APTs way: evading Your EBNIDS

APTs and government-supported attackers use a broad arsenal of techniques to avoid having their exploits detected by IDSes. Signature Based IDSes are not efficient against nation-state-sponsored attackers which use custom shellcode encoders in an exploit. Emulation Based NIDSes (EBNIDS) have been proposed as a solution to mitigate such attacks. EBNISes detect a suspicious network stream (pre-processing) and after converting them to emulate-able byte sequences run it in an instrumented environment (Emulation), finally matching the behavior with certain heuristics (Heuristics Detection). In this talk, we will present novel ways that an APT might use to circumvente the Pre-Processing, Emulation and Heuristic Detection steps of EBNIDSes by employing a wide range of evasion techniques

On the Infeasibility of Modeling Polymorphic Shellcode

Polymorphic malcode remains a troubling threat. The ability formal code to automatically transform into semantically equivalent variants frustrates attempts to rapidly construct a single, simple, easily verifiable representation. We present a quantitative analysis of the strengths and limitations of shellcode polymorphism and consider its impact on current intrusion detection practice. We focus on the nature of shellcode decoding routines. The empirical evidence we gather helps show that modeling the class of self-modifying code is likely intractable by known methods, including both statistical constructs and string signatures. In addition, we develop and present measures that provide insight into the capabilities, strengths, and weaknesses of polymorphic engines. In order to explore countermeasures to future polymorphic threats, we show how to improve polymorphic techniques and create a proof-of-concept engine expressing these improvements. Our results indicate that the class of polymorphic behavior is too greatly spread and varied to model effectively. Our analysis also supplies a novel way to understand the limitations of current signature-based techniques. We conclude that modeling normal content is ultimately a more promising defense mechanism than modeling malicious or abnormal content

Shadow Honeypots

We present Shadow Honeypots, a novel hybrid architecture that combines the best features of honeypots and anomaly detection. At a high level, we use a variety of anomaly detectors to monitor all traffic to a protected network or service. Traffic that is considered anomalous is processed by a "shadow honeypot" to determine the accuracy of the anomaly prediction. The shadow is an instance of the protected software that shares all internal state with a regular ("production") instance of the application, and is instrumented to detect potential attacks. Attacks against the shadow are caught, and any incurred state changes are discarded. Legitimate traffic that was misclassified will be validated by the shadow and will be handled correctly by the system transparently to the end user. The outcome of processing a request by the shadow is used to filter future attack instances and could be used to update the anomaly detector. Our architecture allows system designers to fine-tune systems for performance, since false positives will be filtered by the shadow. We demonstrate the feasibility of our approach in a proof-of-concept implementation of the Shadow Honeypot architecture for the Apache web server and the Mozilla Firefox browser. We show that despite a considerable overhead in the instrumentation of the shadow honeypot (up to 20% for Apache), the overall impact on the system is diminished by the ability to minimize the rate of false-positives

Using Memory Management to Detect and Extract Illegitimate Code for Malware Analysis

Exploits that successfully attack computers are mostly based on some form of shellcode, i.e., illegitimate code that is injected by the attacker to take control of the system. Detecting and extracting such code is the first step to detailed analysis of malware containing illegitimate code. The amount and sophistication of modern malware calls for automated mechanisms that perform such detection and extraction. In this paper we present a novel generic and fully automatic approach to detect the execution of illegitimate code and extract such code upon detection. The basic idea of the approach is to flag critical memory pages as non-executable and use a modified page fault handler to dump corresponding memory pages. We present an implementation of the approach for the Windows platform called CWXDetector. Evaluations using malicious PDF documents as example show that CWXDetector produces no false positives and has a similarly low false negative rate

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

Defending Browsers against Drive-by Downloads: Mitigating Heap-Spraying Code Injection Attacks

Abstract. Drive-by download attacks are among the most common methods for spreading malware today. These attacks typically exploit memory corruption vul-nerabilities in web browsers and browser plug-ins to execute shellcode, and in consequence, gain control of a victim’s computer. Compromised machines are then used to carry out various malicious activities, such as joining botnets, send-ing spam emails, or participating in distributed denial of service attacks. To counter drive-by downloads, we propose a technique that relies on x86 instruc-tion emulation to identify JavaScript string buffers that contain shellcode. Our de-tection is integrated into the browser, and performed before control is transfered to the shellcode, thus, effectively thwarting the attack. The solution maintains fair performance by avoiding unnecessary invocations of the emulator, while ensur-ing that every buffer with potential shellcode is checked. We have implemented a prototype of our system, and evaluated it over thousands of malicious and le-gitimate web sites. Our results demonstrate that the system performs accurate detection with no false positives

Shellzer: a tool for the dynamic analysis of malicious shellcode

Abstract. Shellcode is malicious binary code whose execution is triggered after the exploitation of a vulnerability. The automated analysis of malicious shellcode is a challenging task, since encryption and evasion techniques are often used. This paper introduces Shellzer, a novel dynamic shellcode analyzer that generates a complete list of the API functions called by the shellcode, and, in addition, returns the binaries retrieved at run-time by the shellcode. The tool is able to modify on-thefly the arguments and the return values of certain API functions in order to simulate specific execution contexts and the availability of the external resources needed by the shellcode. This tool has been tested with over 24,000 real-world samples, extracted from both web-based driveby-download attacks and malicious PDF documents. The results of the analysis show that Shellzer is able to successfully analyze 98 % of the shellcode samples

Unsupervised Anomaly-based Malware Detection using Hardware Features

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (signature) to execution patterns of known malware programs. In this work, we propose a new class of detectors - anomaly-based hardware malware detectors - that do not require signatures for malware detection, and thus can catch a wider range of malware including potentially novel ones. We use unsupervised machine learning to build profiles of normal program execution based on data from performance counters, and use these profiles to detect significant deviations in program behavior that occur as a result of malware exploitation. We show that real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can be detected with nearly perfect certainty. We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection. The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4, results unchange

Identifying Code Injection and Reuse Payloads In Memory Error Exploits

Today's most widely exploited applications are the web browsers and document readers we use every day. The immediate goal of these attacks is to compromise target systems by executing a snippet of malicious code in the context of the exploited application. Technical tactics used to achieve this can be classified as either code injection - wherein malicious instructions are directly injected into the vulnerable program - or code reuse, where bits of existing program code are pieced together to form malicious logic. In this thesis, I present a new code reuse strategy that bypasses existing and up-and-coming mitigations, and two methods for detecting attacks by identifying the presence of code injection or reuse payloads. Fine-grained address space layout randomization efficiently scrambles program code, limiting one's ability to predict the location of useful instructions to construct a code reuse payload. To expose the inadequacy of this exploit mitigation, a technique for "just-in-time" exploitation is developed. This new technique maps memory on-the-fly and compiles a code reuse payload at runtime to ensure it works in a randomized application. The attack also works in face of all other widely deployed mitigations, as demonstrated with a proof-of-concept attack against Internet Explorer 10 in Windows 8. This motivates the need for detection of such exploits rather than solely relying on prevention. Two new techniques are presented for detecting attacks by identifying the presence of a payload. Code reuse payloads are identified by first taking a memory snapshot of the target application, then statically profiling the memory for chains of code pointers that reuse code to implement malicious logic. Code injection payloads are identified with runtime heuristics by leveraging hardware virtualization for efficient sandboxed execution of all buffers in memory. Employing both detection methods together to scan program memory takes about a second and produces negligible false positives and false negatives provided that the given exploit is functional and triggered in the target application version. Compared to other strategies, such as the use of signatures, this approach requires relatively little effort spent on maintenance over time and is capable of detecting never before seen attacks. Moving forward, one could use these contributions to form the basis of a unique and effective network intrusion detection system (NIDS) to augment existing systems.Doctor of Philosoph

Bridging the detection gap: a study on a behavior-based approach using malware techniques

In recent years the intensity and complexity of cyber attacks have increased at a rapid rate. The cost of these attacks on U.S. based companies is in the billions of dollars, including the loss of intellectual property and reputation. Novel and diverse approaches are needed to mitigate the cost of a security breach, and bridge the gap between malware detection and a security breach. This thesis focuses on the short term need to mitigate the impact of undetected shellcodes that cause security breaches. The thesis\u27s approach focuses on the agents driving the attacks, capturing their actions, in order to piece together the attacks for forensics purposes, as well as to better understand the opponent. The work presented in this thesis employs models of normal operating system behavior to detect access to the operating system\u27s shell interface. It also utilizes malware techniques to avoid detection and subsequent termination of the monitoring system, as well as dynamic shellcode execution methodologies in the testing of the thesis\u27 modules to implement a monitoring system --Document