A Fraud Detection System Based on Anomaly Intrusion Detection Systems for E-Commerce Applications

Smart homes, which incorporate IoT technologies to provide home security, efficient environmental services, conveniences, and improved living standards, are becoming the centre of smart urban developments. With the increased inter-connectivity of smart objects and sensors, there is now, also, an increased level of cyber threats, which can compromise privacy and security. These threats either modify packets of information or inject modified packets into the networks. This chapter examines current intrusion detection systems (IDSs) and presents a unique solution to overcome intrusion detection challenges. It discusses the implementation of smart home IDS (SHIDS), using a machine learning based signature and anomaly intrusion detection scheme to detect network intrusions in the smart home. Suggested mechanism is based on naïve Bayes technique to improve the detection performance. The performance of SHIDS has been tested with network intrusions resulting from DoS, probe, remote-to-local (R2L), and user-to-root (U2R) attacks.

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

Cybersecurity Education Science Technique ◽

10.28925/2663-4023.2020.7.1730 ◽

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.

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A Review of Intrusion Detection Systems in Cloud Computing

Cloud Security ◽

10.4018/978-1-5225-8176-5.ch003 ◽

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.

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

Sensors ◽

10.3390/s20092559 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2559 ◽

Cited By ~ 9

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.

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Security Enrichment in Intrusion Detection System Using Classifier Ensemble

Journal of Electrical and Computer Engineering ◽

10.1155/2017/1794849 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 5

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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Network Attacks Detection by Hierarchical Neural Network

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v4i2.108 ◽

2015 ◽

Vol 4 (2) ◽

pp. 119-132

Author(s):

Mohammad Masoud Javidi

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Single Agent ◽

Attack Detection ◽

Intrusion Detection Systems ◽

Training Dataset ◽

Detection Systems ◽

Combination Of Classifiers

Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier

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IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism

Journal Of Big Data ◽

10.1186/s40537-021-00544-5 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

FatimaEzzahra Laghrissi ◽

Samira Douzi ◽

Khadija Douzi ◽

Badr Hssina

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Short Term Memory ◽

Detection System ◽

False Negative ◽

False Negative Rate ◽

Attention Mechanism ◽

Intrusion Detection Systems ◽

Network Attacks ◽

Detection Systems

AbstractNetwork attacks are illegal activities on digital resources within an organizational network with the express intention of compromising systems. A cyber attack can be directed by individuals, communities, states or even from an anonymous source. Hackers commonly conduct network attacks to alter, damage, or steal private data. Intrusion detection systems (IDS) are the best and most effective techniques when it comes to tackle these threats. An IDS is a software application or hardware device that monitors traffic to search for malevolent activity or policy breaches. Moreover, IDSs are designed to be deployed in different environments, and they can either be host-based or network-based. A host-based intrusion detection system is installed on the client computer, while a network-based intrusion detection system is located on the network. IDSs based on deep learning have been used in the past few years and proved their effectiveness. However, these approaches produce a big false negative rate, which impacts the performance and potency of network security. In this paper, a detection model based on long short-term memory (LSTM) and Attention mechanism is proposed. Furthermore, we used four reduction algorithms, namely: Chi-Square, UMAP, Principal Components Analysis (PCA), and Mutual information. In addition, we evaluated the proposed approaches on the NSL-KDD dataset. The experimental results demonstrate that using Attention with all features and using PCA with 03 components had the best performance, reaching an accuracy of 99.09% and 98.49% for binary and multiclass classification, respectively.

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A Review of Intrusion Detection Systems in Cloud Computing

Security and Privacy in Smart Sensor Networks - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-5736-4.ch012 ◽

2018 ◽

pp. 253-283 ◽

Cited By ~ 4

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.

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Intrusion Detection Systems Alerts Reduction

Security and Privacy Management, Techniques, and Protocols - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-5583-4.ch010 ◽

2018 ◽

pp. 255-275 ◽

Cited By ~ 1

Author(s):

Aymen Akremi ◽

Hassen Sallay ◽

Mohsen Rouached

Keyword(s):

Intrusion Detection ◽

High Speed ◽

Detection System ◽

Search Space ◽

Relevant Information ◽

Intrusion Detection Systems ◽

Detection Accuracy ◽

Detection Systems ◽

Rules Set ◽

Intrusion Alerts

Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.

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Framework for Threat Analysis and Attack Modelling of Network Security Protocols

Securing the Internet of Things ◽

10.4018/978-1-5225-9866-4.ch007 ◽

2020 ◽

pp. 110-124

Author(s):

Nachiket Athavale ◽

Shubham Deshpande ◽

Vikash Chaudhary ◽

Jatin Chavan ◽

S. S. Barde

Keyword(s):

Network Security ◽

Intrusion Detection ◽

Detection System ◽

Security Protocols ◽

Intrusion Detection Systems ◽

Sensitive Information ◽

Detection Systems ◽

Threat Analysis ◽

Warning Message ◽

System Administrator

Nowadays everything is computerized including banking and personal records. Also, to boost business profits, businessmen have changed their way of operations from physical way to electronic way, for example Flipkart. But as these developments benefit the developer they also increase the chance of exposing all of customer's personal details to malicious users. Hackers can enter into the system and can steal crucial or sensitive information about other authentic users and in case of banks leads to frauds. Security thus, becomes an important issue for all companies and banks. Intrusion detection systems help such companies by detecting in real time whether an intrusion is carried on or not. Here the authors are developing a signature based intrusion detection system which will scan incoming packets and send a warning message to system administrator. Also, the authors are implementing a framework and provide it to all the users so that developing intrusion detection based system similar to ours. The advantage of using framework is that it can be upgraded and re-defined whenever it is needed.

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