Comparative study between analytical models and packet-level worm simulations
The threat of Internet worms has been, and continues to be, one of the most important issues faced by networking researchers and network users. The need for accurate and efficient modeling and analysis methods cannot be understated. Models that accurately reflect the behavior of existing and yet-to-be deployed worms is critical to understanding how to deal with this ongoing threat. Recently developed analytical models, have been used to generate propagation trends that match with historic worm outbreaks. However in this effort, the values used for some of the parameters are different from empirically measured information, such as probe rate per unit IP address space. Although not found in simpler models, new analytical models are under development that can take into account various network and worm characteristics. But in order to build and test them accurately real world data has been used. In our work, we have focused on packet-level detail in the simulation models, which can take into account realistic network characteristics that include, queuing delay, packet-loss, link delays and also realistic worm characteristics at the expense of additional computational complexity. Using our simulator we show how it can be a useful tool in analyzing and evaluating analytical worm models. We study the worm propagation pattern predicted by one particular analytical model and compare it to our packet-level simulations.

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: 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: 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: 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: 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: Face recognition using computer-generated database
abstract: Many face database and recognition systems have been constructed in specially designed studios with various illuminations, poses, and expressions. However, none of these databases yet satisfies a large variation of poses and illuminations that permit the study of systematic 3D human face information, which results in an unsatisfactory success rate. It is caused primarily by the difficulty of data collection of facial images to satisfy the large variation of poses and illuminations to fully represent the 3D characteristics of human faces. We present how computer-generated database can be used for face recognition experiment focused on multiview face recognition/descriptor. We show multiview face data collection using rendering of 3D models. We also illustrate our approach how to build a face descriptor containing 3D information of human face using multiview concepts. This multiview face recognition descriptor is a 3D descriptor using the concept how much powerful a view influences over nearby views, so called as "quasiview " size

title: Tiling and adaptive image compression
abstract: We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In this task, using a larger number of regions would result in better compression, but would also require more bits for describing the regions and the probability models used in the regions. We discuss using quadtree methods for performing the compression. We introduce a class of probability models for images, the k-rectangular tilings of an image, that is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class of N probability models. For an image of size n/spl times/n, we give a sequential probability assignment algorithm that codes the image with a code length which is within O(k log(Nn/k) of the code length produced by the best probability model in the class. The algorithm has a computational complexity of O(Nn/sup 3/). An interesting subclass of the class of k-rectangular tilings is the class of tilings using rectangles whose widths are powers of two. This class is far more flexible than quadtrees and yet has a sequential probability assignment algorithm that produces a code length that is within O(k log(Nn/k) of the best model in the class with a computational complexity of O(Nn/sup 2/logn) (similar to the computational complexity of sequential probability assignment using quadtrees). We also consider progressive transmission of the coefficients of the image.

title: Boosting the margin: A new explanation for the effectiveness of voting methods
abstract: One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance

title: Supporting XML Security Models Using Relational Databases: A Vision
abstract: As the secure distribution and sharing of information over the World Wide Web becomes increasingly important, the needs for flexible and efficient support of access control systems naturally arise. Since the eXtensible Markup Language (XML) is emerging as the format of the Internet era for storing and exchanging information, there have been, recently, many proposals to extend the XML model to incorporate security aspects. To the lesser or greater extent, however, such proposals neglect the fact that the data for XML documents will most likely reside in relational databases, and consequently do not utilize various security models proposed for and implemented in relational databases.

title: Integration and Analysis of Use Cases Using Modular Petri Nets in Requirements Engineering
abstract: It is well known that requirements engineering plays a critical role in software quality. The use case approach is a requirements elicitation technique commonly used in industrial applications. Software requirements are stated as a collection of use cases, each of which is written in the user's perspective and describes a specific flow of events in the system. The use case approach offers several practical advantages in that use case requirements are relatively easy to describe, understand, and trace. Unfortunately, there are a couple of major drawbacks. Since use cases are often stated in natural languages, they lack formal syntax and semantics. Furthermore, it is difficult to analyze their global system behavior for completeness and consistency, partly because use cases describe only partial behaviors and because interactions among them are rarely represented explicitly. We propose the Constraints-based Modular Petri Nets (CMPNs) approach as an effective way to formalize the informal aspects of use cases. CMPNs, an extension of Place/Transition nets, allow the formal and incremental specification of requirements. The major contributions of the paper, in addition to the formal definitions of CMPNs, are the development of: 1) a systematic procedure to convert use cases stated in natural language to a CMPN model; and 2) a set of guidelines to find inconsistency and incompleteness in CMPNs. We demonstrate an application of our approach using use cases developed for telecommunications services.