Abstract not found

Most memory corruption attacks and Internet worms follow a familiar pattern known as the control-data attack. Hence, many defensive techniques are designed to protect program control flow integrity. Although earlier work did suggest the existence of attacks that do not alter control flow, such attacks are generally believed to be rare against real-world software. The key contribution of this paper is to show that non-control-data attacks are realistic. We demonstrate that many real-world applications, including FTP, SSH, Telnet, and HTTP servers, are vulnerable to such attacks. In each case, the generated attack results in a security compromise equivalent to that due to the controldata attack exploiting the same security bug. Non-control-data attacks corrupt a variety of application data including user identity data, configuration data, user input data, and decision-making data. The success of these attacks and the variety of applications and target data suggest that potential attack patterns are diverse. Attackers are currently focused on control-data attacks, but it is clear that when control flow protection techniques shut them down, they have incentives to study and employ non-control-data attacks. This paper emphasizes the importance of future research efforts to address this realistic threat. 1

Model-based intrusion detection compares a process’s execution against a program model to detect intrusion attempts. Models constructed from static program analysis have historically traded precision for efficiency. We address this problem with our Dyck model, the first efficient statically-constructed context-sensitive model. This model specifies both the correct sequences of system calls that a program can generate and the stack changes occurring at function call sites. Experiments demonstrate that the Dyck model is an order of magnitude more precise than a context-insensitive finite state machine model. With null call squelching, a dynamic technique to bound cost, the Dyck model operates in time similar to the contextinsensitive model. We also present two static analysis techniques designed to counter mimicry and evasion attacks. Our branch analysis identifies between 32 % and 64 % of our test programs’ system call sites as affecting control flow via their return values. Interprocedural argument capture of general values recovers 32 % to 69 % more arguments than previously reported techniques. 1.

Intrusion detection systems that monitor sequences of system calls have recently become more sophisticated in defining legitimate application behavior. In particular, additional information, such as the value of the program counter and the configuration of the program’s call stack at each system call, has been used to achieve better characterization of program behavior. While there is common agreement that this additional information complicates the task for the attacker, it is less clear to which extent an intruder is constrained. In this paper, we present a novel technique to evade the extended detection features of state-of-the-art intrusion detection systems and reduce the task of the intruder to a traditional mimicry attack. Given a legitimate sequence of system calls, our technique allows the attacker to execute each system call in the correct execution context by obtaining and relinquishing the control of the application’s execution flow through manipulation of code pointers. We have developed a static analysis tool for Intel x86 binaries that uses symbolic execution to automatically identify instructions that can be used to redirect control flow and to compute the necessary modifications to the environment of the process. We used our tool to successfully exploit three vulnerable programs and evade detection by existing state-of-the-art system call monitors. In addition, we analyzed three real-world applications to verify the general applicability of our techniques.

Learning-based anomaly detection systems build models of the expected behavior of applications by analyzing events that are generated during their normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, given the assumption that anomalies usually represent evidence of an attack.

Abstract. In this paper, we present Anagram, a content anomaly detector that models a mixture of high-order n-grams (n> 1) designed to detect anomalous and “suspicious ” network packet payloads. By using higher-order n-grams, Anagram can detect significant anomalous byte sequences and generate robust signatures of validated malicious packet content. The Anagram content models are implemented using highly efficient Bloom filters, reducing space requirements and enabling privacy-preserving cross-site correlation. The sensor models the distinct content flow of a network or host using a semi-supervised training regimen. Previously known exploits, extracted from the signatures of an IDS, are likewise modeled in a Bloom filter and are used during training as well as detection time. We demonstrate that Anagram can identify anomalous traffic with high accuracy and low false positive rates. Anagram’s high-order n-gram analysis technique is also resilient against simple mimicry attacks that blend exploits with “normal ” appearing byte padding, such as the blended polymorphic attack recently demonstrated in [1]. We discuss randomized n-gram models, which further raises the bar and makes it more difficult for attackers to build precise packet structures to evade Anagram even if they know the distribution of the local site content flow. Finally, Anagram’s speed and high detection rate makes it valuable not only as a standalone sensor, but also as a network anomaly flow classifier in an instrumented fault-tolerant host-based environment; this enables significant cost amortization and the possibility of a “symbiotic ” feedback loop that can improve accuracy and reduce false positive rates over time. 1

A very effective means to evade signature-based intrusion detection systems (IDS) is to employ polymorphic techniques to generate attack instances that do not share a fixed signature. Anomaly-based intrusion detection systems provide good defense because existing polymorphic techniques can make the attack instances look different from each other, but cannot make them look like normal. In this paper we introduce a new class of polymorphic attacks, called polymorphic blending attacks, that can effectively evade byte frequencybased network anomaly IDS by carefully matching the statistics of the mutated attack instances to the normal profiles. The proposed polymorphic blending attacks can be viewed as a subclass of the mimicry attacks. We take a systematic approach to the problem and formally describe the algorithms and steps required to carry out such attacks. We not only show that such attacks are feasible but also analyze the hardness of evasion under different circumstances. We present detailed techniques using PAYL, a byte frequency-based anomaly IDS, as a case study and demonstrate that these attacks are indeed feasible. We also provide some insight into possible countermeasures that can be used as defense. 1

this paper presents a novel anomaly detection approach that takes into account the information contained in system call arguments. We introduce several models that learn the characteristics of legitimate argument values and are capable of finding malicious instances. Based on the proposed models, we developed a host-based intrusion detection system that monitors running applications to identify malicious behavior. The system includes a novel technique for performing Bayesian classification of the outputs of individual detection models. This technique provides an improvement over the nave threshold-based schemes traditionally used to combine model outputs

Abstract. Web-based systems are often a composition of infrastructure components, such as web servers and databases, and of applicationspecific code, such as HTML-embedded scripts and server-side applications. While the infrastructure components are usually developed by experienced programmers with solid security skills, the application-specific code is often developed under strict time constraints by programmers with little security training. As a result, vulnerable web-applications are deployed and made available to the Internet at large, creating easilyexploitable entry points for the compromise of entire networks. Web-based applications often rely on back-end database servers to manage application-specific persistent state. The data is usually extracted by performing queries that are assembled using input provided by the users of the applications. If user input is not sanitized correctly, it is possible to mount a variety of attacks that leverage web-based applications to compromise the security of back-end databases. Unfortunately, it is not always possible to identify these attacks using signature-based intrusion detection systems, because of the ad hoc nature of many web-based applications. Signatures are rarely written for this class of applications due to the substantial investment of time and expertise this would require. We have developed an anomaly-based system that learns the profiles of the normal database access performed by web-based applications using a number of different models. These models allow for the detection of unknown attacks with reduced false positives and limited overhead. In addition, our solution represents an improvement with respect to previous approaches because it reduces the possibility of executing SQL-based mimicry attacks.

Abstract. Anomaly based intrusion detection has been held out as the best (perhaps only) hope for detecting previously unknown exploits. We examine two anomaly detectors based on the analysis of sequences of system calls and demonstrate that the general information hiding paradigm applies in this area also. Given even a fairly restrictive definition of normal behavior, we were able to devise versions of several exploits that escape detection. This is done in several ways: by modifying the exploit so that its manifestations match “normal, ” by making a serious attack have the manifestations of a less serious but similar attack, and by making the attack look like an entirely different attack. We speculate that similar attacks are possible against other anomaly based IDS and that the results have implications for other areas of information hiding. 1