We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a generalpurpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an “open box, ” i.e. a general-purpose hardware platform like today’s PCs and workstations, or a “closed box, ” an opaque special-purpose platform that protects the privacy and integrity of its contents like today’s game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations. In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis: a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing: an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead

Analyzing intrusions today is an arduous, largely manual task because system administrators lack the information and tools needed to understand easily the sequence of steps that occurred in an attack. The goal of BackTracker is to identify automatically potential sequences of steps that occurred in an intrusion. Starting with a single detection point (e.g., a suspicious file), BackTracker identifies files and processes that could have affected that detection point and displays chains of events in a dependency graph. We use BackTracker to analyze several real attacks against computers that we set up as honeypots. In each case, BackTracker is able to highlight effectively the entry point used to gain access to the system and the sequence of steps from that entry point to the point at which we noticed the intrusion. The logging required to support BackTracker added 9 % overhead in running time and generated 1.2 GB per day of log data for an operating-system intensive workload.

We describe a new, general approach for safeguarding systems against any type of code-injection attack. We apply Kerckhoff’s principle, by creating process-specific randomized instruction sets (e.g., machine instructions) of the system executing potentially vulnerable software. An attacker who does not know the key to the randomization algorithm will inject code that is invalid for that randomized processor, causing a runtime exception. To determine the difficulty of integrating support for the proposed mechanism in the operating system, we modified the Linux kernel, the GNU binutils tools, and the bochs-x86 emulator. Although the performance penalty is significant, our prototype demonstrates the feasibility of the approach, and should be directly usable on a suitable-modified processor (e.g., the Transmeta Crusoe). Our approach is equally applicable against code-injecting attacks in scripting and interpreted languages, e.g., web-based SQL injection. We demonstrate this by modifying the Perl interpreter to permit randomized script execution. The performance penalty in this case is minimal. Where our proposed approach is feasible (i.e., in an emulated environment, in the presence of programmable or specialized hardware, or in interpreted languages), it can serve as a low-overhead protection mechanism, and can easily complement other mechanisms.

Most systems contain software with yet-to-be-discovered security vulnerabilities. When a vulnerability is disclosed, administrators face the grim reality that they have been running software which was open to attack. Sites that value availability may be forced to continue running this vulnerable software until the accompanying patch has been tested. Our goal is to improve security by detecting intrusions that occurred before the vulnerability was disclosed and by detecting and responding to intrusions that are attempted after the vulnerability is disclosed. We detect when a vulnerability is triggered by executing vulnerability-specific predicates as the system runs or replays. This paper describes the design, implementation and evaluation of a system that supports the construction and execution of these vulnerability-specific predicates. Our system, called Intro-Virt, uses virtual-machine introspection to monitor the execution of application and operating system software. Intro-Virt executes predicates over past execution periods by combining virtual-machine introspection with virtual-machine replay. IntroVirt eases the construction of powerful predicates by allowing predicates to run existing target code in the context of the target system, and it uses checkpoints so that predicates can execute target code without perturbing the state of the target system. IntroVirt allows predicates to refresh themselves automatically so they work in the presence of preemptions. We show that vulnerabilityspecific predicates can be written easily for a wide variety of real vulnerabilities, can detect and respond to intrusions over both the past and present time intervals, and add little overhead for most vulnerabilities.

Attackers and defenders of computer systems both strive to gain complete control over the system. To maximize their control, both attackers and defenders have migrated to low-level, operating system code. In this paper, we assume the perspective of the attacker, who is trying to run malicious software and avoid detection. By assuming this perspective, we hope to help defenders understand and defend against the threat posed by a new class of rootkits. We evaluate a new type of malicious software that gains qualitatively more control over a system. This new type of malware, which we call a virtual-machine based rootkit (VMBR), installs a virtual-machine monitor underneath an existing operating system and hoists the original operating system into a virtual machine. Virtual-machine based rootkits are hard to detect and remove because their state cannot be accessed by software running in the target system. Further, VMBRs support general-purpose malicious services by allowing such services to run in a separate operating system that is protected from the target system. We evaluate this new threat by implementing two proof-of-concept VMBRs. We use our proof-of-concept VMBRs to subvert Windows XP and Linux target systems, and we implement four example malicious services using the VMBR platform. Last, we use what we learn from our proof-of-concept VMBRs to explore ways to defend against this new threat. We discuss possible ways to detect and prevent VMBRs, and we implement a defense strategy suitable for protecting systems against this threat. 1.

We propose a new approach for reacting to a wide variety of software failures, ranging from remotely exploitable vulnerabilities to more mundane bugs that cause abnormal program termination (e.g., illegal memory dereference). Our emphasis is in creating "self-healing" software that can protect itself against a recurring fault until a more comprehensive fix is applied.

We propose SecVisor, a tiny hypervisor that ensures code integrity for commodity OS kernels. In particular, SecVisor ensures that only user-approved code can execute in kernel mode over the entire system lifetime. This protects the kernel against code injection attacks, such as kernel rootkits. SecVisor can achieve this property even against an attacker who controls everything but the CPU, the memory controller, and system memory chips. Further, SecVisor can even defend against attackers with knowledge of zero-day kernel exploits. Our goal is to make SecVisor amenable to formal verification and manual audit, thereby making it possible to rule out known classes of vulnerabilities. To this end, SecVisor offers small code size and small external interface. We rely on memory virtualization to build SecVisor and implement two versions, one using software memory virtualization and the other using CPU-supported memory virtualization. The code sizes of the runtime portions of these versions are 1739 and 1112 lines, respectively. The size of the external interface for both versions of SecVisor is 2 hypercalls. It is easy to port OS kernels to SecVisor. We port the Linux kernel version 2.6.20 by adding 12 lines and deleting 81 lines, out of a total of approximately 4.3 million lines of code in the kernel.

The honeypot has emerged as an effective tool to provide insights into new attacks and current exploitation trends. Though effective, a single honeypot or multiple independently operated honeypots only provide a limited local view of network attacks. Deploying and managing a large number of coordinating honeypots in different network domains will not only provide a broader and more diverse view, but also create potentials in global network status inference, early network anomaly detection, and attack correlation in large scale. However, coordinated honeypot deployment and operation require close and consistent collaboration across participating network domains, in order to mitigate potential security risks associated with each honeypot and the non-uniform level

for advertised honeypots with automatic signature generation