Attack Analysis and Detection for Ad Hoc Routing Protocols
Attack analysis is a challenging problem, especially in emerging environments where there are few known attack cases. One such new environment is the Mobile Ad hoc Network (MANET). In this paper, we present a systematic approach to analyze attacks. We introduce the concept of basic events. An attack can be decomposed into certain combinations of basic events. We then define a taxonomy of anomalous basic events by analyzing the basic security goals.

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: 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: 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: 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.

title: Distributed caching of multi-dimensional data in mobile environments
abstract: Caching has been an important technique for saving network traffic and reducing response time, especially in mobile environments where bandwidth is often a scarce resource. In this paper, we propose a novel approach for caching multidimensional data in a cluster of mobile devices. In particular, we focus on the most common types of multi-dimensional queries, namely range and k-nearest neighbor queries, by computing a cacheable region for every query, caching the result at the client, and indexing it in an R*-tree at the cluster gateway. Subsequent queries are first issued to the R*-tree and only remainder queries or queries that cannot be guaranteed exact answers are sent to the remote data server. To the best of our knowledge, our work is the first to study caching results from complex multi-dimensional queries (e.g., kNN query) and propose to build an R*-tree on previously fetched query results in a cluster of mobile devices. Rigorous experiments show that our approach significantly reduces network traffic and response time.

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.

title: RankCut - A Domain Independent Forward Pruning Method for Games
abstract: Forward pruning, also known as selective search, is now employed in many strong game-playing programs. In this paper, we introduce RankCut – a domain independent forward pruning technique which makes use of move ordering, and prunes once no better move is likely to be available. Since gameplaying programs already perform move ordering to improve the performance of αβ search, this information is available at no extra cost. As RankCut uses additional information untapped by current forward pruning techniques, RankCut is a complementary forward pruning method that can be used with existing methods, and is able to achieve improvements even when conventional pruning techniques are simultaneously employed. We implemented RankCut in a modern open-source chess program, CRAFTY. RankCut reduces the game-tree size by approximately 10%-40% for search depths 8-12 while retaining tactical reliability, when implemented alongside CRAFTY’s existing forward pruning techniques.

title: Baring It All to Software: Raw Machines
abstract: The most radical of the architectures that appear in this issue are Raw processors-highly parallel architectures with hundreds of very simple processors coupled to a small portion of the on-chip memory. Each processor, or tile, also contains a small bank of configurable logic, allowing synthesis of complex operations directly in configurable hardware. Unlike the others, this architecture does not use a traditional instruction set architecture. Instead, programs are compiled directly onto the Raw hardware, with all units told explicitly what to do by the compiler. The compiler even schedules most of the intertile communication. The real limitation to this architecture is the efficacy of the compiler. The authors demonstrate impressive speedups for simple algorithms that lend themselves well to this architectural model, but whether this architecture will be effective for future workloads is an open question.