Voting is the process through which a democratic society determines its government. Therefore, voting systems are as important as other well-known critical systems, such as air traffic control systems or nuclear plant monitors. Unfortunately, voting systems have a history of failures that seems to indicate that their quality is not up to the task. Because of the alarming frequency and impact of the malfunctions of voting systems, in recent years a number of vulnerability analysis exercises have been carried out against voting systems to determine if they can be compromised in order to control the results of an election. We have participated in two such large-scale projects, sponsored by the Secretaries of State of California and Ohio, whose goals were to perform the security testing of the electronic voting systems used in their respective states. As the result of the testing process, we identified major vulnerabilities in all of the systems analyzed. We then took advantage of a combination of these vulnerabilities to generate a series of attacks that would spread across the voting systems and would “steal” votes by combining voting record tampering with social engineering approaches. As a response to the two large-scale security evaluations, the Secretaries of State of California and Ohio recommended changes to improve the security of the voting process. In this paper, we describe the methodology that we used in testing the two real-world electronic voting systems we evaluated, the findings of our analysis, our attacks, and the lessons we learned.

Web-based applications have become a popular means of exposing functionality to large numbers of users by leveraging the services provided by web servers and databases. The wide proliferation of custom-developed web-based applications suggests that a

Electronic voting systems play a critical role in today's democratic societies, as they are responsible for recording and counting the citizens' votes. Unfortunately, there is an alarming number of reports describing the malfunctioning of these systems, suggesting that their quality is not up to the task. Recently, there has been a focus on the security testing of voting systems to determine if they can be compromised in order to control the results of an election. We have participated in two large-scale projects, sponsored by the Secretaries of State of California and Ohio, whose respective goals were to perform the security testing of the electronic voting systems used in those two states. The testing process identified major flaws in all the systems analyzed, and resulted in substantial changes in the voting procedures of both states. In this paper, we describe the testing methodology that we used in testing two real-world electronic voting systems, the findings of our analysis, and the lessons we learned.

Intrusion detection systems (IDSs) are used to detect traces of malicious activities targeted against the network and its resources. Anomaly-based IDSs build models of the expected behavior of applications by analyzing events that are generated during the applications' normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, on the assumption that anomalies represent evidence of an attack. Host-based anomaly detection systems often rely on system call sequences to characterize the normal behavior of applications. Recently, it has been shown how these systems can be evaded by launching attacks that execute legitimate system call sequences. The evasion is possible because existing techniques do not take into account all available features of system calls. In particular, system call arguments are not considered. We propose two primary improvements upon existing host-based anomaly detectors. First, we apply multiple detection models to system call arguments. Multiple models allow the arguments of each system call invocation to be evaluated from several different perspectives. Second, we introduce a sophisticated method of combining the anomaly scores from each model into an overall aggregate score. The combined anomaly score determines whether an event is part of an attack. Individual anomaly scores are often contradicting and, therefore, a simple weighted sum cannot deliver reliable results. To address this problem, we propose a technique that uses Bayesian networks to perform system call classification. We show that the analysis of system call arguments and the use of Bayesian classification improves detection accuracy and resilience against evasion attempts. In addition, the paper describes a tool based on our approach and provides a quantitative evaluation of its performance in terms of both detection effectiveness and overhead. A comparison with four related approaches is also presented.

Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Even though the correlation process is often presented as a single step, the analysis is actually carried out by a number of components, each of which has a specific goal. Unfortunately, most approaches to correlation concentrate on just a few components of the process, providing formalisms and techniques that address only specific correlation issues. This paper presents a general correlation model that includes a comprehensive set of components and a framework based on this model. A tool using the framework has been applied to a number of well-known intrusion detection data sets to identify how each component contributes to the overall goals of correlation. The results of these experiments show that the correlation components are effective in achieving alert reduction and abstraction. They also show that the effectiveness of a component depends heavily on the nature of the data set analyzed.

Intrusion detection systems (IDSs) attempt to identify attacks by comparing collected data to predefined signatures known to be malicious (misuse-based IDSs) or to a model of legal behavior (anomaly-based IDSs). Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behavior, which may result in a large number of false alarms. Almost all current anomaly-based intrusion detection systems classify an input event as normal or anomalous by analyzing its features, utilizing a number of different models. A decision for an input event is made by aggregating the results of all employed models. We have identified two reasons for the large number of false alarms, caused by incorrect classification of events in current systems. One is the simplistic aggregation of model outputs in the decision phase. Often, only the sum of the model results is calculated and compared to a threshold. The other reason is the lack of integration of additional information into the decision process. This additional information can be related to the models, such as the confidence in a model's output, or can be extracted from external sources. To mitigate these shortcomings, we propose an event classification scheme that is based on Bayesian networks. Bayesian networks improve the aggregation of different model outputs and allow one to seamlessly incorporate additional information. Experimental results show that the accuracy of the event classification process is significantly improved using our proposed approach.

Intrusion detection systems are distributed applications that analyze the events in a networked system to identify malicious behavior. The analysis is performed using a number of attack models (or signatures) that are matched against a specific event stream. Intrusion detection systems may operate in heterogeneous environments, analyzing different types of event streams. Currently, intrusion detection systems and the corresponding attack modeling languages are developed following an ad hoc approach to match the characteristics of specific target environments. As the number of systems that have to be protected increases, this approach results in increased development effort. To overcome this limitation, we developed a framework, called STAT, that supports the development of new intrusion detection functionality in a modular fashion. The STAT framework can be extended following a well-defined process to implement intrusion detection systems tailored to specific environments, platforms, and event streams. The STAT framework is novel in the fact that the extension process also includes the extension of the attack modeling language. The resulting intrusion detection systems represent a software family whose members share common attack modeling features and the ability to reconfigure their behavior dynamically.

Security analysis should take advantage of a reliable knowledge base that contains semantically-rich information about a protected network. This knowledge is provided by network mapping tools. These tools rely on models to represent the entities of interest, and they leverage off network discovery techniques to populate the model structure with the data that is pertinent to a specific target network. Unfortunately, existing tools rely on incomplete data models. Networks are complex systems and most approaches oversimplify their target models in an effort to limit the problem space. In addition, the techniques used to populate the models are limited in scope and are difficult to extend. This paper presents NetMap, a security tool for network modeling, discovery, and analysis. NetMap relies on a comprehensive network model that is not limited to a specific network level; it integrates network information throughout the layers. The model contains information about topology, infrastructure, and deployed services. In addition, the relationships among different entities in different layers of the model are made explicit. The modeled information is managed by using a suite of composable network tools that can determine various aspects of network configurations through scanning techniques and heuristics. Tools in the suite are responsible for a single, well-defined task.

As networks become faster there is an emerging need for security, analysis techniques that can keep up with the increased network throughput. Existing network-based intrusion detection sensors can barely, keep up with bandwidths of a few hundred Mbps. Analysis tools that can deal with higher throughput are unable to maintain state between different steps of an attack or they are limited to the analysis of packet headers. We propose a partitioning approach to network security, analysis that supports in-depth, stateful intrusion detection on high-speed links. The approach is centered around a slicing mechanism that divides the overall network traffic into subsets of manageable size. The traffic partitioning is done so that a single slice contains all the evidence necessary to detect a specific attack, making sensor-to-sensor interactions unnecessary. This paper describes the approach and presents a first experimental evaluation of its effectiveness.