Designing Light Weight Intrusion Detection Systems

2009 ◽  
pp. 216-229 ◽  
Author(s):  
Václav Snášel ◽  
Jan Platoš ◽  
Pavel Krömer ◽  
Ajith Abraham
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Matrix Factorization ◽  
Cyber Security ◽  
Detection System ◽  
Factorization Method ◽  
Performance Comparison ◽  
Intrusion Detection Systems ◽  
Light Weight ◽  
Detection Systems

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low false alarm rate and reduced number of features. Finally, the authors present the Non-negative matrix factorization method for detecting real attacks and the performance comparison with other computational intelligence techniques.

Designing Light Weight Intrusion Detection Systems

2012 ◽  
pp. 304-317
Author(s):  
Václav Snášel ◽  
Jan Platoš ◽  
Pavel Krömer ◽  
Ajith Abraham
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Matrix Factorization ◽  
Cyber Security ◽  
Detection System ◽  
Factorization Method ◽  
Performance Comparison ◽  
Intrusion Detection Systems ◽  
Light Weight ◽  
Detection Systems

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low false alarm rate and reduced number of features. Finally, the authors present the Non-negative matrix factorization method for detecting real attacks and the performance comparison with other computational intelligence techniques.

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 THE LOAD BALANCING OF SELF-SIMILAR TRAFFIC IN NETWORK INTRUSION DETECTION SYSTEMS

2020 ◽  
Vol 3 (7) ◽  
pp. 17-30
Author(s):  
Tamara Radivilova ◽  
Lyudmyla Kirichenko ◽  
Maksym Tawalbeh ◽  
Petro Zinchenko ◽  
Vitalii Bulakh
Keyword(s):  
Load Balancing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Deep Packet Inspection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Load Imbalance ◽  
Packet Inspection

The problem of load balancing in intrusion detection systems is considered in this paper. The analysis of existing problems of load balancing and modern methods of their solution are carried out. Types of intrusion detection systems and their description are given. A description of the intrusion detection system, its location, and the functioning of its elements in the computer system are provided. Comparative analysis of load balancing methods based on packet inspection and service time calculation is performed. An analysis of the causes of load imbalance in the intrusion detection system elements and the effects of load imbalance is also presented. A model of a network intrusion detection system based on packet signature analysis is presented. This paper describes the multifractal properties of traffic. Based on the analysis of intrusion detection systems, multifractal traffic properties and load balancing problem, the method of balancing is proposed, which is based on the funcsioning of the intrusion detection system elements and analysis of multifractal properties of incoming traffic. The proposed method takes into account the time of deep packet inspection required to compare a packet with signatures, which is calculated based on the calculation of the information flow multifractality degree. Load balancing rules are generated by the estimated average time of deep packet inspection and traffic multifractal parameters. This paper presents the simulation results of the proposed load balancing method compared to the standard method. It is shown that the load balancing method proposed in this paper provides for a uniform load distribution at the intrusion detection system elements. This allows for high speed and accuracy of intrusion detection with high-quality multifractal load balancing.

A Review of Intrusion Detection Systems in Cloud Computing

2019 ◽  
pp. 54-83
Author(s):  
Chiba Zouhair ◽  
Noreddine Abghour ◽  
Khalid Moussaid ◽  
Amina El Omri ◽  
Mohamed Rida
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Detection System ◽  
Security Requirements ◽  
Intrusion Detection Systems ◽  
Cloud Environment ◽  
Detection Techniques ◽  
Detection Systems ◽  
Cloud Architecture ◽  
Data Source

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

scholarly journals Security Enrichment in Intrusion Detection System Using Classifier Ensemble

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Uma R. Salunkhe ◽  
Suresh N. Mali
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Hybrid Approach ◽  
Classifier Ensemble ◽  
Intrusion Detection Systems ◽  
Detection Rates ◽  
Detection Systems

In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

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