Detecting malicious activities with user-agent-based profiles

Hypertext transfer protocol (HTTP) has become the main protocol to carry out malicious activities. Attackers typically use HTTP for communication with command-and-control servers, click fraud, phishing and other malicious activities, as they can easily hide among the large amount of benign HTTP traffic. The user-agent (UA) field in the HTTP header carries information on the application, operating system (OS), device, and so on, and adversaries fake UA strings as a way to evade detection. Motivated by this, we propose a novel grammar-guided UA string classification method in HTTP flows. We leverage the fact that a number of ‘standard’ applications, such as web browsers and iOS mobile apps, have well-defined syntaxes that can be specified using context-free grammars, and we extract OS, device and other relevant information from them. We develop association heuristics to classify UA strings that are generated by ‘non-standard’ applications that do not contain OS or device information. We provide a proof-of-concept system that demonstrates how our approach can be used to identify malicious applications that generate fake UA strings to engage in fraudulent activities

Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data

Recent years have seen the rise of more sophisticated attacks including advanced persistent threats (APTs) which pose severe risks to organizations and governments by targeting confidential proprietary information. Additionally, new malware strains are appearing at a higher rate than ever before. Since many of these malware are designed to evade existing security products, traditional defenses deployed by most enterprises today, e.g., anti-virus, firewalls, intrusion detection systems, often fail at detecting infections at an early stage. We address the problem of detecting early-stage infection in an enterprise setting by proposing a new framework based on belief propagation inspired from graph theory. Belief propagation can be used either with "seeds" of compromised hosts or malicious domains (provided by the enterprise security operation center -- SOC) or without any seeds. In the latter case we develop a detector of C&C communication particularly tailored to enterprises which can detect a stealthy compromise of only a single host communicating with the C&C server. We demonstrate that our techniques perform well on detecting enterprise infections. We achieve high accuracy with low false detection and false negative rates on two months of anonymized DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of real-world web proxy logs collected at the border of a large enterprise. Through careful manual investigation in collaboration with the enterprise SOC, we show that our techniques identified hundreds of malicious domains overlooked by state-of-the-art security products

$1.00 per RT #BostonMarathon #PrayForBoston: analyzing fake content on Twitter

This study found that 29% of the most viral content on Twitter during the Boston bombing crisis were rumors and fake content.AbstractOnline social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information. We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves

Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots

The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms. To block the monitoring system in the internet the attackers are targeted the ITM system. In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic. We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM. Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots