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

MalwareLab: Experimentation with Cybercrime Attack Tools

Cybercrime attack tools (i.e. Exploit Kits) are reportedly responsible for the majority of attacks affecting home users. Exploit kits are traded in the black markets at different prices and advertising different capabilities and functionalities. In this paper we present our experimental approach in testing 10 exploit kits leaked from the markets that we deployed in an isolated environment, our MalwareLab. The purpose of this experiment is to test these tools in terms of resiliency against changing software configurations in time. We present our experiment design and implementation, discuss challenges, lesson learned and open problems, and present a preliminary analysis of the results

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

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

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

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

Binary Exploitation in Industrial Control Systems: Past, Present and Future

Despite being a decades-old problem, binary exploitation still remains a serious issue in computer security. It is mainly due to the prevalence of memory corruption errors in programs written with notoriously unsafe but yet indispensable programming languages like C and C++. For the past 30 years, the nip-and-tuck battle in memory between attackers and defenders has been getting more technical, versatile, and automated. With raised bar for exploitation in common information technology (IT) systems owing to hardened mitigation techniques, and with unintentionally opened doors into industrial control systems (ICS) due to the proliferation of industrial internet of things (IIoT), we argue that we will see an increased number of cyber attacks leveraging binary exploitation on ICS in the near future. However, while this topic generates a very rich and abundant body of research in common IT systems, there is a lack of systematic study targeting this topic in ICS. The present work aims at filling this gap and serves as a comprehensive walkthrough of binary exploitation in ICS. Apart from providing an analysis of the past cyber attacks leveraging binary exploitation on ICS and the ongoing attack surface transition, we give a review of the attack techniques and mitigation techniques on both general-purpose computers and embedded devices. At the end, we conclude this work by stressing the importance of network-based intrusion detection, considering the dominance of resource-constrained real-time embedded devices, low-end embedded devices in ICS, and the limited ability to deploy arbitrary defense mechanism directly on these devices

Honey-copy : a concept and prototype of a generic honeypot system

In this paper, we present Honey-Copy, a concept and prototype for a honeypot system that can pinpoint modifications caused by attacks or intrusion for any honeypot. To achieve this, we track modifications without having to install any additional tools on them. We make use of cloning to identify whether or not a modification has been caused by the honeypot itself or an attacker or intruder. We briefly present our initial prototype and discuss the challenges to be solved toward a more complete and feature rich version of our prototype