Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems
Unsupervised or unlabeled learning approaches for network anomaly detection have been recently proposed. In particular, recent work on unlabeled anomaly detection focused on high speed classification based on simple payload statistics. For example, PAYL, an anomaly IDS, measures the occurrence frequency in the payload of n-grams. A simple model of normal traffic is then constructed according to this description of the packets' content. It has been demonstrated that anomaly detectors based on payload statistics can be "evaded" by mimicry attacks using byte substitution and padding techniques. In this paper we propose a new approach to construct high speed payload-based anomaly IDS intended to be accurate and hard to evade. We propose a new technique to extract the features from the payload. We use a feature clustering algorithm originally proposed for text classification problems to reduce the dimensionality of the feature space. Accuracy and hardness of evasion are obtained by constructing our anomaly-based IDS using an ensemble of one-class SVM classifiers that work on different feature spaces.

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: Extracting key-substring-group features for text classification
abstract: In many text classification applications, it is appealing to take every document as a string of characters rather than a bag of words. Previous research studies in this area mostly focused on different variants of generative Markov chain models. Although discriminative machine learning methods like Support Vector Machine (SVM) have been quite successful in text classification with word features, it is neither effective nor efficient to apply them straightforwardly taking all substrings in the corpus as features. In this paper, we propose to partition all substrings into statistical equivalence groups, and then pick those groups which are important (in the statistical sense) as features (named key-substring-group features) for text classification. In particular, we propose a suffix tree based algorithm that can extract such features in linear time (with respect to the total number of characters in the corpus). Our experiments on English, Chinese and Greek datasets show that SVM with key-substring-group features can achieve outstanding performance for various text classification tasks.

title: On processing nested queries in distributed object-oriented database systems
abstract: In this paper, we discuss nested query processing in a distributed object-oriented database system. We present three query processing strategies to exploit parallelism in a distributed environment. Then, we review three access methods designed for centralized systems and discuss how they can be applied to the distributed environment. Heuristics for selecting attributes for indexing and determining where to store the indexes are also presented. Finally, a replication strategy is proposed for the path dictionary method.<<ETX>>

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: 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: Efficient agnostic learning of neural networks with bounded fan-in
abstract: We show that the class of two-layer neural networks with bounded fan-in is efficiently learnable in a realistic extension to the probably approximately correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to approximate the neural network which minimizes the expected quadratic error. As special cases, the model allows learning real-valued functions with bounded noise, learning probabilistic concepts, and learning the best approximation to a target function that cannot be well approximated by the neural network. The networks we consider have real-valued inputs and outputs, an unlimited number of threshold hidden units with bounded fan-in, and a bound on the sum of the absolute values of the output weights. The number of computation steps of the learning algorithm is bounded by a polynomial in 1//spl epsiv/, 1//spl delta/, n and B where /spl epsiv/ is the desired accuracy, /spl delta/ is the probability that the algorithm fails, n is the input dimension, and B is the bound on both the absolute value of the target (which may be a random variable) and the sum of the absolute values of the output weights. In obtaining the result, we also extended some results on iterative approximation of functions in the closure of the convex hull of a function class and on the sample complexity of agnostic learning with the quadratic loss function.