29 research outputs found
Towards Certification of Machine Learning-Based Distributed Systems
Machine Learning (ML) is increasingly used to drive the operation of complex
distributed systems deployed on the cloud-edge continuum enabled by 5G.
Correspondingly, distributed systems' behavior is becoming more
non-deterministic in nature. This evolution of distributed systems requires the
definition of new assurance approaches for the verification of non-functional
properties. Certification, the most popular assurance technique for system and
software verification, is not immediately applicable to systems whose behavior
is determined by Machine Learning-based inference. However, there is an
increasing push from policy makers, regulators, and industrial stakeholders
towards the definition of techniques for the certification of non-functional
properties (e.g., fairness, robustness, privacy) of ML. This article analyzes
the challenges and deficiencies of current certification schemes, discusses
open research issues and proposes a first certification scheme for ML-based
distributed systems.Comment: 5 pages, 1 figure, 1 tabl
Toward Sensor-Based Context Aware Systems
This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach
Machine learning is becoming ubiquitous. From finance to medicine, machine
learning models are boosting decision-making processes and even outperforming
humans in some tasks. This huge progress in terms of prediction quality does
not however find a counterpart in the security of such models and corresponding
predictions, where perturbations of fractions of the training set (poisoning)
can seriously undermine the model accuracy. Research on poisoning attacks and
defenses received increasing attention in the last decade, leading to several
promising solutions aiming to increase the robustness of machine learning.
Among them, ensemble-based defenses, where different models are trained on
portions of the training set and their predictions are then aggregated, provide
strong theoretical guarantees at the price of a linear overhead. Surprisingly,
ensemble-based defenses, which do not pose any restrictions on the base model,
have not been applied to increase the robustness of random forest models. The
work in this paper aims to fill in this gap by designing and implementing a
novel hash-based ensemble approach that protects random forest against
untargeted, random poisoning attacks. An extensive experimental evaluation
measures the performance of our approach against a variety of attacks, as well
as its sustainability in terms of resource consumption and performance, and
compares it with a traditional monolithic model based on random forest. A final
discussion presents our main findings and compares our approach with existing
poisoning defenses targeting random forests.Comment: 15 pages, 8 figure
Object Counting in Remote Sensing via Triple Attention and Scale-Aware Network
Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been barely studied compared with object counting in natural images due to the challenging factors, e.g., background clutter and scale variation. This paper proposes a triple attention and scale-aware network (TASNet). Specifically, a triple view attention (TVA) module is adopted to remedy the background clutter, which executes three-dimension attention operations on the input tensor. In this case, it can capture the interaction dependencies between three dimensions to distinguish the object region. Meanwhile, a pyramid feature aggregation (PFA) module is employed to relieve the scale variation. The PFA module is built in a four-branch architecture, and each branch has a similar structure composed of dilated convolution layers to enlarge the receptive field. Furthermore, a scale transmit connection is introduced to enable the lower branch to acquire the upper branch’s scale, increasing the output’s scale diversity. Experimental results on remote sensing datasets prove that the proposed model can address the issues of background clutter and scale variation. Moreover, it outperforms the state-of-the-art (SOTA) competitors subjectively and objectively
Facial identification problem: A tracking based approach
This paper presents a method for face identification using a query by example approach. Our technique is suitable for use within Ambient Security Environments and is robust across variations in pose, expression and illuminations conditions. To account for these variations, we use a face template matching algorithm based on a 3D head model created from a single frontal face image. Thanks to our tracking-based approach our algorithm is able to extract simultaneously all parameters related to the face expression and to the 3D posture. With these estimates, we are able to reconstruct a frontal, neutral and normalized image on which dissimilarity analysis for identification and anomalies detection is performed. Our tracking process combined with dissimilarity analysis was tested on Kanade-Cohn database [13] for expression independent identification and several other experimental databases for robustness. 1
A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT
5G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective lowlatency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system and introduces new powerful attack vectors leading to severe security and data privacy risks. Malware attacks are starting targeting weak but highly connected IoT devices showing the importance of security and privacy in this scenario. This article designs a 5G-enabled system, consisted in a deep learning based architecture aimed to classify malware attacks on the IIoT. Our methodology is based on an image representation of the malware and a convolutional neural networks that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy
Real-time signal processing in embedded systems
International audienc