Using Botnet Technologies to Counteract Network Traffic Analysis

Botnets have been problematic for over a decade. They are used to launch malicious activities including DDoS (Distributed-Denial-of-Service), spamming, identity theft, unauthorized bitcoin mining and malware distribution. A recent nation-wide DDoS attacks caused by the Mirai botnet on 10/21/2016 involving 10s of millions of IP addresses took down Twitter, Spotify, Reddit, The New York Times, Pinterest, PayPal and other major websites. In response to take-down campaigns by security personnel, botmasters have developed technologies to evade detection. The most widely used evasion technique is DNS fast-flux, where the botmaster frequently changes the mapping between domain names and IP addresses of the C&C server so that it will be too late or too costly to trace the C&C server locations. Domain names generated with Domain Generation Algorithms (DGAs) are used as the \u27rendezvous\u27 points between botmasters and bots. This work focuses on how to apply botnet technologies (fast-flux and DGA) to counteract network traffic analysis, therefore protecting user privacy. A better understanding of botnet technologies also helps us be pro-active in defending against botnets. First, we proposed two new DGAs using hidden Markov models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) which can evade current detection methods and systems. Also, we developed two HMM-based DGA detection methods that can detect the botnet DGA-generated domain names with/without training sets. This helps security personnel understand the botnet phenomenon and develop pro-active tools to detect botnets. Second, we developed a distributed proxy system using fast-flux to evade national censorship and surveillance. The goal is to help journalists, human right advocates and NGOs in West Africa to have a secure and free Internet. Then we developed a covert data transport protocol to transform arbitrary message into real DNS traffic. We encode the message into benign-looking domain names generated by an HMM, which represents the statistical features of legitimate domain names. This can be used to evade Deep Packet Inspection (DPI) and protect user privacy in a two-way communication. Both applications serve as examples of applying botnet technologies to legitimate use. Finally, we proposed a new protocol obfuscation technique by transforming arbitrary network protocol into another (Network Time Protocol and a video game protocol of Minecraft as examples) in terms of packet syntax and side-channel features (inter-packet delay and packet size). This research uses botnet technologies to help normal users have secure and private communications over the Internet. From our botnet research, we conclude that network traffic is a malleable and artificial construct. Although existing patterns are easy to detect and characterize, they are also subject to modification and mimicry. This means that we can construct transducers to make any communication pattern look like any other communication pattern. This is neither bad nor good for security. It is a fact that we need to accept and use as best we can

D-FENS: DNS Filtering & Extraction Network System for Malicious Domain Names

While the DNS (Domain Name System) has become a cornerstone for the operation of the Internet, it has also fostered creative cases of maliciousness, including phishing, typosquatting, and botnet communication among others. To address this problem, this dissertation focuses on identifying and mitigating such malicious domain names through prior knowledge and machine learning. In the first part of this dissertation, we explore a method of registering domain names with deliberate typographical mistakes (i.e., typosquatting) to masquerade as popular and well-established domain names. To understand the effectiveness of typosquatting, we conducted a user study which helped shed light on which techniques were more successful than others in deceiving users. While certain techniques fared better than others, they failed to take the context of the user into account. Therefore, in the second part of this dissertation we look at the possibility of an advanced attack which takes context into account when generating domain names. The main idea is determining the possibility for an adversary to improve their success rate of deceiving users with specifically-targeted malicious domain names. While these malicious domains typically target users, other types of domain names are generated by botnets for command & control (C2) communication. Therefore, in the third part of this dissertation we investigate domain generation algorithms (DGA) used by botnets and propose a method to identify DGA-based domain names. By analyzing DNS traffic for certain patterns of NXDomain (non-existent domain) query responses, we can accurately predict DGA-based domain names before they are registered. Given all of these approaches to malicious domain names, we ultimately propose a system called D-FENS (DNS Filtering & Extraction Network System). D-FENS uses machine learning and prior knowledge to accurately predict unreported malicious domain names in real-time, thereby preventing Internet devices from unknowingly connecting to a potentially malicious domain name

Using Context to Improve Network-based Exploit Kit Detection

Today, our computers are routinely compromised while performing seemingly innocuous activities like reading articles on trusted websites (e.g., the NY Times). These compromises are perpetrated via complex interactions involving the advertising networks that monetize these sites. Web-based compromises such as exploit kits are similar to any other scam -- the attacker wants to lure an unsuspecting client into a trap to steal private information, or resources -- generating 10s of millions of dollars annually. Exploit kits are web-based services specifically designed to capitalize on vulnerabilities in unsuspecting client computers in order to install malware without a user's knowledge. Sadly, it only takes a single successful infection to ruin a user's financial life, or lead to corporate breaches that result in millions of dollars of expense and loss of customer trust. Exploit kits use a myriad of techniques to obfuscate each attack instance, making current network-based defenses such as signature-based network intrusion detection systems far less effective than in years past. Dynamic analysis or honeyclient analysis on these exploits plays a key role in identifying new attacks for signature generation, but provides no means of inspecting end-user traffic on the network to identify attacks in real time. As a result, defenses designed to stop such malfeasance often arrive too late or not at all resulting in high false positive and false negative (error) rates. In order to deal with these drawbacks, three new detection approaches are presented. To deal with the issue of a high number of errors, a new technique for detecting exploit kit interactions on a network is proposed. The technique capitalizes on the fact that an exploit kit leads its potential victim through a process of exploitation by forcing the browser to download multiple web resources from malicious servers. This process has an inherent structure that can be captured in HTTP traffic and used to significantly reduce error rates. The approach organizes HTTP traffic into tree-like data structures, and, using a scalable index of exploit kit traces as samples, models the detection process as a subtree similarity search problem. The technique is evaluated on 3,800 hours of web traffic on a large enterprise network, and results show that it reduces false positive rates by four orders of magnitude over current state-of-the-art approaches. While utilizing structure can vastly improve detection rates over current approaches, it does not go far enough in helping defenders detect new, previously unseen attacks. As a result, a new framework that applies dynamic honeyclient analysis directly on network traffic at scale is proposed. The framework captures and stores a configurable window of reassembled HTTP objects network wide, uses lightweight content rendering to establish the chain of requests leading up to a suspicious event, then serves the initial response content back to the honeyclient in an isolated network. The framework is evaluated on a diverse collection of exploit kits as they evolve over a 1 year period. The empirical evaluation suggests that the approach offers significant operational value, and a single honeyclient can support a campus deployment of thousands of users. While the above approaches attempt to detect exploit kits before they have a chance to infect the client, they cannot protect a client that has already been infected. The final technique detects signs of post infection behavior by intrusions that abuses the domain name system (DNS) to make contact with an attacker. Contemporary detection approaches utilize the structure of a domain name and require hundreds of DNS messages to detect such malware. As a result, these detection mechanisms cannot detect malware in a timely manner and are susceptible to high error rates. The final technique, based on sequential hypothesis testing, uses the DNS message patterns of a subset of DNS traffic to detect malware in as little as four DNS messages, and with orders of magnitude reduction in error rates. The results of this work can make a significant operational impact on network security analysis, and open several exciting future directions for network security research.Doctor of Philosoph