23,232 research outputs found
On the Feasibility of Transfer-learning Code Smells using Deep Learning
Context: A substantial amount of work has been done to detect smells in
source code using metrics-based and heuristics-based methods. Machine learning
methods have been recently applied to detect source code smells; however, the
current practices are considered far from mature. Objective: First, explore the
feasibility of applying deep learning models to detect smells without extensive
feature engineering, just by feeding the source code in tokenized form. Second,
investigate the possibility of applying transfer-learning in the context of
deep learning models for smell detection. Method: We use existing metric-based
state-of-the-art methods for detecting three implementation smells and one
design smell in C# code. Using these results as the annotated gold standard, we
train smell detection models on three different deep learning architectures.
These architectures use Convolution Neural Networks (CNNs) of one or two
dimensions, or Recurrent Neural Networks (RNNs) as their principal hidden
layers. For the first objective of our study, we perform training and
evaluation on C# samples, whereas for the second objective, we train the models
from C# code and evaluate the models over Java code samples. We perform the
experiments with various combinations of hyper-parameters for each model.
Results: We find it feasible to detect smells using deep learning methods. Our
comparative experiments find that there is no clearly superior method between
CNN-1D and CNN-2D. We also observe that performance of the deep learning models
is smell-specific. Our transfer-learning experiments show that
transfer-learning is definitely feasible for implementation smells with
performance comparable to that of direct-learning. This work opens up a new
paradigm to detect code smells by transfer-learning especially for the
programming languages where the comprehensive code smell detection tools are
not available
Crime Scene Re-investigation: A Postmortem Analysis of Game Account Stealers' Behaviors
As item trading becomes more popular, users can change their game items or
money into real money more easily. At the same time, hackers turn their eyes on
stealing other users game items or money because it is much easier to earn
money than traditional gold-farming by running game bots. Game companies
provide various security measures to block account- theft attempts, but many
security measures on the user-side are disregarded by users because of lack of
usability. In this study, we propose a server-side account theft detection
system base on action sequence analysis to protect game users from malicious
hackers. We tested this system in the real Massively Multiplayer Online Role
Playing Game (MMORPG). By analyzing users full game play log, our system can
find the particular action sequences of hackers with high accuracy. Also, we
can trace where the victim accounts stolen money goes.Comment: 7 pages, 8 figures, In Proceedings of the 15th Annual Workshop on
Network and Systems Support for Games (NetGames 2017
Does Canada Have a Problem with Occupational Fraud?
Small and medium-sized enterprises (SMEs) are an important collective force in the Canadian economy, however the visibility and economic power of small businesses suffer due to their size and frequent turnover. When it comes to the issue of businesses being subject to occupational fraud, the moderate visibility of SMEs only contributes to the challenge of assessing the real scope of the problem. This paper seeks to examine the prevalence and types of occupational fraud experienced by Canadian SMEs as well as gathers information on prevention and detection methods used to safeguard against occupational fraud. That is done based on data compiled from a survey of 802 SMEs across Canada. The analysis shows that a substantial proportion of SMEs experience incidents of occupational fraud; however, the majority of SMEs are not fully prepared to respond to fraud. Furthermore, SMEs’ experience with and attitudes toward fraud vary noticeably with company characteristics, although a large proportion of SMEs believe risk to occupational fraud is low.Occupational fraud, fraud prevention, fraud detection, types of occupational fraud, Canadian small and medium businesses, employee fraud, internal fraud
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de EconomĂa y Competitividad DPI2015-70842-
Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery
Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together
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