Thwarting Cybersecurity Attacks with Explainable Concept Drift

• URL: http://arxiv.org/abs/2403.13023v1
• Date: Mon, 18 Mar 2024 20:20:00 GMT
• Title: Thwarting Cybersecurity Attacks with Explainable Concept Drift
• Authors: Ibrahim Shaer, Abdallah Shami,
• Abstract summary: Cyber-security attacks pose a significant threat to the operation of autonomous systems. This paper proposes a Feature Drift Explanation (FDE) module to identify the drifting features. FDE successfully identifies 85.77 % of drifting features and showcases its utility in the DL adaptation method.
• Score: 10.517955982799553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine Learning (ML) models using the captured data. As such, attacks that alter the readings of these sensors can severely affect the HVAC system operations impacting residents' comfort and energy reduction goals. Such attacks may induce changes in the online data distribution being fed to the ML models, violating the fundamental assumption of similarity in training and testing data distribution. This leads to a degradation in model prediction accuracy due to a phenomenon known as Concept Drift (CD) - the alteration in the relationship between input features and the target variable. Addressing CD requires identifying the source of drift to apply targeted mitigation strategies, a process termed drift explanation. This paper proposes a Feature Drift Explanation (FDE) module to identify the drifting features. FDE utilizes an Auto-encoder (AE) that reconstructs the activation of the first layer of the regression Deep Learning (DL) model and finds their latent representations. When a drift is detected, each feature of the drifting data is replaced by its representative counterpart from the training data. The Minkowski distance is then used to measure the divergence between the altered drifting data and the original training data. The results show that FDE successfully identifies 85.77 % of drifting features and showcases its utility in the DL adaptation method under the CD phenomenon. As a result, the FDE method is an effective strategy for identifying drifting features towards thwarting cyber-security attacks.

Related papers

• Towards Physically Consistent Deep Learning For Climate Model Parameterizations [46.07009109585047]
  parameterizations are a major source of systematic errors and large uncertainties in climate projections. Deep learning (DL)-based parameterizations, trained on data from computationally expensive short, high-resolution simulations, have shown great promise for improving climate models. We propose an efficient supervised learning framework for DL-based parameterizations that leads to physically consistent models.
  arXiv  Detail & Related papers  (2024-06-06T10:02:49Z)
• An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks [0.0]
  Advanced Persistent Threats (APTs) are a type of attack that implement a wide range of strategies to evade detection. Machine Learning (ML) techniques in Intrusion Detection Systems (IDSs) is widely used to detect such attacks but has a challenge when the data distribution changes. An incremental hybrid adaptive Network Intrusion Detection System (NIDS) is proposed to tackle the issue of concept drift in SDN.
  arXiv  Detail & Related papers  (2024-04-01T13:33:40Z)
• Logits Poisoning Attack in Federated Distillation [8.728629314547248]
  We introduce FDLA, a poisoning attack method tailored for Federated Distillation (FD) We demonstrate that LPA effectively compromises client model accuracy, outperforming established baseline algorithms in this regard. Our findings underscore the critical need for robust defense mechanisms in FD settings to mitigate such adversarial threats.
  arXiv  Detail & Related papers  (2024-01-08T06:18:46Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Deep Metric Learning for Unsupervised Remote Sensing Change Detection [60.89777029184023]
  Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs) The performance of existing RS-CD methods is attributed to training on large annotated datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues.
  arXiv  Detail & Related papers  (2023-03-16T17:52:45Z)
• Autoregressive based Drift Detection Method [0.0]
  We propose a new concept drift detection method based on autoregressive models called ADDM. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods.
  arXiv  Detail & Related papers  (2022-03-09T14:36:16Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Detecting Concept Drift With Neural Network Model Uncertainty [0.0]
  Uncertainty Drift Detection (UDD) is able to detect drifts without access to true labels. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model. We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks.
  arXiv  Detail & Related papers  (2021-07-05T08:56:36Z)
• Automatic Learning to Detect Concept Drift [40.69280758487987]
  We propose Meta-ADD, a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via prototypical neural network. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning.
  arXiv  Detail & Related papers  (2021-05-04T11:10:39Z)
• Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining [15.49323098362628]
  We propose a framework for robust concept drift detection in the presence of adversarial and poisoning attacks. We introduce the taxonomy for two types of adversarial concept drifts, as well as a robust trainable drift detector. We also introduce Relative Loss of Robustness - a novel measure for evaluating the performance of concept drift detectors under poisoning attacks.
  arXiv  Detail & Related papers  (2020-09-20T18:46:31Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.