1402963
MOPS: an infrastructure for examining security properties of software
We describe a formal approach for finding bugs in security-relevant software and verifying their absence. The idea is as follows: we identify rules of safe programming practice, encode them as safety properties, and verify whether these properties are obeyed. Because manual verification is too expensive, we have built a program analysis tool to automate this process. Our program analysis models the program to be verified as a pushdown automaton, represents the security property as a finite state automaton, and uses model checking techniques to identify whether any state violating the desired security goal is reachable in the program. The major advantages of this approach are that it is sound in verifying the absence of certain classes of vulnerabilities, that it is fully interprocedural, and that it is efficient and scalable. Experience suggests that this approach will be useful in finding a wide range of security vulnerabilities in large programs efficiently.
1194024
Hierarchical classification of Web content
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.
9276534
Setuid Demystified
7740275
FIREMAN: a toolkit for firewall modeling and analysis
Security concerns are becoming increasingly critical in networked systems. Firewalls provide important defense for network security. However, misconfigurations in firewalls are very common and significantly weaken the desired security. This paper introduces FIREMAN, a static analysis toolkit for firewall modeling and analysis. By treating firewall configurations as specialized programs, FIREMAN applies static analysis techniques to check misconfigurations, such as policy violations, inconsistencies, and inefficiencies, in individual firewalls as well as among distributed firewalls. FIREMAN performs symbolic model checking of the firewall configurations for all possible IP packets and along all possible data paths. It is both sound and complete because of the finite state nature of firewall configurations. FIREMAN is implemented by modeling firewall rules using binary decision diagrams (BDDs), which have been used successfully in hardware verification and model checking. We have experimented with FIREMAN and used it to uncover several real misconfigurations in enterprise networks, some of which have been subsequently confirmed and corrected by the administrators of these networks
43818812
Piecewise linear modulation model of handwriting
A new piecewise linear modulation model of handwriting is proposed. In this model, the velocity of handwriting trajectory is modeled as the impulse response of a time varying second order system. For mathematical tractability, the entire trajectory is segmented into several non-overlapping frames, while the natural frequency and the damping factor of the system are assumed to vary linearly with time in each frame and are continuous along the entire trajectory. In other words, handwriting is regarded as an oscillation modulated by a continuous and piecewise linear signal. The parameters of this model are estimated by Powell's optimization algorithm which does not require the computation of the first order derivative. The number and lengths of the frames are decided by a modified binary search algorithm along with the estimation of parameters. This model has achieved very high data compression rate as well as accurate reproduction of real handwriting.
4274853
Lifting based discrete wavelet transform architecture for JPEG2000
A lifting based 1-D discrete wavelet transform (DWT) core is proposed. It is re-configurable for 5/3 and 9/7 filters in JPEG2000. Folded architecture is adopted to reduce the hardware cost and achieve the higher hardware utilization. Multiplication is realized in hardwired multiplier with coefficients represented in canonic signed-digit (CSD) form. It is a compact and efficient DWT core for the hardware implementation of JPEG2000 encoder.
32552978
A Knowledge-Based Design for Hypertext-Based Document Retrieval Systems
This paper presents some aspects of an ongoing research that. aims at incorporating knowledge-based search mechanisms into hypertext-based document retrieval systems. We propose an information structure that is grounded on a semantic network-based thesaurus. It serves to represent the documents and concepts (terms) involved in information retrieval. The second important component of our design is heuristics-based search mechanisms that were derived from our prior cognitive modeling study (which investigated the search strategies of expert searchers). By incorporating these components into a navigation-based hypertext, we attempt to create a system that facilitates effective and efficient information retrieval in a complex retrieval environment.
21787077
Information Visualization for Collaborative Computing
Information technology continues to generate increasing amounts of data, most of which is useless without scalable methods to collect, analyze, process, and understand it. Visualization is a promising approach to such systemization because it lets users see underlying processes and guide process simulations interactively. However, visualization must be combined with some way to make repositories of text documents more manageable, providing users with a flexible, interactive environment in which to access them. The article describes a prototype tool that addresses these problems for GroupSystems, an electronic meeting system developed at the University of Arizona and installed at more than 1,500 business, government, and university settings. The tool automatically categorizes information, statistically clusters similar documents, and displays the organized document set graphically, providing more at-a-glance information than a typical text based display. Users can thus more easily browse document collections. The tool uses text analysis techniques that aim to identify descriptors and develop an unambiguous internal representation of a document.
15840540
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
Presented is a model of an integrate-and-fire neuron with active dendrites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested on a neural network with a self-organizing map of competitive neurons. The goal of the presented work is to develop computationally efficient models rather than approximating the real neurons. The approach described in this paper demonstrates the potential advantages of using the processing functionalities of active dendrites as a novel paradigm of computing with networks of artificial spiking neurons.
40127439
Authorship Analysis in Cybercrime Investigation
Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies.