4,732 research outputs found
CHERI: a research platform deconflating hardware virtualisation and protection
Contemporary CPU architectures conflate virtualization and protection,
imposing virtualization-related performance, programmability,
and debuggability penalties on software requiring finegrained
protection. First observed in micro-kernel research, these
problems are increasingly apparent in recent attempts to mitigate
software vulnerabilities through application compartmentalisation.
Capability Hardware Enhanced RISC Instructions (CHERI) extend
RISC ISAs to support greater software compartmentalisation.
CHERI’s hybrid capability model provides fine-grained compartmentalisation
within address spaces while maintaining software
backward compatibility, which will allow the incremental deployment
of fine-grained compartmentalisation in both our most trusted
and least trustworthy C-language software stacks. We have implemented
a 64-bit MIPS research soft core, BERI, as well as a
capability coprocessor, and begun adapting commodity software
packages (FreeBSD and Chromium) to execute on the platform
Identifying Native Applications with High Assurance
The work described in this paper investigates the problem
of identifying and deterring stealthy malicious processes on
a host. We point out the lack of strong application iden-
tication in main stream operating systems. We solve the
application identication problem by proposing a novel iden-
tication model in which user-level applications are required
to present identication proofs at run time to be authenti-
cated by the kernel using an embedded secret key. The se-
cret key of an application is registered with a trusted kernel
using a key registrar and is used to uniquely authenticate
and authorize the application. We present a protocol for
secure authentication of applications. Additionally, we de-
velop a system call monitoring architecture that uses our
model to verify the identity of applications when making
critical system calls. Our system call monitoring can be
integrated with existing policy specication frameworks to
enforce application-level access rights. We implement and
evaluate a prototype of our monitoring architecture in Linux
as device drivers with nearly no modication of the ker-
nel. The results from our extensive performance evaluation
shows that our prototype incurs low overhead, indicating the
feasibility of our model
A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection
Enterprise networks that host valuable assets and services are popular and
frequent targets of distributed network attacks. In order to cope with the
ever-increasing threats, industrial and research communities develop systems
and methods to monitor the behaviors of their assets and protect them from
critical attacks. In this paper, we systematically survey related research
articles and industrial systems to highlight the current status of this arms
race in enterprise network security. First, we discuss the taxonomy of
distributed network attacks on enterprise assets, including distributed
denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing
methods in monitoring and classifying network behavior of enterprise hosts to
verify their benign activities and isolate potential anomalies. Third,
state-of-the-art detection methods for distributed network attacks sourced from
external attackers are elaborated, highlighting their merits and bottlenecks.
Fourth, as programmable networks and machine learning (ML) techniques are
increasingly becoming adopted by the community, their current applications in
network security are discussed. Finally, we highlight several research gaps on
enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive
Robust and secure monitoring and attribution of malicious behaviors
Worldwide computer systems continue to execute malicious software that degrades the systemsâ performance and consumes network capacity by generating high volumes of unwanted traffic. Network-based detectors can effectively identify machines participating in the ongoing attacks by monitoring the traffic to and from the systems. But, network detection alone is not enough; it does not improve the operation of the Internet or the health of other machines connected to the network. We must identify malicious code running on infected systems, participating in global attack networks.
This dissertation describes a robust and secure approach that identifies malware present on infected systems based on its undesirable use of network. Our approach, using virtualization, attributes malicious traffic to host-level processes responsible for the traffic. The attribution identifies on-host processes, but malware instances often exhibit parasitic behaviors to subvert the execution of benign processes.
We then augment the attribution software with a host-level monitor that detects parasitic behaviors occurring at the user- and kernel-level. User-level parasitic attack detection happens via the system-call interface because it is a non-bypassable interface for user-level processes. Due to the unavailability of one such interface inside the kernel for drivers, we create a new driver monitoring interface inside the kernel to detect parasitic attacks occurring through this interface.
Our attribution software relies on a guest kernelâ s data to identify on-host processes. To allow secure attribution, we prevent illegal modifications of critical kernel data from kernel-level malware. Together, our contributions produce a unified research outcome --an improved malicious code identification system for user- and kernel-level malware.Ph.D.Committee Chair: Giffin, Jonathon; Committee Member: Ahamad, Mustaque; Committee Member: Blough, Douglas; Committee Member: Lee, Wenke; Committee Member: Traynor, Patric
A log mining approach for process monitoring in SCADA
SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow
AdSplit: Separating smartphone advertising from applications
A wide variety of smartphone applications today rely on third-party
advertising services, which provide libraries that are linked into the hosting
application. This situation is undesirable for both the application author and
the advertiser. Advertising libraries require additional permissions, resulting
in additional permission requests to users. Likewise, a malicious application
could simulate the behavior of the advertising library, forging the user's
interaction and effectively stealing money from the advertiser. This paper
describes AdSplit, where we extended Android to allow an application and its
advertising to run as separate processes, under separate user-ids, eliminating
the need for applications to request permissions on behalf of their advertising
libraries.
We also leverage mechanisms from Quire to allow the remote server to validate
the authenticity of client-side behavior. In this paper, we quantify the degree
of permission bloat caused by advertising, with a study of thousands of
downloaded apps. AdSplit automatically recompiles apps to extract their ad
services, and we measure minimal runtime overhead. We also observe that most ad
libraries just embed an HTML widget within and describe how AdSplit can be
designed with this in mind to avoid any need for ads to have native code
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