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

Thwarting Attacks in Malcode-Bearing Documents by Altering Data Sector Values

Embedding malcode within documents provides a convenient means of attacking systems. Such attacks can be very targeted and difficult to detect to stop due to the multitude of document-exchange vectors and the vulnerabilities in modern document processing applications. Detecting malcode embedded in a document is difficult owing to the complexity of modern document formats that provide ample opportunity to embed code in a myriad of ways. We focus on Microsoft Word documents as malcode carriers as a case study in this paper. To detect stealthy embedded malcode in documents, we develop an arbitrary data transformation technique that changes the value of data segments in documents in such a way as to purposely damage any hidden malcode that may be embedded in those sections. Consequently, the embedded malcode will not only fail but also introduce a system exception that would be easily detected. The method is intended to be applied in a safe sandbox, the transformation is reversible after testing a document, and does not require any learning phase. The method depends upon knowledge of the structure of the document binary format to parse a document and identify the specific sectors to which the method can be safely applied for malcode detection. The method can be implemented in MS Word as a security feature to enhance the safety of Word documents

Automated Approach to Intrusion Detection in VM-based Dynamic Execution Environment

Because virtual computing platforms are dynamically changing, it is difficult to build high-quality intrusion detection system. In this paper, we present an automated approach to intrusions detection in order to maintain sufficient performance and reduce dependence on execution environment. We discuss a hidden Markov model strategy for abnormality detection using frequent system call sequences, letting us identify attacks and intrusions automatically and efficiently. We also propose an automated mining algorithm, named AGAS, to generate frequent system call sequences. In our approach, the detection performance is adaptively tuned according to the execution state every period. To improve performance, the period value is also under self-adjustment

POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.Comment: The final version of this paper is going to appear in the ACM SIGSAC Conference on Computer and Communications Security (CCS'19), November 11-15, 2019, London, United Kingdo

A Practical Mimicry Attack Against Powerful System-Call Monitors

System-call monitoring has become the basis for many hostbased intrusion detection as well as policy enforcement techniques. Mimicry attacks attempt to evade system-call monitoring IDS by executing innocuous-looking sequences of system calls that accomplish the attacker’s goals. Mimicry attacks may execute a sequence of dozens of system calls in order to evade detection. Finding such a sequence is difficult, so researchers have focused on tools for automating mimicry attacks and extending them to gray-box IDS 1. In this paper, we describe an alternative approach for building mimicry attacks using only skills and technologies that hackers possess today, making this attack a more immediate and realistic threat. These attacks, which we call persistent interposition attacks, are not as powerful as traditional mimicry attacks — an adversary cannot obtain a root shell using a persistent interposition attack — but are sufficient to accomplish the goals of today’s cyber-criminals. Persistent interposition attacks are stealthier than standard mimicry attacks and are amenable to covert information-harvesting attacks, features that are likely to be attractive to profitmotivated criminals. Persistent interposition attacks are not IDS specific — they can evade a large class of systemcall-monitoring intrusion-detection systems, which we call I/O-data-oblivious. I/O-data-oblivious monitors have perfect knowledge of the values of all system call arguments as well as their relationships, with the exception of data buffer arguments to read and write. Many of today’s black-box and gray-box IDS are I/O-data-oblivious and hence vulnerable to persistent interposition attacks

Automated Virtual Machine Introspection for Host-Based Intrusion Detection

This thesis examines techniques to automate configuration of an intrusion detection system utilizing hardware-assisted virtualization. These techniques are used to detect the version of a running guest operating system, automatically configure version-specific operating system information needed by the introspection library, and to locate and monitor important operating system data structures. This research simplifies introspection library configuration and is a step toward operating system independent introspection. An operating system detection algorithm and Windows virtual machine system service dispatch table monitor are implemented using the Xen hypervisor and a modified version of the XenAccess library. All detection and monitoring is implemented from the Xen management domain. Results of the operating system detection are used to initialize the XenAccess library. Library initialization time and kernel symbol retrieval are compared to the standard library. The algorithm is evaluated using nine versions of the Windows operating system. The system service dispatch table monitor is evaluated using the Agony and ProAgent rootkits. The automation techniques successfully detect the operating system and system service dispatch table hooks for the nine Windows versions tested. The modified XenAccess library exhibits an average initialization speedup of 1.9. Kernel symbol lookup is 10 times faster, on average. The hook detector is able to detect all hooks used by both rookits

Security Evaluation of a Banking Fraud Analysis System

open7noThe significant growth of banking frauds, fueled by the underground economy of malware, raised the need for effective detection systems. Therefore, in last the years, banks have upgraded their security to protect transactions from frauds. State-of-the-art solutions detect frauds as deviations from customers’ spending habits. To the best of our knowledge, almost all existing approaches do not provide an in-depth model’s granularity and security analysis against elusive attacks. In this paper, we examine Banksealer, a decision support system for banking fraud analysis, evaluating the influence on the detection performance of the granularity at which the spending habits are modeled and its security against evasive attacks. First, we compare user-centric modeling, which builds a model for each user, with system-centric modeling, which builds a model for the entire system, from the point of view of the detection performance. Then, we assess the robustness of Banksealer against malicious attackers that are aware of the structure of the models in use. To this end, we design and implement a proof-of-concept attack tool that performs mimicry attacks, emulating a sophisticated attacker that cloaks frauds to avoid detection. We experimentally confirm the feasibility of such attacks, their cost and the effort required to an attacker in order to perform them. In addition, we discuss possible countermeasures. We provide a comprehensive evaluation on a large, real-world dataset obtained from one of the largest Italian banks.openCarminati, Michele; Polino, Mario; Continella, Andrea; Lanzi, Andrea; Maggi, Federico; Zanero, StefanoCarminati, Michele; Polino, Mario; Continella, Andrea; Lanzi, Andrea; Maggi, Federico; Zanero, Stefano; Zanero, Stefan