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    Analysis and Evaluation of Intrusion Detection Systems Test Methods

    , M.Sc. Thesis Sharif University of Technology Amiri, Behnam (Author) ; Jahangir, Amir Hossein (Supervisor)
    Abstract
    Intrusion detection systems’ test and evaluation is an active research area on which many researchers have been working for years. A complete and comprehensive test methodology that can be applied in reasonable time and cost is important and useful both to evaluate a newly designed system and to compare two or more existing systems to select an appropriate system for a particular network. In this research, we first determine the critical features of an IDS and then inspect methods and effective parameters that may influence the test process and propose a method for testing intrusion detection systems. In the proposed test methodology we only examine critical features which lake of them cause... 

    An efficient method for identifying IDS agent nodes by discovering compromised nodes in MANET

    , Article 2009 International Conference on Computer and Electrical Engineering, ICCEE 2009, 28 December 2009 through 30 December 2009, Dubai ; Volume 1 , 2009 , Pages 625-629 ; 9780769539256 (ISBN) Kuchaki Rafsanjani, M ; Khavasi, A. A ; Movaghar, A ; Sharif University of Technology
    Abstract
    Intrusion Detection Systems (IDS) for Mobile Ad hoc NETworks (MANETs) are necessary when they are deployed in reality. In This paper, we have presented a combined method of selecting IDS agent nodes in mobile ad hoc networks. Since, the IDS agents in MANET due to more activities need to more battery power. In our method, first, compromised nodes are detected and then the nodes with the highest energy power from among valid nodes as IDS agent nodes are considered. So, with this method, some valid nodes contribute in intrusion detection activities and costs of the network monitoring will be reduced and the network lifetime will be increased. © 2009 IEEE  

    IDS modelling and evaluation in WANETs against black/grey-hole attacks using stochastic models

    , Article International Journal of Ad Hoc and Ubiquitous Computing ; Volume 27, Issue 3 , 2018 , Pages 171-186 ; 17438225 (ISSN) Entezari Maleki, R ; Gharib, M ; Khosravi, M ; Movaghar, A ; Sharif University of Technology
    Inderscience Enterprises Ltd  2018
    Abstract
    The aim of this paper is to model and evaluate the performance of intrusion detection systems (IDSs) facing black-hole and grey-hole attacks within wireless ad hoc networks (WANETs). The main performance metric of an IDS in a WANET can be defined as the mean time required for the IDS to detect an attack. To evaluate this measure, two types of stochastic models are used in this paper. In the first step, two different continuous time Markov chains (CTMCs) are proposed to model the attacks, and then, the method of computing the mean time to attack detection is presented. Since the number of states in the proposed CTMCs grows rapidly with increasing the number of intermediate nodes and the... 

    A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

    , Article Cluster Computing ; 2022 ; 13867857 (ISSN) Sangaiah, A. K ; Javadpour, A ; Ja’fari, F ; Pinto, P ; Zhang, W ; Balasubramanian, S ; Sharif University of Technology
    Springer  2022
    Abstract
    Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We... 

    A novel Intrusion Detection System for Mobile Ad-Hoc Network Based on Clustering

    , M.Sc. Thesis Sharif University of Technology Salemi, Hossein (Author) ; Movaghar, Ali (Supervisor)
    Abstract
    A Mobile Ad hoc NETwork (MANET) is a self-configuring network that is formed automatically by a collection of mobile nodes without the help of a fixed infrastructure or centralized management. In recent years, the use of MANETs has been widespread in many applications, including some mission critical applications, and as such security has become one of the major concerns in MANETs. Due to some unique characteristics of MANETs, prevention methods alone are not sufficient to make them secure; therefore, detection should be added as another defense before an attacker can breach the system. In this thesis, we have expressed some well-known and related intrusion detection systems. Besides we have... 

    Performance Evaluation of MANET’s IDSs Using Stochastic Activity Networks (SANs)

    , M.Sc. Thesis Sharif University of Technology Khosravi, Maryam (Author) ; Movaghar, Ali (Supervisor)
    Abstract
    Blackhole and grayhole attacks have been become two of the major security concerns in mobile ad hoc networks (MANET). To achieve security in MANETs, a lot of mechanisms had been proposed by now. Using intrusion detection systems(IDSs) is one of the important mechanism to reach this goal. Thus, a well-known IDS was chosen and analyzed in this thesis. Furthermore, a collaborative bayesian filter approach for this intrusion detection system was proposed to enhance its performance. Then the performance of this approach was considered. This intrusion detection system was analyzed using stochastic modeling like continuous time markov chain(CTMC), stochastic reward net(SRN) and stochastic... 

    An Intrusion Detection System for Wormhole Attack Detection in MANETs

    , M.Sc. Thesis Sharif University of Technology Shamaei Chaharsooghi, Shiva (Author) ; Movaghar, Ali (Supervisor)
    Abstract
    Mobile ad hoc networks (MANETs) have been attracting the attention of the researchers in the duration of last years. Because of lack of infrastructure in such networks, all network operations such as routing are done by the nodes themselves. On the other hand, standard MANETs' routing protocols suppose that all nodes are trusted. Thus, these protocols are prone to serious security attack. Wormhole attack is one of the attacks which abuse distributed routing in MANETs. This attack is held between two malicious nodes which are far away from each other. Mentioned nodes introduce themselves as one-hop neighbor of each other. Therefore, they deceive normal nodes and disturb the routing mechanism.... 

    Analysis and Improvement of Intrusion Detection Methods in Data Network Routers

    , M.Sc. Thesis Sharif University of Technology Jamshidi, Mohammad Ali (Author) ; Aref, Mohammad Reza (Supervisor) ; Pakravan, Mohammad Reza (Co-Advisor)
    Abstract
    High-quality online services demand reliable and fast packet delivery at the network layer. However, clear evidence documents the existence of compromised routers in the ISP and enterprise networks, threatening network availability and reliability. A compromised router can stealthily drop, modify, inject, or delay packets in the forwarding path to launch Denial-of-Service, surveillance, man-in-the-middle attacks, etc. So researches tried to create intrusion detection methods to identify adversarial routers and switches. To this end, data-plane fault localization (FL) aims to identify faulty links and is an effective means of achieving high network availability. FL protocols use... 

    Design and Implementaion of a Web Application Honeypot

    , M.Sc. Thesis Sharif University of Technology Ali Akbarian, Amir Hossein (Author) ; Kharrazi, Mehdi (Supervisor)
    Abstract
    With the rapid growth of Internet popularity, web applications are growing in usage and complexity, and therefore, are attractive targets for attackers. Increasing number of services and amount of information stored in the Internet, stimulates attackers to focus on these kind of applications. On the other hand, security specialists are deploying different solutions to mitigate such threats. One of these solutions are Honeypot systems. In contrast with other security solutions, honeypots are not designed to defend against attackers directly. Honeypots, rather, are planned to gather data about what attackers do. This information can help security administrators to learn and understand... 

    Web Anomaly Host Based IDS, a Machine Learning Approach

    , M.Sc. Thesis Sharif University of Technology Khalkhali, Iman (Author) ; Azmi, Reza (Supervisor) ; Khansari, Mohammad (Co-Advisor)
    Abstract
    Web servers and web applications are susceptible to different attacks. In order to detect web-based attacks Intrusion detection systems (IDS) should be equipped with a large number of signatures. Unfortunately various types of web threats are increasingly growing and so detection and prevention of all these new and old attacks is exhaustive and really difficult.This thesis represents a designed system for intrusion detection that uses different techniques to discover vulnerabilities with derived patterns and also some user behavior based attacks against web applications. This was done by using new dataset which was generated by new log file.The primary objective of this thesis shows the... 

    Intrusion Detection in Data Networks Using Header Space Analysis

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Amir Ahmad (Author) ; Pakravan, Mohammad Reza (Supervisor) ; Kazemian, Payman (Supervisor)
    Abstract
    Software Defined Networking (SDN) provides a logically centralized view of the state of the network, and as a result opens up new ways to manage and monitor networks. In this dissertation a novel approach to network intrusion detection in SDNs is introduced that takes advantage of these attributes. This approach can detect compromised routers that produce faulty messages, copy or steal traffic or maliciously drop certain types of packets. To identify these attacks and the affected switches, we correlate the forwarding state of network---i.e. installed forwarding rules---with the forwarding status of packets---i.e. the actual route packets take in the network and detect anomaly in routes.... 

    FPGA-Based Implementation of Deep Learning Accelerator with Concentration on Intrusion Detection Systems

    , M.Sc. Thesis Sharif University of Technology Fard, Ebrahim (Author) ; Jahangir, Amir Hossein (Supervisor)
    Abstract
    Intrusion Detection System (IDS) is an equipment destined to provide computer networks security. In recent years, Machine Learning and Deep Neural Network (DNN) methods have been considered as a way to detect new network attacks. Due to the huge amounts of calculations needed for these methods, there is a need for high performance and parallel or specific processors, such as Application Specific Integrated Circuit (ASIC), Graphical Processor Unit (GPU) and Field-Programmable Gate Array (FPGA). The latter seems more suitable than others due to its higher configurability and lesser power consumption. The goal of this study is the acceleration of a DNN-based IDS on FPGA. In this study, which is... 

    Performance Improvement of Machine Learning based Intrusion Detection Systems

    , M.Sc. Thesis Sharif University of Technology Ramin, Shirali Hossein Zadeh (Author) ; Jafari Siavoshani, Mahdi (Supervisor)
    Abstract
    The rapid growth of computer networks has increased the importance of analytics and traffic analysis tools for these networks, and the increasing importance of these networks has increased the importance of security of these networks and the intrusion detection in these networks. Many studies aimed at providing a powerful way to quickly and accurately detect computer network intrusions, each of which has addressed this issue.The common point of all these methods is their reliance on the features extracted from network traffic by an expert. This strong dependence has prevented these methods from being flexible against new attacks and methods of intrusion or changes in the current normal... 

    Deep Learning-Based Intrusion Detection Systems in Industrial Control Systems

    , M.Sc. Thesis Sharif University of Technology Amir Hossein Salehi (Author) ; Aref, Mohammad Reza (Supervisor) ; Ahmadi, Siavash (Co-Supervisor)
    Abstract
    With the spread of threats against industrial control systems, preserving the security of these systems faces serious challenges. On the other hand, with the increase of communication between industrial control networks and external communication networks, the entry points of these networks have also increased and this exposes them to IP network threats. Beside that, traditional attacks on these systems, which generally occur by infiltrating the internal network, are also constantly changing and becoming more complex. These attacks mainly have a phase of hiding the attack from the monitoring systems, which eliminates the possibility of identifying the attacker's operations to a great extent... 

    Deep Learning Based Enhancement of Intrusion Detection Methods

    , Ph.D. Dissertation Sharif University of Technology Soltani, Mahdi (Author) ; Jahangir, Amir Hossein (Supervisor) ; Jafari Siavoshani, Mahdi (Supervisor)
    Abstract
    We live in the cyber era in which network-based technologies have become omnipresent. Meanwhile, threats and attacks are rapidly growing in cyberspace. Nowadays, some signature-based intrusion detection systems try to detect these malicious traffics. However, as new vulnerabilities and new zero-day attacks appear, there is a growing risk of bypassing the current intrusion detection systems. Many research studies have worked on machine learning algorithms for intrusion detection applications. Their major weakness is to consider the different aspects of network security concurrently. For example, continuous concept drift in normal and abnormal traffic, the permanent appearance of zero-day... 

    Web driven alert verification

    , Article 2014 11th International ISC Conference on Information Security and Cryptology, ISCISC 2014 ; Sep , 2014 , p. 180-185 Najafi, A ; Sepahi, A ; Jalili, R ; Sharif University of Technology
    Abstract
    A web attack is an attack against a web server through the HTTP Protocol. By analyzing known web attacks, we find out that each one has its own behavior. Vestiges of their behavior could be detected in non-body parts of the HTTP Protocol. Such information can be used to verify web alerts generated by Web Application Firewalls (WAFs) and Web Intrusion Detection Systems (Web IDSs). In this paper, we propose a method to verify web alerts generated by mentioned sensors. The goal of the alert verification component is to eliminate or tag alerts that do not represent successful attacks. Our approach is based on analyzing HTTP Transaction metadata, including Request method, Request Headers, Status... 

    RTECA: Real time episode correlation algorithm for multi-step attack scenarios detection

    , Article Computers and Security ; Volume 49 , March , 2015 , Pages 206-219 ; 01674048 (ISSN) Ahmadian Ramaki, A ; Amini, M ; Ebrahimi Atani, R ; Sharif University of Technology
    Elsevier Ltd  2015
    Abstract
    Today, from information security perspective, prevention methods are not enough solely. Early Warning Systems (EWSs) are in the category of reactive methods. These systems are complementing Intrusion Detection Systems (IDSs) where their main goals include early detection of potential malicious behavior in large scale environments such as national level. An important process in EWSs is the analysis and correlation of alerts aggregated from the installed sensors (e.g., IDSs, IP telescopes, and botnet detection systems). In this paper, an efficient framework for alert correlation in EWSs is proposed. The framework includes a correlation scheme based on a combination of statistical and stream... 

    An effective approach for determining IDS agent nodes in manet

    , Article Proceedings of the 3rd International Conference on Internet Technologies and Applications, ITA 09, 8 September 2009 through 11 September 2009, Wrexham, Wales ; 2009 , Pages 458-465 ; 9780946881659 (ISBN) Kuchaki Rafsanjani, M ; Khavasi, A. A ; Movaghar, A ; Sharif University of Technology
    Abstract
    Mobile Ad hoc NETworks (MANET) due to different characteristics from wired networks are more vulnerable to security attacks. Construction of Intrusion Detection Systems (IDS) for MANETs is complicated by the fact that they are lack of fixed infrastructure and lack of central management for authentication and distribution of cryptographic keys. On the other hand, the network lifetime is an important issue in MANETs because of the energy power of mobile nodes is limited. In this paper is presented a proposed Method that in the first step, authorized nodes are detected by non-interactive zero knowledge technique and in the second step, nodes with the highest battery power from among authorized... 

    End-to-End adversarial learning for intrusion detection in computer networks

    , Article 44th Annual IEEE Conference on Local Computer Networks, LCN 2019, 14 October 2019 through 17 October 2019 ; Volume 2019-October , 2019 , Pages 270-273 ; 9781728110288 (ISBN) Mohammadi, B ; Sabokrou, M ; Sharif University of Technology
    IEEE Computer Society  2019
    Abstract
    This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity of network attacks in addition to the need for generalization, motivate us to propose a semi-supervised method. Inspired by the successes of Generative Adversarial Networks (GANs) for training deep models in semi-unsupervised setting, we have proposed an end-to-end deep architecture for IDS. The proposed architecture is composed of two deep networks, each of which trained by competing with each other to understand the underlying concept of the normal... 

    A semantic-based correlation approach for detecting hybrid and low-level APTs

    , Article Future Generation Computer Systems ; Volume 96 , 2019 , Pages 64-88 ; 0167739X (ISSN) Lajevardi, A. M ; Amini, M ; Sharif University of Technology
    Elsevier B.V  2019
    Abstract
    Sophisticated and targeted malwares, which today are known as Advanced Persistent Threats (APTs), use multi-step, distributed, hybrid and low-level patterns to leak and exfiltrate information, manipulate data, or prevent progression of a program or mission. Since current intrusion detection systems (IDSs) and alert correlation systems do not correlate low-level operating system events with network events and use alert correlation instead of event correlation, the intruders use low and hybrid events in order to distribute the attack vector, hide malwares behaviors, and therefore make detection difficult for such detection systems. In this paper, a new approach for detecting hybrid and...