณ , ์ฐจ๋Ÿ‰ ์ด๋™ ๊ฒฝ๋กœ๋ฅผ ์ด์šฉํ•œ ์‹œ๊ฐ„ ์˜ˆ์ธก ๊ธฐ๋ฐ˜์˜ ์ง‘ํ•ฉ ๋ฎ๊ฐœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, DOVE ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•œ ์˜คํ”„๋กœ๋”ฉ์„ ์ˆ˜ํ–‰ํ•˜์—ฌ 57%์˜ ์…€๋ฃฐ๋Ÿฌ ๋งํฌ ์‚ฌ์šฉ๋Ÿ‰์„ ํฌ๊ฒŒ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค.While the architecture of current Internet was designed based on the host-to-host communication paradigm for resource sharing, today's Internet usage has been concentrated on content retrievals. As a result, most of Internet traffic is generated by the content retrievals, such as video service and P2P file sharing. However, the discrepancy between the current Internet architecture and the real usage pattern causes inefficient content deliveries (e.g., duplicated content transmission for the same popular content), which leads to traffic explosion problem. To address such issues, there have been a lot of efforts to reduce the network traffic by (i) redesigning the Internet architecture and (ii) proposing data offloading schemes. In this dissertation, we investigate traffic reduction schemes in two different domains, information-centric networking and information-centric vehicular networks. First, we propose a traffic-reduction content-discovery scheme for information-centric networking (ICN). ICN has been proposed to resolve the problem of current Internet such as traffic explosion by redesigning the Internet architecture in a clean-slate manner. ICN can provide substantial benefits such as network traffic reduction by exploiting a nearby (cached) copy of content and reducing duplicated transmissions for the same content request. However, prior studies usually rely on an opportunistic cache-hit (happen-to-meet) to utilize the cached contents. In the happen-to-meet fashion, only the content cached on the path towards the content source can be utilized, which limits the network-wide usage of the in-network storages. To exploit cached contents better, our proposed scheme SCAN discovers nearby content copies. SCAN exchanges the cached content information among the neighbor routers using Bloom filters for the content discovery. With extensive simulations, SCAN shows better performance than a happen-to-meet cache-hit scheme in terms of average hop counts, traffic volume, and load balancing among links. Next, we propose a traffic offloading scheme for information-centric vehicular network. In wireless environments, the increasing mobile traffic is becoming a serious concern for mobile network providers. To address the traffic explosion problem, there have been a lot of efforts to offload the traffic from cellular networks to other networks, such as WiFi hotspots and femtocells. Our work moves forward from prior studies by focusing on the potential benefits of vehicular networks for data offloading. In particular, we propose a Data Offloading framework using Vehicular nEtworks (DOVE), which reduces the cellular traffic for in-vehicle data services in a cost effective way. DOVE exploits vehicle trajectories for offloading purposes so that content files requested by vehicles can be delivered via vehicular networks rather than cellular networks for economical purposes. We formulate the problem of selecting offloading positions as a spatio-temporal set-covering problem, and propose a time-prediction based set-covering algorithm using vehicle trajectories. Simulation results show that our DOVE framework can significantly reduce 57% of cellular link usage by performing data offloading through vehicular networks.I. Introduction 1 II. Content Discovery for Information-Centric Networking 4 2.1 Introduction 4 2.2 Related Work 7 2.2.1 Named Data Networking (NDN) 7 2.2.2 ICN-based Schemes 8 2.2.3 Approaches using BFs 10 2.3 SCAN Architecture 11 2.3.1 SCAN Description 11 2.3.2 SCAN Operation 16 2.3.3 Discussion 19 2.4 CIB Maintenance in SCAN 21 2.4.1 Information Unit 21 2.4.2 Information Exchange 22 2.4.3 Information Decay 23 2.5 Performance Evaluation 25 2.5.1 Content Discovery Performance 27 2.5.2 Network-wide Performance 28 2.5.3 Effect of Cache Size 30 2.5.4 Effect of Scanning Depth 32 2.5.5 Effect of Information Decay Probability 34 2.5.6 Effect of BF Size 36 2.5.7 Effect of BF Exchange Interval 39 2.5.8 Comparison with ICN-enhancements 39 III. Data Offloading for Information-Centric Vehicular Networks 42 3.1 Introduction 42 3.2 Related Work 44 3.3 Problem Formulation 46 3.3.1 Target Scenario and Goal 46 3.3.2 DOVE Components and Assumptions 46 3.3.3 Design Principles using RNs 49 3.3.4 The Concept of Offloading in DOVE 50 3.4 Design and Operations of DOVE 51 3.4.1 Travel Time Prediction 51 3.4.2 The Operation of TCC 53 3.4.3 The Selection Algorithm for Offloading Positions 54 3.4.4 The Selection of Providers 59 3.4.5 The Operation of Vehicles using Offloading Positions 59 3.5 Performance Evaluation 60 3.5.1 Overall Performance of Data Offloading 61 3.5.2 The Impact of Vehicle Number 67 3.5.3 The Impact of Vehicle Speed 68 3.5.4 The Impact of Waiting Time 70 3.5.5 The Impact of Deployment Ratio and Tolerance Time 71 IV. Conclusion 74 Bibliography 76 Korean Abstract 82Docto

    Decoupling Information and Connectivity via Information-Centric Transport

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    The power of Information-Centric Networking (ICN) architectures lies in their abstraction for communication --- the request for named data. This abstraction promises that applications can choose to operate only in the information plane, agnostic to the mechanisms implemented in the connectivity plane. However, despite this powerful promise, the information and connectivity planes are presently coupled in today\u27s incarnations of leading ICNs by a core architectural component, the forwarding strategy. Presently, this component is not sustainable: it implements both the information and connectivity mechanisms without specifying who should choose a forwarding strategy --- an application developer or the network operator. In practice, application developers can specify a strategy only if they understand connectivity details, while network operators can assign strategies only if they understand application expectations. In this paper, we define the role of forwarding strategies, and we introduce Information-Centric Transport (ICT) as an abstraction for cleanly decoupling the information plane from the connectivity plane. We discuss how ICTs allow applications to operate in the information plane, concerned only with namespaces and trust identities, leaving network node operators free to deploy whatever strategy mechanisms make sense for the connectivity that they manage. To illustrate the ICT concept, we demonstrate ICT-Sync and ICT-Notify. We show how these ICTs 1) enable applications to operate regardless of connectivity details, 2) are designed to satisfy a predefined set of application requirements and are free from application-specifics, and 3) can be deployed by network operators where needed, without requiring any change to the application logic

    TOWARD AUTOMATED THREAT MODELING BY ADVERSARY NETWORK INFRASTRUCTURE DISCOVERY

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    Threat modeling can help defenders ascertain potential attacker capabilities and resources, allowing better protection of critical networks and systems from sophisticated cyber-attacks. One aspect of the adversary profile that is of interest to defenders is the means to conduct a cyber-attack, including malware capabilities and network infrastructure. Even though most defenders collect data on cyber incidents, extracting knowledge about adversaries to build and improve the threat model can be time-consuming. This thesis applies machine learning methods to historical cyber incident data to enable automated threat modeling of adversary network infrastructure. Using network data of attacker command and control servers based on real-world cyber incidents, specific adversary datasets can be created and enriched using the capabilities of internet-scanning search engines. Mixing these datasets with data from benign or non-associated hosts with similar port-service mappings allows for building an interpretable machine learning model of attackers. Additionally, creating internet-scanning search engine queries based on machine learning model predictions allows for automating threat modeling of adversary infrastructure. Automated threat modeling of adversary network infrastructure allows searching for unknown or emerging threat actor network infrastructure on the Internet.Major, Ukrainian Ground ForcesApproved for public release. Distribution is unlimited

    Modeling, Predicting and Capturing Human Mobility

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    Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility
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