PDF | On Dec 1, 2015, Basant Subba and others published Intrusion Detection Systems using Linear Discriminant Analysis and Logistic Regression | Find, read and cite all the research...

Drawing on the application methods of deep learning in the field of natural language processing, we propose a novel model BAT-MC via the two phase’s learning of Linear Regression & 3 Layer Neural Network and attention on the time series features …

Jan 1, 2020 · In addition, this work also aims for classifying the intrusions using ML algorithms like Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART) and Random Forest. The work was tested with the KDD-CUP dataset and their efficiency was measured and also compared along with the latest researches. Previous Next

Mar 1, 2021 · Detecting intrusions can identify unknown attacks in a network and has been one of the successful ways to enhance network security. The current methods for identifying network anomalies are...

Bhavsar et al. [15] have proposed an anomaly-based intrusion detection system (IDS) particularly designed for IoT applications. They applied ML algorithms to classify the network activities as either normal or anomalous, thus, detecting unusual …

Anomaly based Intrusion Detection System (IDS) identifies intrusion by training itself to recognize acceptable behavior of the network. It then raises an alarm.

In this paper, we employ two statistical methods viz. Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to develop new intrusion detection models. The performance of these...

The present study employs the Multiple Linear Regression (MLR) statistical technique to construct an Intrusion Detection System (IDS). To achieve this, the entirety of the data has been segregated into three distinct paths, which are commonly known as the train-test-split approach.

Conclusion The present study employs the Multiple Linear Regression (MLR) statistical technique to construct an Intrusion Detection System (IDS). To achieve this, the entirety of the data has been segregated into three distinct paths, which are …

Network Intrusion Detection Systems (NIDS) are essential tools for identifying and mitigating unauthorized access and attacks on network infrastructures. This paper investigates the use of linear regression for network intrusion detection, leveraging its simplicity and interpretability.