SySeVR uses deep learning to detect software vulnerabilities.
problem Detecting software vulnerabilities is challenging and important.
method SySeVR combines syntax and semantic information to represent programs for deep learning.
result SySeVR detects 15 unknown vulnerabilities, including 7 unknown and 8 silently patched ones.
Deep learning detects software vulnerabilities from source code.
problem Automated detection of software vulnerabilities in source code.
method Deep feature representation learning on lexed source code.
result Deep learning can effectively detect software vulnerabilities.
Generative Adversarial Network repairs software bugs without labeled data.
problem Automated repair of software vulnerabilities.
method Adversarial learning approach mapping between source and target domains.
result Effective at repairing software vulnerabilities, close to seq2seq approaches.
Study improves machine learning for predicting exploited vulnerabilities.
problem Predicting which vulnerabilities will be exploited efficiently.
method Replicates and compares machine learning approaches using open-source data.
result Selection of training and test data critically affects model performance.
Paper presents machine learning approach for detecting software vulnerabilities.
problem Detect subtle security vulnerabilities in production software.
method Data-driven approach using machine learning on C and C++ code.
result Highest performing model achieves AUC of 0.87 on ROC curve.
Devign uses graph neural networks to identify vulnerabilities efficiently.
problem Challenging and tedious process of identifying vulnerabilities in software systems.
method Devign employs a graph neural network to classify graph-level vulnerabilities using comprehensive code semantic representations.
result Devign significantly outperforms state-of-the-art models in vulnerability identification.
HarDNN detects and protects CNNs from hardware errors.
problem Hardware errors in CNNs can corrupt inference output.
method Statistical error injection and heuristic vulnerability assessment.
result HarDNN improves CNN resilience with minimal additional computation.
Explores security challenges of machine learning in real-world systems.
problem Vulnerabilities in machine learning models deployed in safety-critical systems.
method Broadens systems security view of ML vulnerabilities, identifies novel challenges, proposes mitigation suggestions.
result Highlights novel challenges and proposes mitigation strategies for securing ML systems.
Develops deep learning for logical code segmentation.
problem Lack of logically segmented source code.
method Novel deep learning approach to generate logical code segments.
result Improves software analysis tasks like commenting, bug detection, and code synthesis.
This research tackles adversarial attacks on compressed deep learning models, proposing a defense technique.
problem Adversarial attacks on compressed deep learning models.
method Jointly considers DNN model reshaping and input perturbations, developing a gradient inhibition defense technique.
result Gradient inhibition reduces adversarial success rate from 87.99% to 4.77%.
We present a second iteration of a machine learning approach to static code analysis and fingerprinting for weaknesses related to security, software engineering, and others using the open-source MARF framework and the MARFCAT application based on it for the NIST's SATE IV static analysis tool exposition workshop's data…
Enhances graph classification by adding virtual nodes to represent latent graph aspects.
problem Graph classification challenges in representing latent graph aspects not directly available from attributes and connectivity.
method Introducing virtual nodes bidirectionally connected to all existing nodes, then using Column Network for representation.
result Virtual Column Network (VCN) outperforms existing methods in bioactivity prediction and vulnerability detection.
Neural network identifies undeclared variables and infers their types.
problem Undeclared variable errors in programs.
method Trained on structural semantic details of AST, identifies and infers types of undeclared variables.
result Correctly identified and inferred types for 80% of programs with undeclared variable errors.
Graph Matching Networks learn graph similarity using GNN embeddings.
problem Learning similarity between graph structured objects.
method Graph Matching Network model using cross-graph attention mechanism.
result Models outperform baseline systems in function similarity search.
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk …
Defends against ML inference attacks using adversarial examples.
problem Automated inference attacks using ML classifiers pose privacy and security threats.
method Turns ML classifier vulnerabilities into defenses by adding adversarial noise to public data.
result Adversarial examples can mislead ML classifiers and protect private data.
The recent financial crisis have generated renewed interests in fragilities of global financial networks among economists and regulatory authorities. In particular, a potential vulnerability of the financial networks is the "financial contagion" process in which insolvencies of individual entities propagate through the…
DRIFT uses RL to automate functional software testing efficiently.
problem Efficient and reliable automated software testing.
method DRIFT employs Q-learning with Graph Neural Networks on symbolic UI representations.
result DRIFT can robustly test software functionalities in a fully automated manner.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.
Paper proposes methods to reduce adversarial vulnerability in neural networks.
problem Adversarial vulnerability of deep neural networks in safety-critical applications.
method Defining vulnerability, proposing Vulnerability Suppression (VS) loss, and a Bayesian feature pruning method.
result Improves adversarial robustness and clean example performance with minimal parameter reduction.
KnotPlot helps beginners and veterans use software for visualizing knots.
problem None explicitly stated in the abstract.
method Software introduction and background for users.
result Provides new and veteran users with KnotPlot functionality.
Firefly algorithm improves software effort estimation models.
problem Improving accuracy of software effort estimation models.
method Using Firefly Algorithm to optimize COCOMO-based models.
result High accuracy and significant error minimization of Firefly Algorithm.
Research proposes an ensemble learning model for efficient software defect prediction.
problem Efficient and cost-effective software testing to minimize project resources.
method Machine learning analysis on different datasets using KNN, Decision Tree, SVM, and Naïve Bayes.
result Ensemble learning model outperforms other techniques in accuracy, precision, recall, and F1-score.
Improving software quality through effective organizational learning.
problem Lack of reliable quantification methods for software evolution.
method Leveraging application lifecycle management data to identify and address managerial practices.
result Effective learning from past processes improves software quality indirectly.
Study shows unequal success of membership inference attacks across different groups.
problem Unequal success of membership inference attacks across different groups.
method Established conditions for preventing MIAs and derived connections to fairness and differential privacy.
result Estimating disparate vulnerability to MIAs can lead to overestimation; suitable attacks and statistical framework provided.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Neural networks become more vulnerable to adversarial attacks as input dimensions increase.
problem Adversarial vulnerability of neural networks increases with input dimension.
method Analyzed the relationship between adversarial vulnerability and input dimension, proving a square root dependence.
result Adversarial vulnerability increases with the square root of input dimension.
Improved software flaw detection using NAS on multimodal DL models.
problem Software flaw detection in multimodal deep learning models.
method Adapted NAS framework for multimodal learning, combined with multimodal deep learning models.
result Improved performance on the Juliet Test Suite.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
problem Predicting time-varying hardware resource availability in control software.
method Path structured multimarginal Schrödinger bridge (MSBP) for learning stochastic resource usage.
result Guaranteed linear convergence to accurate prediction of hardware resource utilization.
This paper tackles co-design of neural hardware and software to improve efficiency.
problem Designing efficient deep learning systems that consider both hardware and software optimizations together.
method Developed a constrained Bayesian optimization framework to automatically identify profitable design points in the joint hardware/software design space.
result Improved energy-delay product by 18% (ResNet) and 40% (DQN) over hand-tuned systems.
Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, i…
Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
problem Vulnerability of capsule networks to adversarial attacks.
method Compared capsule networks to convolutional neural networks using various adversarial attacks.
result Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of…
Machine learning enhances fuzzing for better software testing.
problem Challenges in traditional fuzzing.
method Applications of machine learning to improve fuzzing.
result Machine learning tools have successfully addressed fuzzing bottlenecks.
Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification…
The paper addresses model vulnerability to image transformations.
problem Vulnerability of computer vision models to distributional shifts.
method Formulates a combinatorial optimization problem and uses search algorithms to evaluate vulnerability regions. Embeds this idea in a training procedure to define new data augmentation rules.
result Trains more robust models against distributional shifts and image manipulations.
Survey of software developers' experience with Github Copilot tool.
problem Investigate developers' acceptance of AI-generated code.
method Survey with 18 questions distributed to 42 programmers.
result Mixed developer opinions, mostly positive but reluctance to use.
Optuna introduces a new hyperparameter optimization framework.
problem Efficient hyperparameter optimization for machine learning models.
method Define-by-run API, efficient implementation, versatile architecture.
result Optuna is the first software meeting next-generation hyperparameter optimization criteria.
AI's high performance is linked to its vulnerability due to data geometry.
problem Vulnerability of AI systems to adversarial attacks.
method Simple geometric argument based on high-dimensional data sets.
result High performance and vulnerability are intrinsically coupled.
Adapts ISO 26262 for machine learning in automotive software.
problem Conflict between innovation and safety in automotive software development.
method Detailed assessment and adaptation of ISO 26262 for machine learning, analyzing and proposing new requirements.
result Adapted ISO 26262 for machine learning, addressing safety and innovation in automotive software.
New risk measures assess cryptocurrency market vulnerabilities during financial distress.
problem Capturing systemic risk in cryptocurrency markets during financial distress.
method Introducing Vulnerability Conditional Risk Measures (VCoES) and related measures.
result Validated theoretical insights and demonstrated practical relevance in cryptocurrency market.
RAMANMETRIX simplifies Raman spectroscopy data analysis.
problem Complexity of Raman spectroscopy data processing.
method User-friendly software with chemometric analysis workflow.
result Facilitates integration of Raman spectroscopy into clinical routines.
Hybrid model prices vulnerable options with stochastic volatility.
problem Pricing options with stochastic volatility and credit risk.
method Closed-form hybrid credit risk model with Heston-Nandi GARCH processes.
result Explicit pricing formula for vulnerable options.
Software helps teach latent variable methods in multivariate data analytics.
problem Challenges in understanding multivariate data analytics for students.
method Interactive software for comparing latent variable methods.
result Builds intuition on choosing methods and interpreting coefficients.
This study compares traditional ML and deep learning for classifying multilingual user feedback.
problem Classifying multilingual user feedback for software development teams.
method Comparison of traditional machine learning and deep learning approaches.
result Traditional machine learning can achieve comparable results to deep learning for multilingual user feedback classification.
Import2vec creates embeddings for software libraries to improve learning tasks.
problem Developing semantic representations for software libraries.
method Applied word embedding techniques from NLP to library packages.
result Library vectors capture meaningful relationships among libraries.
Preschool attendance correlates with lower developmental vulnerabilities in Queensland, Australia.
problem Understanding the relationship between preschool attendance and developmental vulnerabilities in different regions.
method Data Analysis and Machine Learning to identify clusters of socio-demographic variables.
result Identified three clusters with varying socio-demographic variables affecting the relationship between preschool attendance and developmental vulnerabilities.
EvaSylv software evaluates forest management with natural risk considerations.
problem Evaluating forest management under increased natural risk due to climate change.
method User-friendly software simulates forest management scenarios, integrating natural risk using a Poisson process and Faustmann approach.
result Software optimizes forest management criteria like Faustmann value and Averaged yield value.