54,157 research outputs found
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
Semantic discovery and reuse of business process patterns
Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse
Optimization of stand-alone photovoltaic system by implementing fuzzy logic MPPT controller
A photovoltaic (PV) generator is a nonlinear device having insolation-dependent
volt-ampere characteristics. Since the maximum-power point varies with solar
insolation, it is difficult to achieve an optimum matching that is valid for all
insolation levels. Thus, Maximum power point tracking (MPPT) plays an
important roles in photovoltaic (PV) power systems because it maximize the
power output from a PV system for a given set of condition, and therefore
maximize their array efficiency. This project presents a maximum power point
tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused
on a comparative study between most conventional controller namely Perturb and
Observe (P&O) algorithm and is compared to a design fuzzy logic controller
(FLC). The introduction of fuzzy controller has given very good performance on
whatever the parametric variation of the system
Identifying the relevance of personal values to e-government portals' success: insights from a Delphi study
Most governments around the world have put considerable financial resources into the development of e-government systems. They have been making significant efforts to provide information and services online. However, previous research shows that the rate of adoption and success of e-government systems vary significantly across countries. It is argued here that culture can be an important factor affecting e- government success. This paper aims to explore the relevance of personal values to the e-government success from an individual user’s perspective. The ten basic values identified by Schwartz were used. A Delphi study was carried out with a group of experts to identify the most relevant personal values to the e-government success from an individual’s point of view. The findings suggest that four of the ten values, namely Self-direction, Security, Stimulation, and Tradition, most likely affect the success. The findings provide a basis for developing a comprehensive e-government evaluation framework to be validated using a large scale survey in Saudi Arabia
What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel NaĂŻve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
On the Activity Privacy of Blockchain for IoT
Security is one of the fundamental challenges in the Internet of Things (IoT)
due to the heterogeneity and resource constraints of the IoT devices. Device
classification methods are employed to enhance the security of IoT by detecting
unregistered devices or traffic patterns. In recent years, blockchain has
received tremendous attention as a distributed trustless platform to enhance
the security of IoT. Conventional device identification methods are not
directly applicable in blockchain-based IoT as network layer packets are not
stored in the blockchain. Moreover, the transactions are broadcast and thus
have no destination IP address and contain a public key as the user identity,
and are stored permanently in blockchain which can be read by any entity in the
network. We show that device identification in blockchain introduces privacy
risks as the malicious nodes can identify users' activity pattern by analyzing
the temporal pattern of their transactions in the blockchain. We study the
likelihood of classifying IoT devices by analyzing their information stored in
the blockchain, which to the best of our knowledge, is the first work of its
kind. We use a smart home as a representative IoT scenario. First, a blockchain
is populated according to a real-world smart home traffic dataset. We then
apply machine learning algorithms on the data stored in the blockchain to
analyze the success rate of device classification, modeling both an informed
and a blind attacker. Our results demonstrate success rates over 90\% in
classifying devices. We propose three timestamp obfuscation methods, namely
combining multiple packets into a single transaction, merging ledgers of
multiple devices, and randomly delaying transactions, to reduce the success
rate in classifying devices. The proposed timestamp obfuscation methods can
reduce the classification success rates to as low as 20%
- …