An Effective Intrusion Detection System using Supervised Machine Learning Techniques

Ahanger, Aamir S; Khan, Sajad M; Masoodi, Faheem

doi:10.1109/iccmc51019.2021.9418291

Cited by 29 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, based on the accuracy of the electricity dataset which is 88.16%, this model superiors the MLP model of [24] which achieved an accuracy of 81.06%. For the accuracy of the NSL-KDD dataset which is 98.67%, the proposed model outperforms the model presented by [25] that attained an accuracy of 97.05% and also the model proposed by [26] which achieved an accuracy of 97.97%. In terms of the accuracy of HuGaDB dataset which is 97.63%, the proposed model outperforms previous models such as [6] that attained an accuracy of 92.5%, [12] which achieved an accuracy of 88.0%, and [27] that obtained 91.7% as an accuracy.…”

Section: Resultsmentioning

confidence: 77%

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

Al-Khamees

Al-A’araji

Al-Shamery

2023

IJECE

View full text Add to dashboard Cite

The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because it is able to control the overall network performance. This paper presents a new developing DNN model using a multi-layer perceptron (MLP) structure that includes network training based on the optimal learning rate. Thereupon, this model consists of three hidden layers and does not adopt the stability of the learning rate but has a non-constant value (varying over time) to obtain the optimal learning rate which is able to reduce the error in each iteration and increase the model accuracy. This is done by deriving a new parameter that is added to and subtracted from the learning rate. The proposed model is evaluated by three streaming datasets: electricity, network security layer-knowledge discovery in database (NSL-KDD), and human gait database (HuGaDB) datasets. The results proved that the proposed model achieves better results than the constant model and outperforms previous models in terms of accuracy, where it achieved 88.16%, 98.67%, and 97.63% respectively.

show abstract

Section: Resultsmentioning

confidence: 77%

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

Al-Khamees

Al-A’araji

Al-Shamery

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…Differential privacy does not appear to provide protection against property inference attacks and is intended to protect privacy under conditions of participation inference attacks. In [37] investigated other protections against property inference attacks in addition to DP. Normalization (dropout) had a negative effect and actually increased the attacks.…”

Section: Defences Against Attacksmentioning

confidence: 99%

A Reinvent Survey on Machine Learning Attacks

Patil,

Zuber

2023

E3S Web Conf.

View full text Add to dashboard Cite

The increasing prevalence of machine learning technology highlights the urgent need to delve into its insinuations for safety and confidentiality. While inquiry on the safety aspects of mechanism knowledge has garnered considerable attention, privacy considerations have often taken a backseat, although recent years have seen a significant upswing in privacy-focused research. In an effort to contribute to this growing field, we conducted an analysis encompassing more than 40 articles addressing privacy threats in the context of mechanism knowledge, published ended the historical seven centuries. We have contributed to this research by creating a thorough threat architecture and an assault taxonomy. These tools help in categorizing various attacks based on the assets they target and the knowledge adversaries possess. We also conducted an in-depth exploration of the different privacy threats posed by machine learning, shedding light on their mechanisms and implications. Furthermore, our research includes a preliminary investigation into the underlying reasons for privacy breaches in machine learning systems. This aspect delves into the root causes of privacy leaks, shedding light on the factors that make such incidents more likely to occur. In addition to identifying privacy threats and their causes, we have compiled a summary of the most commonly proposed defense mechanisms against these threats. These defences can serve as a resource for organizations and researchers seeking to bolster the privacy of their machine learning systems. Lastly, we recognize that the field of machine learning privacy still faces unanswered questions and developing difficulties. As such, we encourage further research and exploration of potential future areas of interest. By addressing these unresolved issues and embracing emerging technologies and methodologies, we can better safeguard the privacy of individuals in an increasingly data-driven and machine learning-driven world.

show abstract

“…In this subsection, we describe some relevant research on intrusion detection using SVMs, applying the NSL-KDD dataset as the main training and testing use case. A recent study [38] employs SVMs with RBF kernel, RF, DT, and Multi-Layered Perceptron (MLP) to classify intrusions as either normal or malicious. First, data preprocessing involves deleting unnecessary features, and handling missing values; then, data normalization and standardization are achieved, Then, the data is split into training and validation sets and applied to each model separately.…”

Section: Ids Using Svms On the Nsl-kdd Datasetmentioning

confidence: 99%

Intrusion Detection Systems Using Support Vector Machines on the KDDCUP’99 and NSL-KDD Datasets: A Comprehensive Survey

Ngueajio

Washington²,

Rawat³

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line of defense, in detecting attacks on the internet. As intrusion tactics from intruders become more sophisticated and difficult to detect, researchers have started to apply novel Machine Learning (ML) techniques to effectively detect intruders and hence preserve internet users' information and overall trust in the entire internet network security. Over the last decade, there has been an explosion of research on intrusion detection techniques based on ML and Deep Learning (DL) architectures on various cyber security-based datasets such as the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we review contemporary literature and provide a comprehensive survey of different types of intrusion detection technique that applies Support Vector Machines (SVMs) algorithms as a classifier. We focus only on studies that have been evaluated on the two most widely used datasets in cybersecurity namely: the KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method, identifying the role of the SVMs classifier, and all other algorithms involved in the studies. Furthermore, we present a critical review of each method, in tabular form, highlighting the performances measures, strengths, and limitations, of each of the methods surveyed.

show abstract

An Effective Intrusion Detection System using Supervised Machine Learning Techniques

Cited by 29 publications

References 18 publications

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

Enhancing the stability of the deep neural network using a non-constant learning rate for data stream

A Reinvent Survey on Machine Learning Attacks

Intrusion Detection Systems Using Support Vector Machines on the KDDCUP’99 and NSL-KDD Datasets: A Comprehensive Survey

Contact Info

Product

Resources

About