The acoustic emanations from keyboards provide a side-channel attack from which an attacker can recover sensitive user information, such as passwords and personally identifiable information. Previous work has shown the feasibility of these attacks given isolated key strokes, but has not demonstrated robust keystroke detection and segmentation in the presence of realistic noise and fast typing speeds. Common problems include noises like doors closing or speech as well as overlapping keystroke waveforms. Prior work has assumed that isolating the waveform of individual key strokes can be achieved with near 100% accuracy, but we show that these techniques generate a large number of misses and false positives, drastically impacting the downstream keystroke classification task.

Descriptive comments play a crucial role in the software engineering process. They decrease development time, enable better bug detection, and facilitate the reuse of previously written code. However, comments are commonly the last of a software developer’s priorities and are thus either insufficient or missing entirely. Automatic source code summarization may therefore have the ability to significantly improve the software development process. We introduce a novel encoder-decoder model that summarizes source code, effectively writing a comment to describe the code’s functionality. We make two primary innovations beyond current source code summarization models. First, our encoder is fully language-agnostic and requires no complex input preprocessing. Second, our decoder has an open vocabulary, enabling it to predict any word, even ones not seen in training. We demonstrate results comparable to state-of-the-art methods on a single-language data set and provide the first results on a data set consisting of multiple programming languages.

Many software analysis methods have come to rely on machine learning approaches.Code segmentation -the process of decomposing source code into meaningful blockscan augment these methods by featurizing code, reducing noise, and limiting the problem space.Traditionally, code segmentation has been done using syntactic cues; current approaches do not intentionally capture logical content.We develop a novel deep learning approach to generate logical code segments regardless of the language or syntactic correctness of the code.Due to the lack of logically segmented source code, we introduce a unique data set construction technique to approximate ground truth for logically segmented code.Logical code segmentation can improve tasks such as automatically commenting code, detecting software vulnerabilities, repairing bugs, labeling code functionality, and synthesizing new code.

We address the problem of allowing authorized users, who have yet to establish a secret key, to securely and efficiently exchange key establishment messages over an insecure channel in the presence of jamming and message insertion attacks. This problem was first introduced by Strasser, Pöpper, Čapkun, and Čagalj in their recent work, leaving joint consideration of security and efficiency as an open problem. In this paper, we present three approaches based on coding theory which reduce the overall time required to verify the packets and reconstruct the original message in the presence of jamming and malicious insertion. We first present the Hashcluster scheme which reduces the total overhead included in the short packets. We next present the Merkleleaf scheme which uses erasure coding to reduce the average number of packet receptions required to reconstruct the message. We then present the Witnesscode scheme which uses one-way accumulators to individually verify packets and reduce redundancy. We demonstrate through analysis and simulation that our candidate protocols can significantly decrease the amount of time required for key establishment in comparison to existing approaches without degrading the guaranteed level of security.

Joint analysis of security and routing protocols in wireless networks reveals vulnerabilities of secure network traffic that remain undetected when security and routing protocols are analyzed independently. We formulate a class of continuous metrics to evaluate the vulnerability of network traffic as a function of security and routing protocols used in wireless networks. We develop two complementary vulnerability definitions using set theoretic and circuit theoretic interpretations of the security of network traffic, allowing a network analyst or an adversary to determine weaknesses in the secure network. We formalize node capture attacks using the vulnerability metric as a nonlinear integer programming minimization problem and propose the GNAVE algorithm, a Greedy Node capture Approximation using Vulnerability Evaluation. We discuss the availability of security parameters to the adversary and show that unknown parameters can be estimated using probabilistic analysis. We demonstrate vulnerability evaluation using the proposed metrics and node capture attacks using the GNAVE algorithm through detailed examples and simulation.

We investigate the impact of node capture attacks on the confidentiality and integrity of network traffic. We map the compromise of network traffic to the flow of current through an electric circuit and propose a metric for quantifying the vulnerability of the traffic using the circuit mapping. We compute the vulnerability metric as a function of the routing and the cryptographic protocols used to secure the network traffic. We formulate the minimum cost node capture attack problem as a nonlinear integer programming problem. Due to the NP-hardness of the minimization problem, we provide a greedy heuristic that approximates the minimum cost attack. We provide examples of node capture attacks using our vulnerability metric and show that the adversary can expend significantly less resources to compromise target traffic by exploiting information leakage from the routing and cryptographic protocols.

We present a framework for throughput optimization for multipath unicast routing in wireless networks in the presence of probabilistic jamming. The framework introduces a statistical characterization into the maximum network flow problem to compensate for the reduction in network flow due to the loss of jammed packets. We map the problem of throughput optimization under probabilistic jamming to that of optimal investment portfolio selection, treating the network throughput as the return on financial investments and using a common portfolio selection framework from financial statistics. Based on the portfolio selection framework, we present approaches to maximize expected throughput and to minimize throughput variance. We include both a detailed example and a simulation study to illustrate the application of the throughput optimization framework.

We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.

An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. We analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. We show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. We illustrate the performance of material identification over different viewing angles using simulated forest hyperspectral images.

An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. The authors analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. The authors show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. They illustrate the performance of material identification over different viewing angles using simulated forest and desert hyperspectral digital imagery collection experiment (HYDICE) images.

The spectral radiance measured for a material by an airborne hyperspectral sensor depends strongly on. The illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely exclusively on the information contained in hyperspectral image data. In this paper we use a comprehensive physical model to show that the set of observed 0.4-2.5 /spl mu/m spectral radiance vectors for a material lies in a lour-dimensional subspace of the hyperspectral measurement space. The physical model captures the dependence of reflected sunlight, reflected skylight, and path radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximum likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions.

Automated material classification from airborne imagery is an important capability for many applications including target recognition and geospatial database construction. Hyperspectral imagery provides a rich source of information for this purpose but utilization is complicated by the variability in a material's observed spectral signature due to the ambient conditions and the scene geometry. In this paper, we present a method that uses a single spectral radiance function measured from a material under unknown conditions to synthesize a comprehensive set of radiance spectra that corresponds to that material over a wide range of conditions. This set of radiance spectra can be used to build a hyperspectral subspace representation that can be used for material identification over a wide range of circumstances. We demonstrate the use of these algorithms for model synthesis and material mapping using HYDICE imagery acquired at Fort Hood, Texas. The method correctly maps several classes of roofing materials, roads, and vegetation over significant spectral changes due to variation in surface orientation. We show that the approach outperforms methods based on direct spectral comparison.

The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth's surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of material-identification algorithms that rely on hyperspectral image data without associated ground-truth information. In this paper, the authors use a comprehensive physical model to show that the set of observed 0.4-2.5 /spl mu/m spectral-radiance vectors for a material lies in a low-dimensional subspace of the hyperspectral-measurement space. The physical model captures the dependence of the reflected sunlight, reflected skylight, and path-radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, they develop a local maximum-likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. The authors show that the low dimensionality of material subspaces allows for the robust discrimination of a large number of materials over a wide range of conditions. They demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions.

The spectral properties of outdoor illumination functions can vary significantly due to atmospheric conditions and scene geometry. The authors show using a statistical analysis of a comprehensive physical model that the variation in outdoor illumination functions over both the visible range (0.33 /spl mu/m-0.7 /spl mu/m) and the visible/near-infrared range (0.4 /spl mu/m-2.5 /spl mu/m) can be represented accurately by use of seven-dimensional linear models. The physical model includes solar and scattered radiation as well as the effects of atmospheric gases and aerosols. The MODTRAN 3.5 code was employed for computing radiative transfer aspects of the model. The authors show that the new model has strong agreement over the visible wavelengths with the empirical study of Judd et al. (1964). The authors also demonstrate the accuracy of the model over the 0.4 /spl mu/m-2.5 /spl mu/m spectral range using measured outdoor illumination functions.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3-D tissue sample is reconstructed using registered neuron regions from each section. An object-oriented database management system provides an efficient framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.

We derive a sensitivity analysis for moment invariants of multidimensional distributions. These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in color images. In this context, the sensitivity analysis predicts the response of moment invariants to partial occlusion. Using the results of the sensitivity analysis, we develop a novel surface representation called the invariant profile which captures color distribution and spatial information while remaining invariant to the spectral content of the scene illumination. Unlike previous representations, the recognition of invariant profiles does not require illumination correction. We demonstrate the sensitivity analysis and the use of invariant profiles for recognition with a set of experiments on color images.

The availability of multiple spectral measurements at each pixel in an image provides important additional information for recognition. Spectral information is of particular importance for applications where spatial information is limited. Such applications include the recognition of small objects or the recognition of small features on partially occluded objects. We introduce a feature matrix representation for deterministic local structure in color images. Although feature matrices are useful for recognition, this representation depends on the spectral properties of the scene illumination. Using a linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that changes in the spectral content of the illumination correspond to linear transformations of the feature matrices, and that image plane rotations correspond to circular shifts of the matrices. From these relationships, we derive an algorithm for the recognition of local surface structure which is invariant to these scene transformations. We demonstrate the algorithm with a series of experiments on images of real objects.

Spatial filters provide a useful and efficient means of analyzing an input color image into components that capture different spatial properties. Representations based on spatial filtering have restricted usefulness for recognition, however, because the output of a spatial filter across an image depends on the scene illumination conditions. We use a physically accurate linear model for spectral reflectance to derive invariants of distributions in spatially filtered color images that do not depend on the scene illumination. These invariants can be used for the illumination-invariant recognition of regions following an arbitrary linear filtering operation. We describe a method for illumination correction based on color distributions and introduce an illumination change consistency constraint that is useful for verifying matches obtained using the invariants. We show, using a set of classification experiments, that the filtered distribution invariants can significantly improve the capability of a recognition system in environments where illumination cannot be controlled.

We represent local spatial structure in a color image using feature matrices that are computed from an image region. Feature matrices contain significantly more information about local image structure than previous representations. Although feature matrices are useful for surface recognition, this representation depends on the spectral properties of the scene illumination. Using a finite dimensional linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that illumination changes correspond to linear transformations of the feature matrices and that surface rotations correspond to circular shifts of the matrices. From these relationships we derive an algorithm for illumination and geometry invariant recognition of local surface structure. We demonstrate the algorithm with a series of experiments on images of real objects.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3D tissue sample is reconstructed using registered neuron regions from each section. An object oriented database management system provides an experimental framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.

Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. Using physical models for image formation, the authors derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces including many textured surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. The authors have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. The authors present several examples demonstrating the system's ability to recognize model objects in cluttered scenes independent of object configuration and scene illumination. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses.

Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. We derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. We have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. We present several examples demonstrating the system's ability to recognize model objects in cluttered scenes. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses.>

Color pixel distributions provide a useful cue for object recognition. Recently, for example, a technique called color indexing due to M. Swain and D. Ballard (1991) used color histograms for the efficient recognition of objects from a large database in the presence of changes in scene geometry and occlusion. The effectiveness of this and other approaches that match color distributions, however, depends on the approximate constancy of the scene illumination. In this paper, we develop color histogram descriptors that are invariant to changes in the intensity and spectral distribution of the illumination. We present a set of experiments that demonstrate the effectiveness of these descriptors for object recognition in the presence of changes in illuminant spectral power distribution.>

A real-time system for automatic fire detection using color video input is presented. Such a system has significant advantages over traditional ultraviolet and infrared fire detectors. These include improved detection, fewer false alarms, and additional descriptive information about fire location, size, and growth rate. From the physical properties of fire, algorithms are derived for fire detection based on the spectral, spatial, and temporal properties of fire events. These algorithms are integrated into a system that has been tested successfully on a wide range of fire and false alarm stimuli. Experimental results are presented demonstrating system performance on a burning jet fuel fire.>

Abstract Software maintenance is an important activity, but is often perceived as less challenging than software development. By following a management‐led discipline of data collection and reporting, maintenance control and visibility can be achieved. The discipline aims to promote the maintenance function within an organization and show that it is as demanding as software development. This paper describes a maintenance data collection scheme (MDCS) which was implemented at an industrial site, as a case study. This implementation resulted in: an increase in management control, promotion of maintenance to senior management, systematic recording of faults, and the successful application of a statistical model to the weekly number of maintenance incidents. An incident form was developed to record data on the effort necessary to resolve incidents, the elapsed time, status, category and severity of incidents. It turned out that maintenance incident effort was highly variable. Maintenance reports were prepared for management based on the results of data analysis. These reports addressed the following questions: Is the maintenance effort increasing or decreasing? How fast are maintenance problems being dealt with? Which systems are demanding the most effort? Statistical techniques were used to model maintenance activities. Multivariate techniques were found to be inappropriate for the type of data collected. A log‐normal distribution was fitted to the weekly number of maintenance incidents. This distribution gave a confidence interval of practical use for management.