Towards an Information-Theoretic Framework for Analyzing Intrusion Detection Systems
IDS research still needs to strengthen mathematical foundations and theoretic guidelines. In this paper, we build a formal framework, based on information theory, for analyzing and quantifying the effectiveness of an IDS. We firstly present a formal IDS model, then analyze it following an information-theoretic approach. Thus, we propose a set of information-theoretic metrics that can quantitatively measure the effectiveness of an IDS in terms of feature representation capability, classification information loss, and overall intrusion detection capability. We establish a link to relate these metrics, and prove a fundamental upper bound on the intrusion detection capability of an IDS. Our framework is a practical theory which is data trace driven and evaluation oriented in this area. In addition to grounding IDS research on a mathematical theory for formal study, this framework provides practical guidelines for IDS fine-tuning, evaluation and design, that is, the provided set of metrics greatly facilitates a static/dynamic fine-tuning of an IDS to achieve optimal operation and a fine-grained means to evaluate IDS performance and improve IDS design. We conduct experiments to demonstrate the utility of our framework in practice.

title: Using Labeling to Prevent Cross-Service Attacks Against Smart Phones
abstract: Wireless devices that integrate the functionality of PDAs and cell phones are becoming commonplace, making different types of network services available to mobile applications. However, the integration of different services allows an attacker to cross service boundaries. For example, an attack carried out through the wireless network interface may eventually provide access to the phone functionality. This type of attacks can cause considerable damage because some of the services (e.g., the GSM-based services) charge the user based on the traffic or time of use. In this paper, we demonstrate the feasibility of these attacks by developing a proof-of-concept exploit that crosses service boundaries. To address these security issues, we developed a solution based on resource labeling. We modified the kernel of an integrated wireless device so that processes and files are marked in a way that allows one to regulate the access to different system resources. Labels are set when certain network services are accessed. The labeling is then transferred between processes and system resources as a result of either access or execution. We also defined a language for creating labeling rules, and demonstrated how the system can be used to prevent attacks that attempt to cross service boundaries. Experimental evaluation shows that the implementation introduces little overhead. Our security solution is orthogonal to other protection schemes and provides a critical defense for the growing problem of cell phone viruses and worms

title: Worm detection, early warning and response based on local victim information
abstract: Worm detection systems have traditionally focused on global strategies. In the absence of a global worm detection system, we examine the effectiveness of local worm detection and response strategies. This paper makes three contributions: (1) we propose a simple two-phase local worm victim detection algorithm, DSC (Destination-Source Correlation), based on worm behavior in terms of both infection pattern and scanning pattern. DSC can detect zero-day scanning worms with a high detection rate and very low false positive rate. (2) We demonstrate the effectiveness of early worm warning based on local victim information. For example, warning occurs with 0.19% infection of all vulnerable hosts on Internet when using a /12 monitored network. (3) Based on local victim information, we investigate and evaluate the effectiveness of an automatic real-time local response in terms of slowing down the global Internet worms propagation. (2) and (3) are general results, not specific to certain detection algorithm like DSC. We demonstrate (2) and (3) with both analytical models and packet-level network simulator experiments.

title: A framework for constructing features and models for intrusion detection systems
abstract: Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, and extensible. Given these requirements and the complexities of today's network environments, we need a more systematic and automated IDS development process rather that the pure knowledge encoding and engineering approaches. This article describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Instrusion Detection. This framework uses data mining algorithms to compute activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records taht are processed according to the feature definitions to generate intrusion detection rules.  Evaluation showed that our ID model was one of the best performing of all the participating systems. We also briefly discuss our experience in converting the detection models produced by off-line data mining programs to real-time modules of existing IDSs.

title: Anomaly detection using call stack information
abstract: The call stack of a program execution can be a very good information source for intrusion detection. There is no prior work on dynamically extracting information from the call stack and effectively using it to detect exploits. In this paper we propose a new method to do anomaly detection using call stack information. The basic idea is to extract return addresses from the call stack, and generate an abstract execution path between two program execution points. Experiments show that our method can detect some attacks that cannot be detected by other approaches, while its convergence and false positive performance is comparable to or better than the other approaches. We compare our method with other approaches by analyzing their underlying principles and thus achieve a better characterization of their performance, in particular on what and why attacks will be missed by the various approaches.

title: Statistical Causality Analysis of INFOSEC Alert Data
abstract: With the increasingly widespread deployment of security mechanisms, such as firewalls, intrusion detection systems (IDSs), antiviras software and authentication services, the problem of alert analysis has become very important. The large amount of alerts can overwhelm security administrators and prevent them from adequately understanding and analyzing the security state of the network, and initiating appropriate response in a timely fashion. Recently, several approaches for alert correlation and attack scenario analysis have been proposed. However, these approaches all have limited capabilities in detecting new attack scenarios. In this paper, we study the problem of security alert correlation with an emphasis on attack scenario analysis. In our framework, we use clustering techniques to process low-level alert data into high-level aggregated alerts, and conduct causal analysis based on statistical tests to discover new relationships among attacks. Our statistical causality approach complements other approaches that use hard-coded prior knowledge for pattern matching. We perform a series of experiments to validate our method using DARPA’s Grand Challenge Problem (GCP) datasets, the 2000 DARPA Intrusion Detection Scenario datasets, and the DEF CON 9 datasets. The results show that our approach can discover new patterns of attack relationships when the alerts of attacks are statistically correlated.

title: Partially Supervised Classification of Text Documents
abstract: We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation.

title: Data management in location-dependent information services
abstract: Location-dependent information services (LDISs) answer queries in accordance with the locations the queries are associated with (e.g., the locations from which the queries are issued. The emergence of LDISs is resulted from the convergence of high-speed wireless networks, personal portable devices, and locatining techniques. LDISs have a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurant), and are expected to become an integral part of our daily life. This seminar will provide background and an overview of research on location-dependent information access in mobile and pervasive environments. In particular, it will discuss the following topic areas:

title: A Theoretical Analysis of Query Selection for Collaborative Filtering
abstract: We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast algorithm for a theoretical model of this problem with a provable approximation guarantee, and prove that solving the problem exactly is NP-Hard.

title: Performance evaluation of a wireless hierarchical data dissemination system
abstract: Various techniques have been developed to improve the performance of wireless information services. Techniques such as information broadcasting, caching of frequently accessed data, and point-to-point channels for pull-based data requests are often used to reduce data access time. To efficiently utilize information broadcast, indexing and scheduling schemes are employed for the organization of data broadcast. Most of the studies in the literature focused either on individual technique or a combination of them with some restrictive assumptions. There is no study considering these techniques working together in an integrated manner. In this paper, we propose a dynamic data delivery model for wireless communication environments. An important feature of our model is that data are disseminated through various storage mediums according to the dynamically collected data access patterns. Various results are presented in a set of simulation studies, which give some of the intuitions behind the design of a wireless data delivery system.

title: Symbolic-numeric sparse interpolation of multivariate polynomials
abstract: We consider the problem of sparse interpolation of an approximate multivariate black-box polynomial in floating-point arithmetic. That is, both the inputs and outputs of the black-box polynomial have some error, and all numbers are represented in standard, fixed-precision, floating point arithmetic. By interpolating the black box evaluated at random primitive roots of unity, we give efficient and numerically robust solutions. We note the similarity between the exact Ben-Or/Tiwari sparse interpolation algorithm and the classical Prony's method for interpolating a sum of exponential functions, and exploit the generalized eigenvalue reformulation of Prony's method. We analyze the numerical stability of our algorithms and the sensitivity of the solutions, as well as the expected conditioning achieved through randomization. Finally, we demonstrate the effectiveness of our techniques in practice through numerical experiments and applications.