JAM: Java Agents for Meta-Learning over Distributed Databases
In this paper, we describe the JAM system, a distributed, scalable and portable agent-based data mining system that employs a general approach to scaling data mining applications that we call meta-learning. JAM provides a set of learning programs, implemented either as JAVA applets or applications, that compute models over data stored locally at a site. JAM also provides a set of meta-learning agents for combining multiple models that were learned (perhaps) at different sites. It employs a special distribution mechanism which allows the migration of the derived models or classifier agents to other remote sites. We describe the overall architecture of the JAM system and the specific implementation currently under development at Columbia University. One of JAM's target applications is fraud and intrusion detection in financial information systems. A brief description of this learning task and JAM's applicability are also described. Interested users may download JAM from http://www.cs.columbia.edu/~sal/JAM/PROJECT.

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: 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: 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: Spatial Index on Air
abstract: With the advent of wireless networking and personal digital devices, the population of mobile users will increase significantly. Broadcasting is particularly suitable for environments having a large number of clients. In this paper, we study the query processing of some typical location-dependent queries, such as window queries and kNN queries, in a broadcast system. To reduce clients' power consumption and provide efficient services, a transformation of the objects is applied based on Hilbert curve. Furthermore, a linear index structure is constructed and several algorithms are devised to answer spatial queries. Experiments are conducted to evaluate the performance of the proposed transformation and related algorithms. Results show that the proposed schemes outperform existing algorithms significantly.

title: Optimal channel allocation for data dissemination in mobile computing environments
abstract: We discuss the wireless channel allocation problem for data dissemination in mobile computing systems. Methods for accessing data through broadcast and on-demand channels are described. We provide analytical models and cost formulae for the exclusive broadcast channels and the exclusive on-demand channels and propose a dynamic channel allocation algorithm for optimizing system performance. Our performance evaluation shows that dynamic channel allocation significantly improves system performance and the channel allocation algorithm gives us the optimal solution for various system parameter settings.

title: Using Signature Techniques for Information Filtering in Wireless and Mobile Environments
abstract: This paper discusses the issue of power conservation on mobile clients, e.g., palmtop, in wireless and mobile environments. It suggests that techniques using signatures are suitable for realtime information filtering on mobile clients. Three signature-based approaches, namely simple signature, integrated signature and multi-level signature schemes, are presented. The cost models for the access time and tune-in time of these three approaches are developed. We show that the multi-level signature method is in general better than the other two methods.

title: Distributed scalable location data management system based on the GALIS architecture
abstract: A challenging task in the LBS system engineering is to implement a highly scalable system architecture which can manage moderate-size configurations handling thousands of moving items as well as upper-end configurations handling millions of moving items. The architecture named the GALIS is a cluster-based distributed computing system architecture that consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. In this paper, we explain a prototype location data management system structuring major components of GALIS by employing the TMO programming scheme, including the execution engine middleware developed to support real-time distributed object programming and real-time distributed computing system design. We present how to generate realistic location sensing reports and how to process such location reports and location-related queries. Some experimental results showing performance factors regarding distributed query processing are also explained.

title: Word Sense Disambiguation with Semi-Supervised Learning
abstract: Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised leaming algorithms are evaluated on 29 nouns of Senseval-2 (SE2) English lexical sample task and SE2 English all-words task. Empirical results show that unlabeled data can bring significant improvement in WSD accuracy.