Formalizing sensitivity in static analysis for intrusion detection
A key function of a host-based intrusion detection system is to monitor program execution. Models constructed using static analysis have the highly desirable feature that they do not produce false alarms; however, they may still miss attacks. Prior work has shown a trade-off between efficiency and precision. In particular, the more accurate models based upon pushdown automata (PDA) are very inefficient to operate due to non-determinism in stack activity. In this paper, we present techniques for determinizing PDA models. We first provide a formal analysis framework of PDA models and introduce the concepts of determinism and stack-determinism. We then present the VP-Static model, which achieves determinism by extracting information about stack activity of the program, and the Dyck model, which achieves stack-determinism by transforming the program and inserting code to expose program state. Our results show that in run-time monitoring, our models slow execution of our test programs by 1% to 135%. This shows that reasonable efficiency needs not be sacrificed for model precision. We also compare the two models and discover that deterministic PDA are more efficient, although stack-deterministic PDA require less memory.

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: 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: 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: 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: Building text classifiers using positive and unlabeled examples
abstract: We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.

title: Generating Animatable 3D Virtual Humans from Photographs
abstract: We present an easy, practical and efficient full body cloningmethodology. This system utilizes photos taken from the front, side and back of a person in any given imaging envir onment without requiring a special background or a controlled illuminating condition. A seamless generic bo dy specified in the VRML H-Anim 1.1 format is used to generate an individualized virtual human. The system is c of two major components: face-cloning and body-cloning. The face-cloning component uses feature poi nts on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless text ure mapping is generated for 360 o coloring on a 3D polygonal model. The body-cloning component has two step s: (i) feature points specification, which enables automatic silhouette detection in an arbitrary background(ii) two-stage body modification by using feature points and body silhouette respectively. The final integrated huma n model has photo-realistic animatable face, hands, feet and body. The result can be visualized in any VRML compli ant browser.

title: Analysis of A Loss-Resilient Proactive Data Transmission Protocol in Wireless Sensor Networks
abstract: Many of sensor network applications require reliable data communication such that data packets can be delivered to the destination without loss. However, existing reliable transmission techniques either are too costly for resource-constrained sensor networks or have limited capabilities for achieving desirable reliability. In this paper, an effective coding scheme that exploits the tradeoff between redundant data transmission and encoding/decoding complexity is proposed, with an in-depth study on two key design parameters, the degree of repair packets and the number of repair packets. Furthermore, the expected probability of a destination obtaining all data packets under recoverable and permanent failure model for proactive transmission is analyzed, respectively. Simulations have been conducted to verify our theoretical results. The simulation results reveal profound insights in achieving high communication reliability in wireless sensor networks.

title: Question classification using support vector machines
abstract: Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naive Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of-ngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track.

title: Exploiting fine-grain thread level parallelism on the MIT multi-ALU processor
abstract: Much of the improvement in computer performance over the last twenty years has come from faster transistors and architectural advances that increase parallelism. Historically, parallelism has been exploited either at the instruction level with a grain-size of a single instruction or by partitioning applications into coarse threads with grain-sizes of thousands of instructions. Fine-grain threads fill the parallelism gap between these extremes by enabling tasks with run lengths as small as 20 cycles. As this fine-grain parallelism is orthogonal to ILP and coarse threads, it complements both methods and provides an opportunity for greater speedup. This paper describes the efficient communication and synchronization mechanisms implemented in the Multi-ALU Processor (MAP) chip, including a thread creation instruction, register communication, and a hardware barrier. These register-based mechanisms provide 10 times faster communication and 60 times faster synchronization than mechanisms that operate via a shared on chip cache. With a three-processor implementation of the MAP, fine-grain speedups of 1.2-2.1 are demonstrated on a suite of applications.