The ultimate control flow transfer in a Java based smart card

International audienceRecently, researchers published several attacks on smart cards. Among these, software attacks are the most affordable, they do not require specific hardware (laser, EM probe, etc.). Such attacks succeed to modify a sensitive system element which offers access to the smart card assets. To prevent that, smart card manufacturers embed dedicated countermeasures that aim to protect the sensitive system elements. We present a generic approach based on a Control Flow Transfer (CFT) attack to modify the Java Card program counter. This attack is built on a type confusion using the couple of instructions jsr/ret. Evaluated on different Java Cards, this new attack is a generic CFT exploitation that succeeds on each attacked cards. We present several countermeasures proposed by the literature or implemented by smart card designers and for all of them we explain how to bypass them. Then, we propose to use Attack Countermeasure Tree to develop an effective and affordable countermeasure for this attack

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

Detecting and Modeling Polymorphic Shellcode

In this thesis, we address the problem of modeling and detecting polymorphic engines shellcode. By polymorphic engines, we mean programs having the ability to transform any piece of malware into many instances consisting of different code but having the same functionality as the original malware. Typically, polymorphic engines work by encrypting the target malware using various encryption techniques and providing a decryption module in order to execute the newly encrypted instance. Moreover, those engines have the ability to mutate their decryption routine making them unique from one instance to another and hard to detect. Our analysis focuses on polymorphic shellcode, which is shellcode that uses a polymorphic engine to mutate while keeping the original function of the code the same. We propose a new concept of signatures, shape signatures, which cope with the highly mutated nature of those engines. Those signatures try to identify the constant part as well as the mutated part of the deciphering routines. This combination is able to cope with the highly mutated nature of those engines in a much more efficient way compared to traditional signatures used in most intrusion detection systems. The second part of the thesis aims at modeling those polymorphic engines by showing that they exhibit commo

Unsupervised Learning Trojan

This work presents a proof of concept of an Unsupervised Learning Trojan. The Unsupervised Learning Trojan presents new challenges over previous work on the Neural network Trojan, since the attacker does not control most of the environment. The current work will presented an analysis of how the attack can be successful by proposing new assumptions under which the attack can become a viable one. A general analysis of how the compromise can be theoretically supported is presented, providing enough background for practical implementation development. The analysis was carried out using 3 selected algorithms that can cover a wide variety of circumstances of unsupervised learning. A selection of 4 encoding schemes on 4 datasets were chosen to represent actual scenarios under which the Trojan compromise might be targeted. A detailed procedure is presented to demonstrate the attack\u27s viability under assumed circumstances. Two tests of hypothesis concerning the experimental setup were carried out which yielded acceptance of the null hypothesis. Further discussion is contemplated on various aspects of actual implementation issues and real world scenarios where this attack might be contemplated

Performance Metrics for Network Intrusion Systems

Intrusion systems have been the subject of considerable research during the past 33 years, since the original work of Anderson. Much has been published attempting to improve their performance using advanced data processing techniques including neural nets, statistical pattern recognition and genetic algorithms. Whilst some significant improvements have been achieved they are often the result of assumptions that are difficult to justify and comparing performance between different research groups is difficult. The thesis develops a new approach to defining performance focussed on comparing intrusion systems and technologies. A new taxonomy is proposed in which the type of output and the data scale over which an intrusion system operates is used for classification. The inconsistencies and inadequacies of existing definitions of detection are examined and five new intrusion levels are proposed from analogy with other detection-based technologies. These levels are known as detection, recognition, identification, confirmation and prosecution, each representing an increase in the information output from, and functionality of, the intrusion system. These levels are contrasted over four physical data scales, from application/host through to enterprise networks, introducing and developing the concept of a footprint as a pictorial representation of the scope of an intrusion system. An intrusion is now defined as “an activity that leads to the violation of the security policy of a computer system”. Five different intrusion technologies are illustrated using the footprint with current challenges also shown to stimulate further research. Integrity in the presence of mixed trust data streams at the highest intrusion level is identified as particularly challenging. Two metrics new to intrusion systems are defined to quantify performance and further aid comparison. Sensitivity is introduced to define basic detectability of an attack in terms of a single parameter, rather than the usual four currently in use. Selectivity is used to describe the ability of an intrusion system to discriminate between attack types. These metrics are quantified experimentally for network intrusion using the DARPA 1999 dataset and SNORT. Only nine of the 58 attack types present were detected with sensitivities in excess of 12dB indicating that detection performance of the attack types present in this dataset remains a challenge. The measured selectivity was also poor indicting that only three of the attack types could be confidently distinguished. The highest value of selectivity was 3.52, significantly lower than the theoretical limit of 5.83 for the evaluated system. Options for improving selectivity and sensitivity through additional measurements are examined.Stochastic Systems Lt

Using Virtualisation to Protect Against Zero-Day Attacks

Bal, H.E. [Promotor]Bos, H.J. [Copromotor

A Semantics-Based Approach to Malware Detection

Malware detection is a crucial aspect of software security. Current malware detectors work by checking for signatures, which attempt to capture the syntactic characteristics of the machine-level byte sequence of the malware. This reliance on a syntactic approach makes current detectors vulnerable to code obfuscations, increasingly used by malware writers, that alter the syntactic properties of the malware byte sequence without significantly affecting their execution behavior. This paper takes the position that the key to malware identification lies in their semantics. It proposes a semantics-based framework for reasoning about malware detectors and proving properties such as soundness and completeness of these detectors. Our approach uses a trace semantics to characterize the behavior of malware as well as that of the program being checked for infection, and uses abstract interpretation to ``hide'' irrelevant aspects of these behaviors. As a concrete application of our approach, we show that (1) standard signature matching detection schemes are generally sound but not complete, (2) the semantics-aware malware detector proposed byChristodorescu et al. is complete with respect to a number of common obfuscations used by malware writers and (3) the malware detection scheme proposed by Kinder et al. and based on standard model-checking techniques is sound in general and complete on some, but not all, obfuscations handled by the semantics-aware malware detector