waashk/dblp huggingface dataset:
a unified security framework for networked applications. various security models have been proposed for different types of applications and numerous types of execution environments. these models are typically reinforced by adding code to the application, which authenticates principals, authorises operations and establishes secure communication among distributed software components (e.g., clients and servers). this code is often application and context-specific, which makes it difficult to integrate an application with other each other.in this paper we propose a new unified access control mechanism that supports most of the existing security models and offers a number of additional controls that are not normally provided by security mechanisms. moreover, the proposed mechanism integrates well with existing programming paradigms for distributed application, e.g., client/server technology and component based programming. this means that it can be seamlessly integrated with most existing distributed applications. we have implemented the proposed mechanism in a framework, that can be instantiated to implement different security models and policies. we present a qualitative evaluation that demonstrates the framework's ability to support a wide range of security policies and a preliminary performance evaluation of the framework.
naive bayes vs decision trees in intrusion detection systems. bayes networks are powerful tools for decision and reasoning under uncertainty. a very simple form of bayes networks is called naive bayes, which are particularly efficient for inference tasks. however, naive bayes are based on a very strong independence assumption. this paper offers an experimental study of the use of naive bayes in intrusion detection. we show that even if having a simple structure, naive bayes provide very competitive results. the experimental study is done on kdd'99 intrusion data sets. we consider three levels of attack granularities depending on whether dealing with whole attacks, or grouping them in four main categories or just focusing on normal and abnormal behaviours. in the whole experimentations, we compare the performance of naive bayes networks with one of well known machine learning techniques which is decision tree. moreover, we compare the good performance of bayes nets with respect to existing best results performed on kdd'99.
smart-tv: a fast and scalable nearest neighbor based classifier for data mining. k-nearest neighbors (knn) is the simplest method for classification. given a set of objects in a multi-dimensional feature space, the method assigns a category to an unclassified object based on the plurality of category of the k-nearest neighbors. the closeness between objects is determined using a distance measure, e.g. euclidian distance. despite its simplicity, knn also has some drawbacks: 1) it suffers from expensive computational cost in training when the training set contains millions of objects; 2) its classification time is linear to the size of the training set. the larger the training set, the longer it takes to search for the k-nearest neighbors. in this paper, we propose a new algorithm, called smart-tv (small absolute difference of total variation), that approximates a set of potential candidates of nearest neighbors by examining the absolute difference of total variation between each data object in the training set and the unclassified object. then, the k-nearest neighbors are searched from that candidate set. we empirically evaluate the performance of our algorithm on both real and synthetic datasets and find that smart-tv is fast and scalable. the classification accuracy of smart-tv is high and comparable to the accuracy of the traditional knn algorithm.
cycle embedding in faulty hierarchical cubic networks. a hierarchical cubic network was proposed as an alternative to the hypercube. we use hcn(n) to denote the hierarchical cubic network that contains 2n n-dimensional hypercubes. in this paper, using gray codes, we construct fault-free hamiltonian cycles in an hcn(n) with n-1 link faults. since the hcn(n) is regular of degree n+1, the result is optimal. we also construct longest fault-free cycles of length 22n-1 in an hcn(n) with one node fault, and fault-free cycles of length at least 22n-2f in an hcn(n) with f node faults, where 22n is the number of nodes in the hcn(n), f&le;n-1 if n=3 or 4, and f&le;n if n&ge;5.
verification caching: towards efficient and secure mobile code execution environments. in the mobile code paradigm for distributed systems (as well as in the active networks and agents frameworks), programs from possibly unknown hosts interact with the resources local to the host. while this model offers great potential, it also raises difficult security and performance issues. the mobile code unit should be guaranteed to be safe (not to abuse the resources of the host) in a limited time (since the acquisition of the code happens in real time --- e.g., a java applet). existing host security schemes can be classified as: (i) discretion based: accept certificate of authenticity at your discretion; and (ii) verification based: formally prove the safety. verification provides the desired level of security; however, it comes at a large performance delay while discretion is efficient, but limited and relies on blind trust. we present an optimization, verification caching, for enhancing the performance of verification-based security methods. secure indexing of previously encountered code units is established by using message digest algorithm (e.g., md 5) to generate a fingerprint of the code. we characterize the performance and security of this scheme and investigate optimizations to lower the cost of generating the fingerprint (by indexing on small, partial, fingerprints and generating the full fingerprint only if there is a cache hit). in addition, we generalize the approach to allow multiple trusting nodes to distribute caching among them, sharing experiences and effectively increasing the cache size.
implementation of a proactive load sharing scheme. this paper presents a proactive approach to load sharing and describes the architecture of a scheme, concert, based on this approach. a proactive approach is characterized by a shift of emphasis from reacting to load imbalance to avoiding its occurrence. in contrast, in a reactive load sharing scheme, activity is triggered when a processing node is either overloaded or underloaded. the main drawback of this approach is that a load imbalance is allowed to develop before costly corrective action is taken. concert is a load sharing scheme for loosely-coupled distributed systems. under this scheme, load and task behaviour information is collected and cached in advance of when it is needed. concert uses linux as a platform for development. implemented partially in kernel space and partially in user space, it achieves transparency to users and applications whilst keeping the extent of kernel modifications to a minimum. non-preemptive task transfers are used exclusively, motivated by lower complexity, lower overheads and faster transfers. the goal is to minimize the average response-time of tasks. concert is compared with other schemes by considering the level of transparency it provides with respect to users, tasks and the underlying operating system.
implementing the essence of reflection: a reflective run-time environment. computational reflection provides the developers with a programming mechanism devoted to favorite code extensibility, reuse and maintenance. notwithstanding that, it has not achieved developers' unanimous acceptance and its full potential yet. in our opinion, this depends on the intrinsic complexity of most of the reflective approaches that hinders their efficient implementation. the aim of this paper consists of defining the essence of reflection, that is, to identify the minimal set of characteristics that a software system must have to be considered reflective. the consequence is the realization of a run-time environment supporting the essence of reflection without affecting the programming language and with a minimal impact on the programming system design. this achievement will improve reflective system performances reducing the impact of one of the most diffuse criticism about reflection: low performance.
finding differentially expressed genes: pattern generation using q-values. in this paper, we consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. we developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. we present two algorithms for the second method, one for error minimization and the other for confidence bounding. also, we show how the method called patterns from gene expression (page) [7] can benefit from q-values. finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (brca1 vs brca2 tumor types) are provided.
efficient fault-tolerant routing algorithm for otis-cube using unsafety vectors. for the first time this paper proposes a new fault-tolerant routing algorithm for the well known class of network, otis-cube. in this new proposed algorithm, each node a starts by computing the first level unsafety set, sa1, composed of the set of unreachable direct neighbours. it then performs m-1 exchanges with its neighbours to determine the k-level unsafety sets sak for all 1 &le; k &le; m, where m is an adjustable parameter between 1 and 2n+1. equipped with these unsafety sets we show how each node calculates numeric unsafety vectors and uses them to achieve efficient fault-tolerant routing. the paper presents also a performance analysis through extensive simulation experiments proving the superiority of the proposed algorithm using the set of unsafety vectors by showing how the destination is reached by using these sets of unsafety vectors. the simulation results are obtained in terms of routing distances and percentage of reachability.
a dynamic load distribution strategy for systems under high task variation and heavy traffic. several approaches have been proposed to deal with the issue of load distribution, however they all have similar limitations, such as: (i) tasks are executed in an arbitrary order (which may cause large tasks to be delayed), (ii) the task dispatcher does not take into consideration the server processing capacity (which may cause a large task to be assigned to a server with low processing power) or (iii) they do not consider task deadlines (which if not met, may cause task starvation). this paper proposes an extension of lff (least flow-time first) task assignment policy [9], called lff-priority, to deal with these limitations. lff-priority dynamically computes two priorities, namely task size and task size priorities, and put them in a priority based multi-section queue. the testing results clearly show that lff-priority out performs existing load distribution strategies (that are based on heavy tailed distribution). the testing results also show that more than 80% of tasks meet their task deadline under lff-priority.
using latent semantic indexing to filter spam. past research has explored the effectiveness of a na&iuml;ve bayesian classifier when filtering unsolicited bulk email (spam). results have shown that the degree of precision of this approach is generally superior to the degree of recall. this study evaluates the effectiveness of a classifier incorporating latent semantic indexing (lsi) to filter spam email on corpus used in previous studies. results show that email classifiers using lsi to filter spam enjoy a very high degree of both recall and precision, no matter if the corpus is treated using a stop list or a lemmatizer. while using lsi leads to precision roughly equal to that of using a na&iuml;ve bayesian approach, the lsi technique has a substantially higher recall and is more effective under certain conditions.results show that incorporating lsi into an anti-spam filter is viable, particularly in implementations when misclassified legitimate messages are not arbitrarily deleted. other inferences are drawn to the applicability of this method to other text mining tasks.
smartgate: a smart push-pull approach to support role-based security in web gateways. efficient and effective web gateways or proxy servers are important to control the access privileges of users and protect private networks that are connected to the internet, thus providing a productive and safe web environment. access control in the form of complex access rules based on users or user sets (groups) has been studied extensively. the objective of this work is to provide role-based (rb) security for web gateways utilizing role-based access control (rbac). rb security reduces the administrative burden, provides fine grained access control and supports various constraints such as context-aware and temporal seamlessly.in this paper we elaborate on the problems, issues that need to be addressed, and our approach for providing rb security for web gateways by leveraging the flexibility and expressiveness of rbac. our approach enables the proxy server to act smarter, rather than just allow or deny access based on access rules, meanwhile preserving the principle of least privileges.
neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets. the behavior of spatial objects is under the influence of nearby spatial processes. therefore in order to perform any type of spatial analysis we need to take into account not only the spatial relationships among objects but also the underlying spatial processes and other spatial features in the vicinity that influence the behavior of a given spatial object. in this paper, we address the outlier detection by refining the concept of a neighborhood of an object, which essentially characterizes similarly behaving objects into one neighborhood. this similarity is quantified in terms of the spatial relationships among the objects and other semantic relationships based on the spatial processes and spatial features in their vicinity. these spatial features could be natural such as a stream, and vegetation, or man-made such as a bridge, railroad, and chemical factory. the paper also addresses the identification of spatio-temporal outliers in high dimensions, in their neighborhood.
editorial message: special track on object oriented programming language and systems. today's large scale software systems are typically designed and implemented using the object-oriented (oo) methodology and paradigm. however, there is still a need for existing oo languages and architectures to continuously adapt in response to demands for new features and innovative approaches. a few examples of new features are unanticipated software evolution, security, safety, distribution, and interoperability.
extracting refactoring trends from open-source software and a possible solution to the 'related refactoring' conundrum. refactoring, as a software engineering discipline has emerged over recent years to become an important aspect of maintaining software. refactoring refers to the restructuring of software according to specific mechanics and principles. in this paper, we describe a tool that allows refactoring data across multiple versions of seven open-source software systems to be collected. the tool automates the identification of refactorings as program transformations between consecutive software releases. the same tool thus allowed an empirical analysis of software development across versions from the perspective of those transformations. we describe results for the systems analysed and point to key conclusions from our analysis. in particular, we investigate a problematic empirical question as to whether certain refactorings are related, i.e., they cannot be undertaken in isolation without other refactorings being undertaken in parallel. in this context, we focus specifically on the four most common refactorings identified by the tool from three of the open-source systems and use a dependency graph to inform conclusions about the empirical data extracted by the tool. an interesting result relating to some common refactorings is described.
editorial message: special track on object oriented programming languages and systems. today's large scale software systems are typically designed and implemented using the object-oriented (oo) methodology and paradigm. however, there is still a need for existing oo languages and architectures to continuously adapt in response to demands for new features and innovative approaches. a few examples of new features are unanticipated software evolution, security, safety, distribution, and interoperability.
mobile agent based pervasive systems manager for enterprise network. mobile wireless devices are rapidly becoming part of the personal computing with advances in the broadband wireless networks. these devices though, have serious resource constraints to perform truly complex enterprise tasks such as managing an enterprise network. a distributed adaptive systems infrastructure is needed to enable these mobile devices as extensions of the enterprise-computing platform. this paper presents a pervasive systems manager that enables mobile devices to manage mid sized enterprise networks from anywhere, anytime. mobile agents based pervasive systems manager allows device to initiate complex customized management actions. the paper describes architecture, implementation and experimental results of management action initiation, execution and dispatch of the enterprise systems management information to mobile devices.
topological mapping: a dimensionality reduction method for efficient video search. in this paper, we present a novel approach for efficient search of shots by the colors of objects that exist in the wanted shots. the idea is to map the color histogram, which represents the color content, of each object in the shot from the high-dimensional feature space into a point in a low-dimensional distance space. then, a spatial access method, such as an r-tree, is used to cluster these points based on their distances in the low-dimensional space. our mapping method, called topological mapping, guarantees no false dismissals in the result of a query. however, the result of a query might contain some false alarms. hence, two refinement steps are performed to remove these false alarms. comparative experiments show the superiority in efficiency of the topological mapping method over other methods.
modelling and filtering of mpeg-7-compliant meta-data for digital video. the recent mpeg-7 standard specifies a semi-structured meta-data format for open interoperability of multimedia. however, the standard refrains from specifying how the meta-data is to be used or how meta-data inappropriate to user requirements may be filtered out. consequently, we propose cosmos-7, which produces structured mpeg-7-compliant meta-data for digital video and enables content-based hybrid filtering of that meta-data.
a zkp-based identification scheme for base nodes in wireless sensor networks. most of the published work on authentication mechanisms for wireless sensor networks establishes secure authentication for sensor nodes, but not for the base node that is in fact required to authenticate other nodes in the same network. this situation can lead to an attack whereby a malicious party masquerades as the base station and fraudulently authenticates other legitimate nodes to capture and/or inject messages within the network. the trust assumption in the existing literature with regard to base stations (i.e., implicitly trusting the base station) presents a serious security loophole. we address this problem by proposing a protocol that will help build a base station authentication mechanism in the framework of a one-hop mesh network and later extend it to a multi-hop framework. our network would consist of a commissioning/installation device, and several forests of nodes (a base node and other nodes). the installation device would be responsible for deploying nodes in an area selected and would distribute information to them as necessary. we shall use a modification of the guillou-quisquater identification scheme as our zero-knowledge (zk) protocol in conjunction with the &mu;tesla protocol for authenticated broadcast, to authenticate the base station.
mpeg-7 in action: end user experiences with cosmos-7 front end systems. mpeg-7 has become a key standard to multimedia research in searching, filtering and retrieval. understanding experiences of users when using mpeg-7-based tools is necessary if we are to improve how mpeg-7 metadata is applied in practice. cosmos-7 enables structured modeling and filtering of mpeg-7-compliant metadata for digital video. we describe two cosmos-7 front end systems: cosmosis, for modeling digital video metadata, and the filtering manager, for filtering digital video metadata. we then present an empirical evaluation of these front end systems undertaken with a sample set of end users from a london, uk, theater company. our results reveal that end users progress through a number of key stages when modeling and filtering video content.
selective compilation via fast code analysis and bytecode tracing. modern java virtual machines (jvm) commonly adopt just-in-time (jit) compilation to speed up the execution of java bytecode. however, the effort of compiling a region of code is only worth if the code is frequently executed. therefore, selective compilation is employed so that the jit compiler is only invoked on those regions of code where most of the computation is performed (hot spots). the core task in selective compilation is to correctly identify the hot spots in a program. in our selekaffe prototype virtual machine, we introduce two heuristics aimed at detecting hot spots both statically, via bytecode analysis, and dynamically, via profiling information. experimental results on a representative set of benchmarks show that our method selection strategy is more accurate than known strategies, and not significantly slower.
detection and prediction of distance-based outliers. in this paper we present an unsupervised distance-based outlier detection method designed to learn a model over the objects contained in a data set. the learned model, called solving set, is a small subset of the data set that is used to classify new unseen objects as outliers or not. we provide an algorithm that computes a solving set with sub-quadratic time requirements, and we give experimental evidence that the computed solving set is small and that the false positive rate, i.e. the fraction of new objects misclassified as outliers using the solving set instead of the overall data set, is negligible.
multi-objective co-exploration of source code transformations and design space architectures for low-power embedded systems. the exploration of the architectural design space in terms of energy and performance is of mainly importance for a broad range of embedded platforms based on the system-on-chip approach. this paper proposes a methodology for the co-exploration of the design space composed of architectural parameters and source program transformations. a heuristic technique based on pareto simulated annealing (psa) has been used to efficiently span the multi-objective co-design space composed of the product of the parameters related to the selected program transformations and the configurable architecture. the analysis of the proposed framework has been carried out for a parameterized superscalar architecture executing a selected set of benchmarks. the reported results show the effectiveness of the proposed co-exploration with respect to the independent exploration of the transformation and architectural spaces to efficiently derive approximate pareto curves.