This paper benchmarks UNSW-NB15 as a modern alternative to KDD-99 for A-NIDS.
problem Skewed response distribution and non-stationarity in KDD-99 hamper A-NIDS performance.
method Compared UNSW-NB15 to KDD-99 and NSL-KDD using various classification models and SMOTE oversampling.
result UNSW-NB15 outperforms KDD-99 and NSL-KDD in minority class performance, suggesting it as a better benchmark.
Deep learning detects cyber-attacks in smart grid systems.
problem Cyber-attacks on smart grid systems.
method Deep learning-based intrusion detection system trained on industrial dataset.
result Proposed system outperforms Naive Bayes, SVM, and Random Forest.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
problem Stealthy DDoS attacks in IoT networks.
method Anomaly-based IDS for timely detection and mitigation.
result Demonstrated capability of detecting and mitigating small attack size per source.
A new IDS method uses GANs to detect anomalies in network traffic.
problem Detecting network anomalies with limited labeled data.
method End-to-end adversarial learning using GANs for semi-supervised learning.
result The method outperforms state-of-the-art IDS approaches.
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.
OCLEP+ detects anomalies and intrusions with short patterns.
problem Detecting anomalies and intrusions in minimal data.
method One-class Classification using Length statistics of Emerging Patterns Plus.
result Effective detection of anomalies and intrusions with minimal data.
Paper proposes mimic learning to share intrusion detection models without private data.
problem Difficulty in obtaining labelled training data for intrusion detection models due to privacy concerns.
method Use of mimic learning to transfer knowledge from a teacher model trained on private data to a student model.
result Student model mimics teacher model without access to private data.
This paper shows diffusion models improve intrusion detection by purifying adversarial examples.
problem Adversarial examples mislead ML intrusion detection systems, leading to false alerts or evasion.
method Used diffusion models to purify adversarial examples in intrusion detection.
result Diffusion models enhance adversarial robustness in intrusion detection without significantly impacting normal performance.
This paper explores deep learning for improved intrusion detection.
problem Improving intrusion detection systems using deep learning.
method Discusses deep learning models and neural networks for intrusion detection.
result Promises enhanced performance in intrusion detection.
Enhances network intrusion detection in noisy data.
problem Robustness against contaminated and noisy data inputs in network intrusion detection.
method Probabilistic Temporal Graph Network Support Vector Data Description (TGN-SVDD) model.
result Significant improvements in detection performance with synthetic noise.
A tree-based IDS detects cyber-attacks in AV networks.
problem Cyber-attacks in AV and IoV networks.
method Tree-structure machine learning models, ensemble learning, feature selection.
result High detection rate and low computational cost achieved.
Proposes SOMDAGMM for more accurate network intrusion detection.
problem Inaccurate network intrusion detection in secure network environments.
method Integrates self-organizing map with deep autoencoding Gaussian mixture model.
result SOMDAGMM outperforms state-of-the-art DAGMM with up to 15.58% improvement in F1 score.
EagerNet detects network attacks quickly with less resources.
problem Efficiently detecting network attacks with minimal resources.
method Proposes a new architecture that trades prediction speed for accuracy, evaluating only a subset of layers.
result Comparable accuracies to simple FCNNs achieved with early predictions, saving energy and computational efforts.
Study on deep learning IDS resistance against adversarial attacks.
problem Vulnerabilities in deep learning-based IDS against adversarial attacks.
method Apply min-max optimization to train IDS against adversarial samples.
result Adversarial attack methods can be used in continuous domains and boost IDS robustness.
Project compares KDDCup99 and NSL-KDD datasets using ML classifiers.
problem Quality and bias in IoT network intrusion detection datasets.
method Evaluation of various ML classifiers on both datasets.
result NSL-KDD dataset is of higher quality with lower bias.
AutoIDS uses auto-encoders to detect network intrusions efficiently.
problem Detecting network intrusions with high accuracy and efficiency.
method Semi-supervised machine learning with cascading encoder-decoder neural networks.
result AutoIDS achieves 90.17% accuracy on NSL-KDD dataset, superior to other methods.
Adversarial attacks hide cyber-physical attacks in ICS.
problem Hiding cyber-physical attacks in industrial control systems.
method Modeling an attacker compromising sensors, manipulating data, and evaluating attacks on both continuous and mixed data.
result Successfully hides cyber-physical attacks with 2.87 out of 12 sensors compromised on average.
User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled sta…
This research improves network security by detecting attacks using SVM and dimension reduction.
problem Detecting and preventing attacks on computer networks.
method Combining SVM and SVD for anomaly detection.
result The proposed method outperforms other methods in detecting network attacks.
Bayesian coresets help reduce network intrusion detection data size.
problem Large network intrusion detection data sets hinder Bayesian learning.
method Use Bayesian coresets to reduce data size while maintaining accuracy.
result Bayesian coresets improve learning accuracy and reduce storage needs.
Active learning improves network intrusion detection with minimal labeled data.
problem Finding new attack vectors in low-volume traffic.
method Proposes an active learning framework to minimize labeled data while improving detection quality.
result Active learning with minimal labeled data significantly improves anomaly detection in network traffic.
Random Forest outperforms other IDS algorithms in smart grids.
problem Security vulnerabilities in smart grids.
method Comparison of four data mining algorithms (Random Forest, SVM, Neural Network, and KNN) in detecting attacks.
result Random Forest outperforms other algorithms in terms of detection accuracy and efficiency.
This paper improves machine learning models' robustness against evasion attacks using randomness.
problem Evasion attacks that adapt to machine learning models to avoid detection.
method Incorporates randomness into both training and application phases of machine learning models.
result The proposed randomization-based approach further improves the robustness of machine learning models, especially random forest.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
problem Challenges in offline NIDS for auto-generated port scans.
method Hypergraph-based metrics for real-time ML ensemble NIDS.
result High accuracy, precision, and recall in detecting port scans and attacks.
Improved IDS using LSTM and embedding for network traffic data.
problem Network security threats and need for IDS to detect attacks.
method Proposes IDS models using LSTM for time-series and embedding for categorical network traffic data.
result Improves binary classification accuracy to 99.72%.
Generative adversarial networks generate synthetic anomalies for host-based intrusion detection.
problem Imbalanced datasets in host-based intrusion detection.
method Cycle-GAN to generate synthetic anomalies from normal data.
result Improved anomaly detection with higher AUC and anomaly detection rate.
New online learning algorithms improve cyberattack detection in industrial control systems.
problem Detecting cyberattacks in industrial control systems with limited resources.
method Online learning algorithms to process continuous data streams and address class imbalance.
result Improved detection rate of cyberattacks in industrial control systems.
Paper proposes an efficient GAN model for discrete cyber-intrusion detection.
problem Cyber-intrusion detection in systems with rare abnormal data.
method Generative adversarial network (GAN) with a specifically-designed loss function for discrete data.
result The proposed model outperforms state-of-the-art models on discrete datasets.
New method optimizes searchers' allocation on perimeters over time.
problem Optimizing searchers' allocation on perimeters to detect intrusions.
method Combinatorial multi-armed bandit (CMAB) with upper confidence bound approach.
result Upper and lower bounds on expected performance of the method.
Bayesian Optimization improves machine learning for detecting network attacks.
problem Detecting network attacks to secure critical information.
method Bayesian Optimization for tuning machine learning algorithms (SVM-RBF, RF, k-NN).
result The proposed framework achieves high accuracy and low false alarms.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
Study efficient resource allocation for detecting extreme values.
problem Efficiently allocate limited resources to detect extreme values in various fields.
method Proposes ExtremeHunter algorithm for sequential resource allocation under limited feedback.
result Demonstrates ExtremeHunter outperforms oracle policy in detecting extreme values.
A new anomaly detection method using partial identification.
problem Detecting anomalies in large datasets.
method Partial Identification framework and PIDScore geometric anomaly measure.
result PIDForest outperforms other methods in anomaly detection.
A new method tunes LOF hyperparameters for better anomaly detection.
problem Tuning hyperparameters for LOF improves anomaly detection performance.
method Heuristic methodology to tune LOF hyperparameters.
result Tuned LOF model outperforms in simulations and real data sets.
This paper studies adversarial examples in NIDS, revealing their vulnerability.
problem Vulnerability of machine learning-based NIDS to adversarial examples.
method Used evolutionary computation and deep learning to generate adversarial examples.
result Adversarial examples cause high misclassification rates in various machine learning models.
MAD-GAN detects anomalies in multivariate time series data using GANs.
problem Complex multivariate time series data requires better anomaly detection methods.
method Unsupervised anomaly detection using Generative Adversarial Networks (GANs).
result MAD-GAN effectively detects anomalies in real-world CPS systems.
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
Proposes an autonomous IDS using multiple learning techniques.
problem Autonomous and zero-day attack detection in IDS.
method Parallel use of GRU, CNN, and random forest; majority vote and OR logic combination.
result Achieved high accuracy (87.28%) on NSL-KDD's 'KDDTest+' dataset.
Paper improves anomaly detection and categorization in multi-cloud environments.
problem Differentiating among different types of attacks for better defense.
method Used supervised machine learning techniques (LR and RF) on a public dataset.
result More than 99% detection accuracy and 93.6% categorization accuracy.
SynGAN generates synthetic network attacks to improve NIDS effectiveness.
problem Low effectiveness of NIDS due to high false positives and lack of continuous testing.
method SynGAN uses GANs to generate adversarial network attacks based on real attack traffic.
result SynGAN-generated attacks improve NIDS detection rates compared to real attacks.
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
problem Detecting anomalies in data, especially collective anomalies.
method Random partitioning binary trees with distance-based leaves and semi-supervised learning.
result DiFF-RF significantly outperforms isolation forest and one-class SVM.
Generative adversarial networks enable distributed IoT IDS without central controller.
problem Detecting cyber attacks in IoT systems with privacy preservation.
method Proposes a fully distributed GAN-based IDS for IoT.
result Higher accuracy and lower false positive rate compared to standalone IDS.
Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph. In these applications presence of intrusion, community or disease outbreak is characterized by novel observations on some unknown connected subgraph. These prob…
This paper uses deep learning to improve network threat detection in finance.
problem Detecting unknown threats in large-scale data applications.
method Uses deep learning for advanced threat detection.
result Improves protective measures in the financial industry.
This paper improves AI defenses against network attacks using ML and adversarial learning.
problem Protecting personal data from sophisticated network attacks.
method Unified multi-modal dataset, machine learning for detection, adversarial learning for synthetic data generation.
result Stable ML models for intrusion detection and high-fidelity synthetic data.
Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection. However, few works have explored the use of GANs for the anomaly detection task. We leverage recently developed GAN models for anomaly dete…
Paper presents adversarial method for explaining IDS misclassifications.
problem Limited interpretability of machine learning models in IDSs.
method Adversarial machine learning to generate feature modifications.
result Satisfactory explanations match expert knowledge.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…