A honeypot is a closely monitored network decoy serving several purposes: it can distract adversaries from more valuable machines on a network, provide early warning about new attack and exploitation trends, or allow in-depth examination of adversaries during and after exploitation of a honeypot. Deploying a physical honeypot is often time intensive and expensive as different operating systems require specialized hardware and every honeypot requires its own physical system. This paper presents Honeyd, a framework for virtual honeypots that simulates virtual computer systems at the network level. The simulated computer systems appear to run on unallocated network addresses. To deceive network fingerprinting tools, Honeyd simulates the networking stack of different operating systems and can provide arbitrary routing topologies and services for an arbitrary number of virtual systems. This paper discusses Honeyd's design and shows how the Honeyd framework helps in many areas of system security, e.g. detecting and disabling worms, distracting adversaries, or preventing the spread of spam email.

/pdf/a-virtual-honeypot-framework-57fm9wrmjt.pdf

A virtual honeypot framework

Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.

Uncovering social spammers: social honeypots + machine learning

This paper describes a system for automated generation of attack signatures for network intrusion detection systems. Our system applies pattern-matching techniques and protocol conformance checks on multiple levels in the protocol hierarchy to network traffic captured a honeypot system. We present results of running the system on an unprotected cable modem connection for 24 hours. The system successfully created precise traffic signatures that otherwise would have required the skills and time of a security officer to inspect the traffic manually.

http://dpnm.postech.ac.kr/research/04/nsri/papers/honypot.pdf

Honeycomb: creating intrusion detection signatures using honeypots

Anti-malware companies receive thousands of malware samples every day. To process this large quantity, a number of automated analysis tools were developed. These tools execute a malicious program in a controlled environment and produce reports that summarize the program’s actions. Of course, the problem of analyzing the reports still remains. Recently, researchers have started to explore automated clustering techniques that help to identify samples that exhibit similar behavior. This allows an analyst to discard reports of samples that have been seen before, while focusing on novel, interesting threats. Unfortunately, previous techniques do not scale well and frequently fail to generalize the observed activity well enough to recognize

/pdf/scalable-behavior-based-malware-clustering-25p1c7kfx6.pdf

Scalable, behavior-based malware clustering

We examine two privacy controls for Android smartphones that empower users to run permission-hungry applications while protecting private data from being exfiltrated: (1) covertly substituting shadow data in place of data that the user wants to keep private, and (2) blocking network transmissions that contain data the user made available to the application for on-device use only. We retrofit the Android operating system to implement these two controls for use with unmodified applications. A key challenge of imposing shadowing and exfiltration blocking on existing applications is that these controls could cause side effects that interfere with user-desired functionality. To measure the impact of side effects, we develop an automated testing methodology that records screenshots of application executions both with and without privacy controls, then automatically highlights the visual differences between the different executions. We evaluate our privacy controls on 50 applications from the Android Market, selected from those that were both popular and permission-hungry. We find that our privacy controls can successfully reduce the effective permissions of the application without causing side effects for 66% of the tested applications. The remaining 34% of applications implemented user-desired functionality that required violating the privacy requirements our controls were designed to enforce; there was an unavoidable choice between privacy and user-desired functionality.

/pdf/these-aren-t-the-droids-you-re-looking-for-retrofitting-19ujl8aa8u.pdf

These aren't the droids you're looking for: retrofitting android to protect data from imperious applications

"The text is comprehensive, an honest survey of every honeypot technology I had ever heard of and a number I read about for the first time." --Stephen Northcutt, The SANS Institute"One of the great byproducts of Lance's work with honeypots and honeynets is that he's helped give us a much clearer picture of the hacker in action." --From the Foreword by Marcus J. Ranum"From the basics of shrink-wrapped honeypots that catch script kiddies to the detailed architectures of next-generation honeynets for trapping more sophisticated bad guys, this book covers it all....This book really delivers new information and insight about one of the most compelling information security technologies today." --Ed Skoudis, author of Counter Hack, SANS instructor, and Vice President of Security Strategy for Predictive SystemsHoneypots are unique technological systems specifically designed to be probed, attacked, or compromised by an online attacker. Implementing a honeypot provides you with an unprecedented ability to take the offensive against hackers. Whether used as simple "burglar alarms," incident response systems, or tools for gathering information about hacker motives and tactics, honeypots can add serious firepower to your security arsenal.Honeypots: Tracking Hackers is the ultimate guide to this rapidly growing, cutting-edge technology. The book starts with a basic examination of honeypots and the different roles they can play, and then moves on to in-depth explorations of six specific kinds of real-world honeypots: BackOfficer Friendly, Specter™, Honeyd, Homemade honeypots, ManTrap®, and Honeynets.Honeypots also includes a chapter dedicated to legal issues surrounding honeypot use. Written with the guidance of three legal experts, this section explores issues of privacy, entrapment, and liability. The book also provides an overview of the Fourth Amendment, the Electronic Communications Privacy Act, the Wiretap Act, and the Pen/Trap Statute, with an emphasis on how each applies to honeypots.With this book you will gain an understanding of honeypot concepts and architecture, as well as the skills to deploy the best honeypot solutions for your environment. You will arm yourself with the expertise needed to track attackers and learn about them on your own. Security professionals, researchers, law enforcement agents, and members of the intelligence and military communities will find this book indispensable.The CD-ROM contains white papers, source code, and data captures of real attacks. Its contents will help you build and deploy your own honeypot solutions and analyze real attacks. 0321108957B08282002

Honeypots: Tracking Hackers

What specific threats do computer networks face from hackers? Who's perpetrating these threats and how? The Honeynet Project is an organization dedicated to answering these questions. It studies the bad guys and shares the lessons learned. The group gathers information by deploying networks (called honeynets) that are designed to be compromised.

The Honeynet Project: trapping the hackers

In the past several years there has been extensive research into honeypot technologies, primarily for detection and information gathering against external threats. However, little research has been done for one of the most dangerous threats, the advance insider, the trusted individual who knows our internal organization. These individuals are not after our systems, they are after our information. We discuss how honeypot technologies can be used to detect, identify, and gather information on these specific threats.

/pdf/honeypots-catching-the-insider-threat-5exdnupa9a.pdf

L. Spitzner

Papers

Honeypots: Tracking Hackers

The Honeynet Project: trapping the hackers

Honeypots: catching the insider threat