scholarly journals A Closer Look at Intrusion Detection System for Web Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-27 ◽  
Author(s):  
Nancy Agarwal ◽  
Syed Zeeshan Hussain
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Web Applications ◽  
Detection System ◽  
Web Traffic ◽  
Comprehensive Overview ◽  
Web Based ◽  
Efficient System ◽  
Detection Systems ◽  
Web Attacks

Intrusion Detection System (IDS) acts as a defensive tool to detect the security attacks on the web. IDS is a known methodology for detecting network-based attacks but is still immature in monitoring and identifying web-based application attacks. The objective of this research paper is to present a design methodology for efficient IDS with respect to web applications. In this paper, we present several specific aspects which make it challenging for an IDS to monitor and detect web attacks. The article also provides a comprehensive overview of the existing detection systems exclusively designed to observe web traffic. Furthermore, we identify various dimensions for comparing the IDS from different perspectives based on their design and functionalities. We also propose a conceptual framework of a web IDS with a prevention mechanism to offer systematic guidance for the implementation of the system. We compare its features with five existing detection systems, namely, AppSensor, PHPIDS, ModSecurity, Shadow Daemon, and AQTRONIX WebKnight. This paper will highly facilitate the interest groups with the cutting-edge information to understand the stronger and weaker sections of the domain and provide a firm foundation for developing an intelligent and efficient system.

scholarly journals An Invariant-Based Approach for Detecting Attacks Against Data in Web Applications

2014 ◽  
Vol 5 (1) ◽  
pp. 19-38
Author(s):  
Romaric Ludinard ◽  
Éric Totel ◽  
Frédéric Tronel ◽  
Vincent Nicomette ◽  
Mohamed Kaâniche ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Web Application ◽  
Web Applications ◽  
Detection System ◽  
Source Code ◽  
Web Attacks ◽  
Application Profile ◽  
The Web ◽  
Ruby On Rails

RRABIDS (Ruby on Rails Anomaly Based Intrusion Detection System) is an application level intrusion detection system (IDS) for applications implemented with the Ruby on Rails framework. The goal of this intrusion detection system is to detect attacks against data in the context of web applications. This anomaly based IDS focuses on the modelling of the normal application profile using invariants. These invariants are discovered during a learning phase. Then, they are used to instrument the web application at source code level, so that a deviation from the normal profile can be detected at run-time. This paper illustrates on simple examples how the approach detects well-known categories of web attacks that involve a state violation of the application, such as SQL injections. Finally, an assessment phase is performed to evaluate the accuracy of the detection provided by the proposed approach.

scholarly journals An Invariant-Based Approach for Detecting Attacks Against Data in Web Applications

2018 ◽  
pp. 1073-1094
Author(s):  
Romaric Ludinard ◽  
Éric Totel ◽  
Frédéric Tronel ◽  
Vincent Nicomette ◽  
Mohamed Kaâniche ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Web Application ◽  
Web Applications ◽  
Detection System ◽  
Source Code ◽  
Web Attacks ◽  
Application Profile ◽  
The Web ◽  
Ruby On Rails

RRABIDS (Ruby on Rails Anomaly Based Intrusion Detection System) is an application level intrusion detection system (IDS) for applications implemented with the Ruby on Rails framework. The goal of this intrusion detection system is to detect attacks against data in the context of web applications. This anomaly based IDS focuses on the modelling of the normal application profile using invariants. These invariants are discovered during a learning phase. Then, they are used to instrument the web application at source code level, so that a deviation from the normal profile can be detected at run-time. This paper illustrates on simple examples how the approach detects well-known categories of web attacks that involve a state violation of the application, such as SQL injections. Finally, an assessment phase is performed to evaluate the accuracy of the detection provided by the proposed approach.

scholarly journals An Intrusion Detection System for Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Nasim Beigi Mohammadi
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Area Network ◽  
Wormhole Attack ◽  
Detection Systems ◽  
Wireless Mesh ◽  
First Time ◽  
Neighborhood Area Network

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this thesis, we present the requirements for intrusion detection systems in smart grid, neighborhood area network (NAN) in particular. We propose an intrusion detection system (IDS) that considers the constraints and requirements of the NAN. It captures the communication and computation overhead constraints as well as the lack of a central point to install the IDS. The IDS is distributed on some nodes which are powerful in terms of memory, computation and the degree of connectivity. Our IDS uses an analytical approach for detecting Wormhole attack. We simulate wireless mesh NANs in OPNET Modeler and for the first time, we integrate our analytical model in Maple from MapleSoft with our OPNET simulation model.

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.

Fusion of Feature Selection and Random Forest for an Anomaly-Based Intrusion Detection System

2019 ◽  
Vol 16 (8) ◽  
pp. 3603-3607 ◽  
Author(s):  
Shraddha Khonde ◽  
V. Ulagamuthalvi
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
New Technologies ◽  
Detection System ◽  
Sensitive Data ◽  
Detection Systems ◽  
New Type

Considering current network scenario hackers and intruders has become a big threat today. As new technologies are emerging fast, extensive use of these technologies and computers, what plays an important role is security. Most of the computers in network can be easily compromised with attacks. Big issue of concern is increase in new type of attack these days. Security to the sensitive data is very big threat to deal with, it need to consider as high priority issue which should be addressed immediately. Highly efficient Intrusion Detection Systems (IDS) are available now a days which detects various types of attacks on network. But we require the IDS which is intelligent enough to detect and analyze all type of new threats on the network. Maximum accuracy is expected by any of this intelligent intrusion detection system. An Intrusion Detection System can be hardware or software that analyze and monitors all activities of network to detect malicious activities happened inside the network. It also informs and helps administrator to deal with malicious packets, which if enters in network can harm more number of computers connected together. In our work we have implemented an intellectual IDS which helps administrator to analyze real time network traffic. IDS does it by classifying packets entering into the system as normal or malicious. This paper mainly focus on techniques used for feature selection to reduce number of features from KDD-99 dataset. This paper also explains algorithm used for classification i.e., Random Forest which works with forest of trees to classify real time packet as normal or malicious. Random forest makes use of ensembling techniques to give final output which is derived by combining output from number of trees used to create forest. Dataset which is used while performing experiments is KDD-99. This dataset is used to train all trees to get more accuracy with help of random forest. From results achieved we can observe that random forest algorithm gives more accuracy in distributed network with reduced false alarm rate.

NETWORK TRAFFIC ANOMALIES DETECTION USING AN ENSEMBLE OF CLASSIFIERS

2020 ◽  
pp. 38-46
Author(s):  
S. A. Sakulin ◽  
A. N. Alfimtsev ◽  
K. N. Kvitchenko ◽  
L. Ya. Dobkach ◽  
Yu. A. Kalgin
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Hybrid Approach ◽  
Stochastic Gradient Descent ◽  
Type I ◽  
Network Attacks ◽  
Detection Systems ◽  
Accuracy Increase

Network technologies have been steadily developing and their application has been expanding. One of the aspects of the development is a modification of the current network attacks and the appearance of new ones. The anomalies that can be detected in network traffic conform with such attacks. Development of new and improvement of the current approaches to detect anomalies in network traffic have become an urgent task. The article suggests a hybrid approach to detect anomalies on the basis of the combined signature approach and computationally effective classifiers of machine learning: logistic regression, stochastic gradient descent and decision tree with accuracy increase due to weighted voting. The choice of the classifiers is explained by the admissible complexity of the algorithms that allows detection of network traffic events for the time close to real. Signature analysis is carried out with the help of the Zeek IDS (Intrusion Detection System) signature base. Learning is fulfilled by preliminary prepared (by excluding extra recordings and parameters) CICIDS2017 (Canadian Institute for Cybersecurity Intrusion Detection System) signature set by cross validation. The set is roughly divided into ten parts that allows us to increase the accuracy. Experimental evaluation of the developed approach comparing with individual classifiers and with other approaches by such criteria as part of type I and II errors, accuracy and level of detection, has proved the approach suitable to be applied in network attacks detection systems. It is possible to introduce the developed approach into both existing and new anomaly detection systems.

Using response action with intelligent intrusion detection and prevention system against web application malware

2014 ◽  
Vol 22 (5) ◽  
pp. 431-449 ◽  
Author(s):  
Ammar Alazab ◽  
Michael Hobbs ◽  
Jemal Abawajy ◽  
Ansam Khraisat ◽  
Mamoun Alazab
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Web Application ◽  
Web Applications ◽  
Detection Efficiency ◽  
Detection System ◽  
Content Type ◽  
Novel Approach ◽  
Prevention System ◽  
Intrusion Detection And Prevention

Purpose – The purpose of this paper is to mitigate vulnerabilities in web applications, security detection and prevention are the most important mechanisms for security. However, most existing research focuses on how to prevent an attack at the web application layer, with less work dedicated to setting up a response action if a possible attack happened. Design/methodology/approach – A combination of a Signature-based Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS), namely, the Intelligent Intrusion Detection and Prevention System (IIDPS). Findings – After evaluating the new system, a better result was generated in line with detection efficiency and the false alarm rate. This demonstrates the value of direct response action in an intrusion detection system. Research limitations/implications – Data limitation. Originality/value – The contributions of this paper are to first address the problem of web application vulnerabilities. Second, to propose a combination of an SIDS and an AIDS, namely, the IIDPS. Third, this paper presents a novel approach by connecting the IIDPS with a response action using fuzzy logic. Fourth, use the risk assessment to determine an appropriate response action against each attack event. Combining the system provides a better performance for the Intrusion Detection System, and makes the detection and prevention more effective.

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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