Ryan Riley

Guest-Transparent Prevention of Kernel Rootkits with VMM-Based Memory Shadowing

Kernel rootkits pose a significant threat to computer systems as they run at the highest privilege level and have unrestricted access to the resources of their victims. Many current efforts in kernel rootkit defense focus on the detectionof kernel rootkits --- after a rootkit attack has taken place, while the smaller number of efforts in kernel rootkit preventionexhibit limitations in their capability or deployability. In this paper we present a kernel rootkit prevention system called NICKLE which addresses a common, fundamental characteristic of most kernel rootkits: the need for executing their own kernel code. NICKLE is a lightweight, virtual machine monitor (VMM) based system that transparently prevents unauthorized kernel code execution for unmodified commodity (guest) OSes. NICKLE is based on a new scheme called memory shadowing, wherein the trusted VMM maintains a shadow physical memory for a running VM and performs real-time kernel code authentication so that only authenticated kernel code will be stored in the shadow memory. Further, NICKLE transparently routes guest kernel instruction fetches to the shadow memory at runtime. By doing so, NICKLE guarantees that only the authenticated kernel code will be executed, foiling the kernel rootkits attempt to strike in the first place. We have implemented NICKLE in three VMM platforms: QEMU+KQEMU, VirtualBox, and VMware Workstation. Our experiments with 23 real-world kernel rootkits targeting the Linux or Windows OSes demonstrate NICKLEs effectiveness. Furthermore, our performance evaluation shows that NICKLE introduces small overhead to the VMM platform.

Defeating Dynamic Data Kernel Rootkit Attacks via VMM-Based Guest-Transparent Monitoring

Targeting the operating system kernel, the core of trust in a system, kernel rootkits are able to compromise the entire system, placing it under malicious control, while eluding detection efforts. Within the realm of kernel rootkits, dynamic data rootkits are particularly elusive due to the fact that they attack only data targets. Dynamic data rootkits avoid code injection and instead use existing kernel code to manipulate kernel data. Because they do not execute any new code, they are able to complete their attacks without violating kernel code integrity. We propose a prevention solution that blocks dynamic data kernel rootkit attacks by monitoring kernel memory access using virtual machine monitor (VMM) policies. Although the VMM is an external monitor, our system preemptively detects changes to monitored kernel data states and enables fine-grained inspection of memory accesses on dynamically changing kernel data. In addition, readable and writable kernel data can be protected by exposing the illegal use of existing code by dynamic data kernel rootkits.We have implemented a prototype of our system using the QEMU VMM. Our experiments show that it successfully defeats synthesized dynamic data kernel rootkits in real-time, demonstrating its effectiveness and practicality.

Polymorphing Software by Randomizing Data Structure Layout

This paper introduces a new software polymorphism technique that randomizes program data structure layout. This technique will generate different data structure layouts for a program and thus diversify the binary code compiled from the same program source code. This technique can mitigate attacks (e.g., kernel rootkit attacks) that require knowledge about data structure definitions. It is also able to disrupt the generation of data structure-based program signatures. We have implemented our data structure layout randomization technique in the open source compiler collection gcc-4.2.4 and applied it to a number of programs. Our evaluation results show that our technique is able to achieve software binary diversity. We also apply the technique to one operating system data structure in order to foil a number of kernel rootkit attacks. Meanwhile, programs produced by the technique were analyzed by a state-of-the-art data structure inference system and it was demonstrated that reliance on data structure signatures alone may lead to false negatives in malware detection.

LiveDM: kernel malware analysis with un-tampered and temporal views of dynamic kernel memory

Dynamic kernel memory has been a popular target of recent kernel malware due to the difficulty of determining the status of volatile dynamic kernel objects. Some existing approaches use kernel memory mapping to identify dynamic kernel objects and check kernel integrity. The snapshot-based memory maps generated by these approaches are based on the kernel memory which may have been manipulated by kernel malware. In addition, because the snapshot only reflects the memory status at a single time instance, its usage is limited in temporal kernel execution analysis. We introduce a new runtime kernel memory mapping scheme called allocation-driven mapping, which systematically identifies dynamic kernel objects, including their types and lifetimes. The scheme works by capturing kernel object allocation and deallocation events. Our system provides a number of unique benefits to kernel malware analysis: (1) an un-tampered view wherein the mapping of kernel data is unaffected by the manipulation of kernel memory and (2) a temporal view of kernel objects to be used in temporal analysis of kernel execution. We demonstrate the effectiveness of allocation-driven mapping in two usage scenarios. First, we build a hidden kernel object detector that uses an un-tampered view to detect the data hiding attacks of 10 kernel rootkits that directly manipulate kernel objects (DKOM). Second, we develop a temporal malware behavior monitor that tracks and visualizes malware behavior triggered by the manipulation of dynamic kernel objects. Allocation-driven mapping enables a reliable analysis of such behavior by guiding the inspection only to the events relevant to the attack.

Iso-X: A Flexible Architecture for Hardware-Managed Isolated Execution

We consider the problem of how to provide an execution environment where the applications secrets are safe even in the presence of malicious system software layers. We propose Iso-X -- a flexible, fine-grained hardware-supported framework that provides isolation for security-critical pieces of an application such that they can execute securely even in the presence of untrusted system software. Isolation in Iso-X is achieved by creating and dynamically managing compartments to host critical fragments of code and associated data. Iso-X provides fine-grained isolation at the memory-page level, flexible allocation of memory, and a low-complexity, hardware-only trusted computing base. Iso-X requires minimal additional hardware, a small number of new ISA instructions to manage compartments, and minimal changes to the operating system which need not be in the trusted computing base. The run-time performance overhead of Iso-X is negligible and even the overhead of creating and destroying compartments is modest. Iso-X offers higher memory flexibility than the recently proposed SGX design from Intel, allowing both fluid partitioning of the vailable memory space and dynamic growth of compartments. An FPGA implementation of Iso-X runtime mechanisms shows a negligible impact on the processor cycle time.

An Architectural Approach to Preventing Code Injection Attacks

Code injection attacks, despite being well researched, continue to be a problem today. Modern architectural solutions such as the execute-disable bit and PaX have been useful in limiting the attacks; however, they enforce program layout restrictions and can oftentimes still be circumvented by a determined attacker. We propose a change to the memory architecture of modern processors that addresses the code injection problem at its very root by virtually splitting memory into code memory and data memory such that a processor will never be able to fetch injected code for execution. This virtual split memory system can be implemented as a software-only patch to an operating system and can be used to supplement existing schemes for improved protection. Furthermore, our system is able to accommodate a number of response modes when a code injection attack occurs. Our experiments with both benchmarks and real-world attacks show the system is effective in preventing a wide range of code injection attacks while incurring reasonable overhead.

Data-Centric OS Kernel Malware Characterization

Traditional malware detection and analysis approaches have been focusing on code-centric aspects of malicious programs, such as detection of the injection of malicious code or matching malicious code sequences. However, modern malware has been employing advanced strategies, such as reusing legitimate code or obfuscating malware code to circumvent the detection. As a new perspective to complement code-centric approaches, we propose a data-centric OS kernel malware characterization architecture that detects and characterizes malware attacks based on the properties of data objects manipulated during the attacks. This framework consists of two system components with novel features: First, a runtime kernel object mapping system which has an un-tampered view of kernel data objects resistant to manipulation by malware. This view is effective at detecting a class of malware that hides dynamic data objects. Second, this framework consists of a new kernel malware detection approach that generates malware signatures based on the data access patterns specific to malware attacks. This approach has an extended coverage that detects not only the malware with the signatures, but also the malware variants that share the attack patterns by modeling the low level data access behaviors as signatures. Our experiments against a variety of real-world kernel rootkits demonstrate the effectiveness of data-centric malware signatures.

BranchScope: A New Side-Channel Attack on Directional Branch Predictor

We present BranchScope - a new side-channel attack where the attacker infers the direction of an arbitrary conditional branch instruction in a victim program by manipulating the shared directional branch predictor. The directional component of the branch predictor stores the prediction on a given branch (taken or not-taken) and is a different component from the branch target buffer (BTB) attacked by previous work. BranchScope is the first fine-grained attack on the directional branch predictor, expanding our understanding of the side channel vulnerability of the branch prediction unit. Our attack targets complex hybrid branch predictors with unknown organization. We demonstrate how an attacker can force these predictors to switch to a simple 1-level mode to simplify the direction recovery. We carry out BranchScope on several recent Intel CPUs and also demonstrate the attack against an SGX enclave.

DroidNative: Automating and optimizing detection of Android native code malware variants

According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively (~99%) on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malware variants, thwarting detection by signature-based detectors. In addition, the plethora of more sophisticated detectors making use of static analysis techniques to detect such variants operate only at the bytecode level, meaning that malware embedded in native code goes undetected. A recent study shows that 86% of the most popular Android applications contain native code, making native code malware a plausible threat vector. This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations and provides automation. As far as we know, DroidNative is the first system that builds cross-platform (x86 and ARM) semantic-based signatures at the Android native code level, allowing the system to detect malware embedded in either bytecode or native code. When tested with a dataset of 5490 samples, DroidNative achieves a detection rate (DR) of 93.57% and a false positive rate of 2.7%. When tested with traditional malware variants, it achieves a DR of 99.48%, compared to the DRs of academic and commercial tools that range from 8.33% to 93.22%.

Binary consensus in sensor motes

In this work, we adapt the binary consensus algorithm for use in wireless sensor networks. Binary consensus is used to allow a collection of distributed entities to reach consensus regarding the answer to a binary question and the final decision is based on the majority opinion. Binary consensus can play a basic role in increasing the accuracy of detecting event occurrence. Existing work on the algorithm focuses on simulation of the algorithm in a purely theoretic sense. In this work, we modify the algorithm to function in wireless sensor networks by adding a method for nodes to determine who to communicate with as well as adding a heuristic for nodes to know when the algorithm has completed. We implement and test our algorithm in real wireless sensor motes and further support our results with a wireless mote simulator.