scholarly journals A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

2017 ◽  
Vol 12 (5) ◽  
pp. 677 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Amin Namadchian
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approach ◽  
Detection Systems ◽  
Video Mining ◽  
Proposed Model ◽  
Evaluation Algorithm

A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2021 ◽  
pp. 467-489
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Deep Learning Model

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2020 ◽  
Vol 16 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Machine Learning Approach

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

scholarly journals A New Ensemble-Based Intrusion Detection System for Internet of Things

2021 ◽  
Author(s):  
Adeel Abbas ◽  
Muazzam A. Khan ◽  
Shahid Latif ◽  
Maria Ajaz ◽  
Awais Aziz Shah ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Model ◽  
Detection Systems ◽  
Proposed Model ◽  
Attack Patterns ◽  
Multi Class Classification

AbstractThe domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

scholarly journals A novel time efficient learning-based approach for smart intrusion detection system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sugandh Seth ◽  
Gurvinder Singh ◽  
Kuljit Kaur Chahal
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Gradient Boosting ◽  
Detection Systems ◽  
Network Parameters ◽  
Proposed Model ◽  
Precision Rate

Abstract Background The ever increasing sophistication of intrusion approaches has led to the dire necessity for developing Intrusion Detection Systems with optimal efficacy. However, existing Intrusion Detection Systems have been developed using outdated attack datasets, with more focus on prediction accuracy and less on prediction latency. The smart Intrusion Detection System framework evolution looks forward to designing and deploying security systems that use various parameters for analyzing current and dynamic traffic trends and are highly time-efficient in predicting intrusions. Aims This paper proposes a novel approach for a time-efficient and smart Intrusion Detection System. Method Herein, we propose a Hybrid Feature Selection approach that aims to reduce the prediction latency without affecting attack prediction performance by lowering the model's complexity. Light Gradient Boosting Machine (LightGBM), a fast gradient boosting framework, is used to build the model on the latest CIC-IDS 2018 dataset. Results The proposed feature selection reduces the prediction latency ranging from 44.52% to 2.25% and the model building time ranging from 52.68% to 17.94% in various algorithms on the CIC-IDS 2018 dataset. The proposed model with hybrid feature selection and LightGBM gives 97.73% accuracy, 96% sensitivity, 99.3% precision rate, and comparatively low prediction latency. The proposed model successfully achieved a raise of 1.5% in accuracy rate and 3% precision rate over the existing model. An in-depth analysis of network parameters is also performed, which gives a deep insight into the variation of network parameters during the benign and malicious sessions.

scholarly journals An Intrusion Detection System- Techniques and Algorithms of Machine and Deep Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 370-376
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Technology ◽  
Detection Systems ◽  
Vital Component ◽  
Open Issues

Computer networks are vital component for today’s development of science and technology, due to the emergence of limitless communication pattern and exponential count of network devices cyber security become crucial for this world to secure the most valuable data or information which is more vulnerable for attack by the intruders. New pattern of intrusion and attacks are created in everyday manner by potential intruders and they should be identified by efficient Intrusion Detection Systems (IDSs), also proper counter should be applied for. The paper surveys about the discussion of various machine /deep learning technology and algorithm related to Intrusion Detection System (IDSs) for the real time performance of the system. Finally the literature review investigated gives some open issues which will need to be considered for further research in the field of network security.

scholarly journals A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

2020 ◽  
Vol 10 (3) ◽  
pp. 2701
Author(s):  
Safaa Laqtib ◽  
Khalid El Yassini ◽  
Moulay Lahcen Hasnaoui
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Detection Systems ◽  
Learning Techniques

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

scholarly journals Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

2021 ◽  
pp. 296-314
Author(s):  
Laiby Thomas ◽  
Subramanya Bhat
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Intrusion Detection ◽  
Detection System ◽  
Main Idea ◽  
Intrusion Detection Systems ◽  
Iot Security ◽  
Detection Systems ◽  
Learning Techniques

Purpose: The authors attempt to examine the work done in the area of Intrusion Detection System in IoT utilizing Machine Learning/Deep Learning technique and various accessible datasets for IoT security in this review of literature. Methodology: The papers in this study were published between 2014 and 2021 and dealt with the use of IDS in IoT security. Various databases such as IEEE, Wiley, Science Direct, MDPI, and others were searched for this purpose, and shortlisted articles used Machine Learning and Deep Learning techniques to handle various IoT vulnerabilities. Findings/Result: In the past few years, the IDS has grown in popularity as a result of their robustness. The main idea behind intrusion detection systems is to detect intruders in a given region. An intruder is a host that tries to connect to other nodes without permission in the world of the Internet of Things. In the field of IDS, there is a research gap. Different ML/DL techniques are used for IDS in IoT. But it does not properly deal with complexity issues. Also, these techniques are limited to some attacks, and it does not provide high accuracy. Originality: A review had been executed from various research works available from online databases and based on the survey derived a structure for the future study. Paper Type: Literature Review.

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