Techniques for assisting users in making security decisions
Başlık çevirisi mevcut değil.
- Tez No: 598727
- Danışmanlar: DR. ENGİN KİRDA
- Tez Türü: Doktora
- Konular: Bilgi ve Belge Yönetimi, Bilim ve Teknoloji, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Information and Records Management, Science and Technology, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2017
- Dil: İngilizce
- Üniversite: Northeastern Unıversıty
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
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Özet (Çeviri)
We are witnessing an arms race between attackers and security experts in today's Internet. Attackers hide their intentions and mimic legitimate behaviour to evade detection. Prominent attacks target endusers' systems with a wide range of goals, such as monetary, financial, political, espionage, destructive. . In this thesis, I examined two well-known instances of these attacks. One of these attacks is the widespread use of trick banners that use social engineering techniques to lure victims into clicking on deceptive fake links and potentially leading to a malicious domain or malware. Other examined Trick banner is an Internet advertising banner with a deceptive visual appearance, crafted to lure users into clicking on them. approaches involve e-mail attacks, such as spearphishing and e-mail attachment attacks. By impersonating trusted e-mail senders through carefully crafted messages and spoofed metadata, adversaries can trick victims into launching attachments containing malicious code or into clicking on malicious links that grant attackers a foothold into otherwise well-protected networks. Unfortunately, current mitigations are unreliable and relying on fallible malware detection techniques or user education. In Spearphishing attacks the adversary crafts e-mail messages that are custom-tailored to the victim and thus appear legitimate. Our hypothesis is that online systems can be designed with optimized settings to help users to make security decisions efficiently. Thus, in this dissertation, I make several contributions to help endusers to make decisions on security: • This dissertation shows how to distinguish trick banners from legitimate download links. I present a set of features to characterize trick banners based on their visual properties such as image size, color, placement on the enclosing web page, whether they contain animation effects, and whether they consistently appear with the same visual properties on consecutive loads of the same web page. I have implemented a tool called TrueClick, which uses image processing and machine learning techniques to build a classifier based on five identified features to detect the trick banners on a web page automatically. • This dissertation shows how to identify a legitimate e-mail sender from a spearphishing e-mail attack. I present a novel automated approach to defend users against spearphishing attacks. The approach first builds probabilistic models of both e-mail metadata and stylometric features of e-mail content. Then, subsequent emails are compared to these models to detect characteristic indicators of spearphishing attacks. • This dissertation aids the end users in making an informed decision about whether or not an e-mail attachment is what they expect. I present adopting a default policy of isolated attachment rendering. E-mails bearing attachments are transparently rewritten to contain static renderings of the attachments within a sandboxed virtual machine environment.
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