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

Smashing the Stack with Hydra: The Many Heads of Advanced Polymorphic Shellcode

Recent work on the analysis of polymorphic shellcode engines suggests that modern obfuscation methods would soon eliminate the usefulness of signature-based network intrusion detection methods and supports growing views that the new generation of shellcode cannot be accurately and efficiently represented by the string signatures which current IDS and AV scanners rely upon. In this paper, we expand on this area of study by demonstrating never before seen concepts in advanced shellcode polymorphism with a proof-of-concept engine which we call Hydra. Hydra distinguishes itself by integrating an array of obfuscation techniques, such as recursive NOP sleds and multi-layer ciphering into one system while offering multiple improvements upon existing strategies. We also introduce never before seen attack methods such as byte-splicing statistical mimicry, safe-returns with forking shellcode and syscall-time-locking. In total, Hydra simultaneously attacks signature, statistical, disassembly, behavioral and emulation-based sensors, as well as frustrates offline forensics. This engine was developed to present an updated view of the frontier of modern polymorphic shellcode and provide an effective tool for evaluation of IDS systems, Cyber test ranges and other related security technologies

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

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

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

Accurate shellcode recognition from network traffic data using artificial neural nets

This paper presents an approach to shellcode recognition directly from network traffic data using a multi-layer perceptron with back-propagation learning algorithm. Using raw network data composed of a mixture of shellcode, image files, and DLL-Dynamic Link Library files, our proposed design was able to classify the three types of data with high accuracy and high precision with neither false positives nor false negatives. The proposed method comprises simple and fast pre-processing of raw data of a fixed length for each network data package and yields perfect results with 100\% accuracy for the three data types considered. The research is significant in the context of network security and intrusion detection systems. Work is under way for real time recognition and fine-tuning the differentiation between various shellcodes

Detecting Network-Based Obfuscated Code Injection Attacks Using Sandboxing

Intrusion detection systems (IDSs) are widely recognised as the last line of defence often used to enable incident response when intrusion prevention mechanisms are ineffective, or have been compromised. A signature based network IDS (NIDS) which operates by comparing network traffic to a database of suspicious activity patterns (known as signatures) is a popular solution due to its ease of deployment and relatively low false positive (incorrect alert) rate. Lately, attack developers have focused on developing stealthy attacks designed to evade NIDS. One technique used to accomplish this is to obfuscate the shellcode (the executable component of an attack) so that it does not resemble the signatures the IDS uses to identify the attacks but is still logically equivalent to the clear-text attacks when executed. We present an approach to detect obfuscated code injection attacks, an approach which compensates for efforts to evade IDSs. This is achieved by executing those network traffic segments that are judged potentially to contain executable code and monitoring the execution to detect operating system calls which are a necessary component of any such code. This detection method is based not on how the injected code is represented but rather on the actions it performs. Correct configuration of the IDS at deployment time is crucial for correct operation when this approach is taken, in particular, the examined executable code must be executed in an environment identical to the execution environment of the host the IDS is monitoring with regards to both operating system and architecture. We have implemented a prototype detector that is capable of detecting obfuscated shellcodes in a Linux environment, and demonstrate how it can be used to detect new or previously unseen code injection attacks and obfuscated attacks as well as well known attacks

STAND: Sanitization Tool for ANomaly Detection

The efficacy of Anomaly Detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic view of the deployment environment. Most realistic data sets are dirty; that is, they contain a number of attacks or anomalous events. The size of these high-quality training data sets makes manual removal or labeling of attack data infeasible. As a result, sensors trained on this data can miss attacks and their variations. We propose extending the training phase of AD sensors (in a manner agnostic to the underlying AD algorithm) to include a sanitization phase. This phase generates multiple models conditioned on small slices of the training data. We use these "micro-models"Â to produce provisional labels for each training input, and we combine the micro-models in a voting scheme to determine which parts of the training data may represent attacks. Our results suggest that this phase automatically and significantly improves the quality of unlabeled training data by making it as "attack-free"Â and "regular"Â as possible in the absence of absolute ground truth. We also show how a collaborative approach that combines models from different networks or domains can further refine the sanitization process to thwart targeted training or mimicry attacks against a single site

Application of a Layered Hidden Markov Model in the Detection of Network Attacks

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload\u27s contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates

Analysis and Detection of Heap-based Malwares Using Introspection in a Virtualized Environment

Malware detection and analysis is a major part of computer security. There is an arm race between security experts and malware developers to develop various techniques to secure computer systems and to find ways to circumvent these security methods. In recent years process heap-based attacks have increased significantly. These attacks exploit the system under attack via the heap, typically by using a heap spraying attack. The main drawback with existing techniques is that they either consume too many resources or are complicated to implement. Our work in this thesis focuses on new methods which offloads process heap analysis for guest Virtual Machines (VM) to the privileged domain using Virtual Machine Introspection (VMI) in a Cloud environment. VMI provides us with a seamless, non-intrusive and invisible (to malwares) way of observing the memory and state of VMs without raising red flags for the malwares