33 research outputs found
Encryption-agnostic classifiers of traffic originators and their application to anomaly detection
This paper presents an approach that leverages classical machine learning techniques to identify the tools from the packets sniffed, both for clear-text and encrypted traffic. This research aims to overcome the limitations to security monitoring systems posed by the widespread adoption of encrypted communications. By training three distinct classifiers, this paper shows that it is possible to detect, with excellent accuracy, the category of tools that generated the analyzed traffic (e.g., browsers vs. network stress tools), the actual tools (e.g., Firefox vs. Chrome vs. Edge), and the individual tool versions (e.g., Chrome 48 vs. Chrome 68). The paper provides hints that the classifiers are helpful for early detection of Distributed Denial of Service (DDoS) attacks, duplication of entire websites, and identification of sudden changes in users’ behavior, which might be the consequence of malware infection or data exfiltration
Topological Gradient-based Competitive Learning
Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches involve the use of unsupervised competitive neural networks. However, these methods are not based on gradient optimization which has been proven to provide striking results in feature extraction also in unsupervised learning. Unfortunately, by focusing mostly on algorithmic efficiency and accuracy, deep clustering techniques are composed of overly complex feature extractors, while using trivial algorithms in their top layer. The aim of this work is to present a novel comprehensive theory aspiring at bridging competitive learning with gradient-based learning, thus allowing the use of extremely powerful deep neural networks for feature extraction and projection combined with the remarkable flexibility and expressiveness of competitive learning. In this paper we fully demonstrate the theoretical equivalence of two novel gradient-based competitive layers. Preliminary experiments show how the dual approach, trained on the transpose of the input matrix i.e. X T , lead to faster convergence rate and higher training accuracy both in low and high-dimensional scenarios
Entropy-Based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since
lawmakers have started requiring interpretable models for
safety-critical domains. Concept-based neural networks have
arisen as explainable-by-design methods as they leverage
human-understandable symbols (i.e. concepts) to predict
class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do
not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In
this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from
neural networks using the formalism of First-Order Logic.
The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this
entropy-based criterion enables the distillation of concise
logic explanations in safety-critical domains from clinical
data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy and matches black box performances
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have
started requiring interpretable models for safety-critical domains.
Concept-based neural networks have arisen as explainable-by-design methods as
they leverage human-understandable symbols (i.e. concepts) to predict class
memberships. However, most of these approaches focus on the identification of
the most relevant concepts but do not provide concise, formal explanations of
how such concepts are leveraged by the classifier to make predictions. In this
paper, we propose a novel end-to-end differentiable approach enabling the
extraction of logic explanations from neural networks using the formalism of
First-Order Logic. The method relies on an entropy-based criterion which
automatically identifies the most relevant concepts. We consider four different
case studies to demonstrate that: (i) this entropy-based criterion enables the
distillation of concise logic explanations in safety-critical domains from
clinical data to computer vision; (ii) the proposed approach outperforms
state-of-the-art white-box models in terms of classification accuracy
Entropy-Based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since
lawmakers have started requiring interpretable models for
safety-critical domains. Concept-based neural networks have
arisen as explainable-by-design methods as they leverage
human-understandable symbols (i.e. concepts) to predict
class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do
not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In
this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from
neural networks using the formalism of First-Order Logic.
The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this
entropy-based criterion enables the distillation of concise
logic explanations in safety-critical domains from clinical
data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy and matches black box performances
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision process
prevents them from earning full human trust. Concept-based models aim to
address this issue by learning tasks based on a set of human-understandable
concepts. However, state-of-the-art concept-based models rely on
high-dimensional concept embedding representations which lack a clear semantic
meaning, thus questioning the interpretability of their decision process. To
overcome this limitation, we propose the Deep Concept Reasoner (DCR), the first
interpretable concept-based model that builds upon concept embeddings. In DCR,
neural networks do not make task predictions directly, but they build syntactic
rule structures using concept embeddings. DCR then executes these rules on
meaningful concept truth degrees to provide a final interpretable and
semantically-consistent prediction in a differentiable manner. Our experiments
show that DCR: (i) improves up to +25% w.r.t. state-of-the-art interpretable
concept-based models on challenging benchmarks (ii) discovers meaningful logic
rules matching known ground truths even in the absence of concept supervision
during training, and (iii), facilitates the generation of counterfactual
examples providing the learnt rules as guidance
Concept Embedding Models
Deploying AI-powered systems requires trustworthy models supporting effective
human interactions, going beyond raw prediction accuracy. Concept bottleneck
models promote trustworthiness by conditioning classification tasks on an
intermediate level of human-like concepts. This enables human interventions
which can correct mispredicted concepts to improve the model's performance.
However, existing concept bottleneck models are unable to find optimal
compromises between high task accuracy, robust concept-based explanations, and
effective interventions on concepts -- particularly in real-world conditions
where complete and accurate concept supervisions are scarce. To address this,
we propose Concept Embedding Models, a novel family of concept bottleneck
models which goes beyond the current accuracy-vs-interpretability trade-off by
learning interpretable high-dimensional concept representations. Our
experiments demonstrate that Concept Embedding Models (1) attain better or
competitive task accuracy w.r.t. standard neural models without concepts, (2)
provide concept representations capturing meaningful semantics including and
beyond their ground truth labels, (3) support test-time concept interventions
whose effect in test accuracy surpasses that in standard concept bottleneck
models, and (4) scale to real-world conditions where complete concept
supervisions are scarce.Comment: To appear at NeurIPS 202
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Deploying AI-powered systems requires trustworthy models supporting effective
human interactions, going beyond raw prediction accuracy. Concept bottleneck
models promote trustworthiness by conditioning classification tasks on an intermediate level of human-like concepts. This enables human interventions which can
correct mispredicted concepts to improve the model’s performance. However, existing concept bottleneck models are unable to find optimal compromises between
high task accuracy, robust concept-based explanations, and effective interventions
on concepts—particularly in real-world conditions where complete and accurate
concept supervisions are scarce. To address this, we propose Concept Embedding Models, a novel family of concept bottleneck models which goes beyond
the current accuracy-vs-interpretability trade-off by learning interpretable highdimensional concept representations. Our experiments demonstrate that Concept
Embedding Models (1) attain better or competitive task accuracy w.r.t. standard
neural models without concepts, (2) provide concept representations capturing
meaningful semantics including and beyond their ground truth labels, (3) support
test-time concept interventions whose effect in test accuracy surpasses that in
standard concept bottleneck models, and (4) scale to real-world conditions where
complete concept supervisions are scarce