131 research outputs found
Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based
algorithms as t-way (where t indicates the interaction strength) and variable-strength
testing strategies. Many AI-based strategies have been developed, such as Ant Colony,
Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most
existing AI-based strategies adopt complex search processes and require heavy
computations. For this reason, existing AI-based strategies have been confined to small
interaction strengths (i.e., t≤3) and small test configurations. Recent studies demonstrate
the need to go up to t=6 in order to capture most faults. This thesis presents the design
and implementation of a new interaction test generation strategy, known as the Particle
Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test
suites. Unlike other existing AI-based strategies, the lightweight computation of the
particle swarm search process enables PSTG to support high interaction strengths of up to
t=6. The performance of PSTG is evaluated using several sets of benchmark experiments.
Comparatively, PSTG consistently outperforms its AI counterparts and other existing
strategies as far as the size of the test suite is concerned. Furthermore, the case study
demonstrates the usefulness of PSTG for detecting faulty interactions of the input
components
A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
Most machine learning methods assume that the input data distribution is the
same in the training and testing phases. However, in practice, this
stationarity is usually not met and the distribution of inputs differs, leading
to unexpected performance of the learned model in deployment. The issue in
which the training and test data inputs follow different probability
distributions while the input-output relationship remains unchanged is referred
to as covariate shift. In this paper, the performance of conventional machine
learning models was experimentally evaluated in the presence of covariate
shift. Furthermore, a region-based evaluation was performed by decomposing the
domain of probability density function of the input data to assess the
classifier's performance per domain region. Distributional changes were
simulated in a two-dimensional classification problem. Subsequently, a higher
four-dimensional experiments were conducted. Based on the experimental
analysis, the Random Forests algorithm is the most robust classifier in the
two-dimensional case, showing the lowest degradation rate for accuracy and
F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results
reveal that in higher-dimensional experiments, the performance of the models is
predominantly influenced by the complexity of the classification function,
leading to degradation rates exceeding 25% in most cases. It is also concluded
that the models exhibit high bias towards the region with high density in the
input space domain of the training samples
IoT Anomaly Detection Methods and Applications: A Survey
Ongoing research on anomaly detection for the Internet of Things (IoT) is a
rapidly expanding field. This growth necessitates an examination of application
trends and current gaps. The vast majority of those publications are in areas
such as network and infrastructure security, sensor monitoring, smart home, and
smart city applications and are extending into even more sectors. Recent
advancements in the field have increased the necessity to study the many IoT
anomaly detection applications. This paper begins with a summary of the
detection methods and applications, accompanied by a discussion of the
categorization of IoT anomaly detection algorithms. We then discuss the current
publications to identify distinct application domains, examining papers chosen
based on our search criteria. The survey considers 64 papers among recent
publications published between January 2019 and July 2021. In recent
publications, we observed a shortage of IoT anomaly detection methodologies,
for example, when dealing with the integration of systems with various sensors,
data and concept drifts, and data augmentation where there is a shortage of
Ground Truth data. Finally, we discuss the present such challenges and offer
new perspectives where further research is required.Comment: 22 page
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