Sequential Binary Classification for Intrusion Detection

• URL: http://arxiv.org/abs/2406.06099v2
• Date: Thu, 13 Feb 2025 09:35:44 GMT
• Title: Sequential Binary Classification for Intrusion Detection
• Authors: Shrihari Vasudevan, Ishan Chokshi, Raaghul Ranganathan, Nachiappan Sundaram,
• Abstract summary: IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models.<n>This paper explores a structural approach to handling class imbalance in multi-class classification problems.<n> Experiments on benchmark IDS datasets demonstrate that the structural approach to handling class-imbalance, as exemplified by SBC, is a viable approach to handling the issue.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network Intrusion Detection Systems (IDS) have become increasingly important as networks become more vulnerable to new and sophisticated attacks. Machine Learning (ML)-based IDS are increasingly seen as the most effective approach to handle this issue. However, IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. Different from existing data-driven techniques to handling class imbalance, this paper explores a structural approach to handling class imbalance in multi-class classification (MCC) problems. The proposed approach - Sequential Binary Classification (SBC), is a hierarchical cascade of (regular) binary classifiers. Experiments on benchmark IDS datasets demonstrate that the structural approach to handling class-imbalance, as exemplified by SBC, is a viable approach to handling the issue.

Related papers

• Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Need [83.10178754323955]
  Few-shot learning enables models to generalize from only a few labeled examples. We propose the Unbiased Max-Min Embedding Classification (UMMEC) Method, which addresses the key challenges in few-shot learning. Our method significantly improves classification performance with minimal labeled data, advancing the state-of-the-art in annotatedL.
  arXiv  Detail & Related papers  (2025-03-28T07:23:07Z)
• Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models [0.23408308015481666]
  This study presents the development and evaluation of an advanced Intrusion detection (IDS) based on a hybrid LSTM-convolution neural network (CNN)-Attention architecture. The research focuses on two key classification tasks: binary and multi-class classification. In binary classification, the model achieved near-perfect accuracy, while in multi-class classification, it maintained a high accuracy level (99.04%), effectively categorizing different attack types with a loss value of 0.0220%.
  arXiv  Detail & Related papers  (2025-01-21T20:52:23Z)
• Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic [2.5182419298876857]
  Multi-class classification models can identify specific types of attacks, allowing for more targeted and effective incident responses. Recent advances suggest that generative models can assist in data augmentation, claiming to offer superior solutions for imbalanced datasets. Our experiments indicate that resampling methods for balancing training data do not reliably improve classification performance.
  arXiv  Detail & Related papers  (2024-08-28T12:44:07Z)
• Alleviating Class Imbalance in Semi-supervised Multi-organ Segmentation via Balanced Subclass Regularization [3.5622306331369993]
  We present a two-phase semi-supervised network (BSR-Net) with balanced subclass regularization for multi-organ segmentation (MoS) In Phase I, we introduce a class-balanced subclass generation strategy based on balanced clustering. In Phase II, we design an auxiliary subclass segmentation task within the multi-task framework of the main MoS task. The SCS task contributes a balanced subclass regularization to the main MoS task and transfers unbiased knowledge to the MoS network.
  arXiv  Detail & Related papers  (2024-08-26T07:02:17Z)
• On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection [50.38534263407915]
  Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their ability to generalize across diverse networks is a critical factor in their effectiveness and a prerequisite for real-world applications. In this study, we conduct a comprehensive analysis on the generalization of machine-learning-based NIDS through an extensive experimentation in a cross-dataset framework.
  arXiv  Detail & Related papers  (2024-02-15T14:39:58Z)
• DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection [0.0]
  Machine Learning approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs) Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. This paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples.
  arXiv  Detail & Related papers  (2022-12-15T00:08:05Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification [4.495583520377878]
  We show that in the case of malicious traffic classification, state-of-the-art DL-based solutions do not necessarily outperform the classical ML-based ones. We exemplify this finding using two well-known datasets for a varied set of tasks, such as: malware detection, malware family classification, detection of zero-day attacks, and classification of an iteratively growing dataset.
  arXiv  Detail & Related papers  (2022-06-16T08:59:53Z)
• Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection [0.0]
  Two new algorithms are proposed to address the class overlap issue in the dataset. The proposed design is evaluated for both binary and multi-category classification.
  arXiv  Detail & Related papers  (2022-05-08T21:06:42Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks [4.607145155913717]
  Intrusion detection system (IDS) is essential element of security monitoring in computer networks. Main challenge of an IDS is facing new (i.e., zero-day) attacks and separating them from benign traffic and existing types of attacks. In this paper, we propose a framework for deep learning-based IDSes addressing new attacks.
  arXiv  Detail & Related papers  (2021-08-20T14:41:28Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• CREPO: An Open Repository to Benchmark Credal Network Algorithms [78.79752265884109]
  Credal networks are imprecise probabilistic graphical models based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. We present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models.
  arXiv  Detail & Related papers  (2021-05-10T07:31:59Z)
• An Explainable Machine Learning-based Network Intrusion Detection System for Enabling Generalisability in Securing IoT Networks [0.0]
  Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the security posture of an organisation. Many systems have been designed and developed in the research community, often achieving a perfect detection rate when evaluated using certain datasets. This paper tightens the gap by evaluating the generalisability of a common feature set to different network environments and attack types.
  arXiv  Detail & Related papers  (2021-04-15T00:44:45Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem [3.411873646414169]
  Population Structure-learned classifier (PSC) is proposed. PSC can obtain better generalization performance on IHDLSS. PSC is superior to the state-of-art methods in IHDLSS.
  arXiv  Detail & Related papers  (2020-09-10T08:33:39Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)
• Searching Central Difference Convolutional Networks for Face Anti-Spoofing [68.77468465774267]
  Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods rely on stacked convolutions and expert-designed network. Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC)
  arXiv  Detail & Related papers  (2020-03-09T12:48:37Z)

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.