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intrusion detection systems explained by iskrena

Santé & Médecine Technologie

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Types of Intrusion Detection Systems ,[object Object],[object Object],[object Object]

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Intrusion Prevention Systems (IPSs) ,[object Object],[object Object],[object Object]

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When talk about intrusion, then it is pre- assume that the intrusion is happened or it is stopped by the intrusion detection system. This is all done through the process of collection of network traffic information at certain point of networks in the digital system. In this way the IDS perform their job to secure the network. There are two types of Intrusion Detection: First is Misuse based detection and second one is Anomaly based detection. The detection which uses data set of known predefined set of attacks is called Misuse - Based IDSs and Anomaly based IDSs are capable of detecting new attacks which are not known to previous data set of attacks and is based on some new heuristic methods. In our hybrid IDS for computer network security we use Min-Min algorithm with neural network in hybrid method for improving performance of higher level of IDS in network. Data releasing is the problem for privacy point of view, so we first evaluate training for error from neural network regression state, after that we can get outer sniffer by using Min length from source, so that we hybridized as with Min – Min in neural network in hybrid system which we proposed in our research paper

A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...

IJCSIS Research Publications

Introduction IDS

Hitesh Mohapatra

AN INTRUSION DETECTION SYSTEM

Apoorv Pandey

Vol 6 No 1 - October 2013

ijcsbi

Intrusion Detection systems (IDS) are an essential element for Network Security Infrastructure and play an important role in detecting large number of attacks. Intrusion Prevention System (IPS) is a tool that is used to prevent spywares from getting intrusion into a system and one of the techniques used in IPS is Completely Automated Public Turning test to tell Computers and Human Apart (CAPTCHA). In order to detect illegal access of the web from the intruder, IDS, IPS can be implemented with the use of honeypot to track the IP address, location and country or region of the attacker in order to block the attacker from accessing the system. Different techniques have been adopted by different researchers using IDS, IPS and honeypot to protect their system against illegal attacks. As discovered in the existing systems CAPTCHA was not employed in IDS to detect spywares capable of breaking and having access to the system. To increase and maintain the security in a Network the combination of IDS with CAPTCHA, IPS and a dummy Honeypot can be employed. This work proposes a CAPTCHA –based Intrusion Detection Model with a redirector in order to identify the intelligent spywares that are capable of breaking CAPTCHA in IPS. Also using a dummy honeypot with circular hyperlinks so as to lewd the software that infiltrated the system in order to capture its IP address and other important information about the spywares such as the country or region it’s coming from, web browser used and date and time of intrusion so as to block and prevent illegal access by intruders. This paper focuses on capturing the intelligent spywares capable to break through the new CAPTCHA trap IDS so as to gather information about it and necessary action can be taken against it. A security model was designed having having CAPTCHA IDS with a redirector, IPS and a honeypot cable of detecting intrusion by intelligent spyware With this model the network will be more secured against intrusion by spywares

A CAPTCHA – BASED INTRUSION DETECTION MODEL

ijseajournal

1776 1779

Editor IJARCET

In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian network.

Detecting Anomaly IDS in Network using Bayesian Network

IOSR Journals

Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.

NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...

ijsptm

Malicious activities (malcodes) are self replicating malware and a major security threat in a network environment. Timely detection and system alert flags are very essential to prevent rapid malcodes spreading in the network. The difficulty in detecting malcodes is that they evolve over time. Despite the fact that signature-based tools, are generally used to secure systems, signature-based malcode detectors neglect to recognize muddled and beforehand concealed malcode executables. Automatic signature generation systems has likewise been use to address the issue of malcodes, yet there are many works required for good detection. Base on the behavior way of malcodes, a behavior approach is required for such detection. Specifically, we require a dynamic investigation and behavior Rule Base system that distinguishes malcodes without erroneously block legitimate traffic or increase false alarms. This paper proposed and discussed the approach using Machine learning and Indicators of Compromise (IOC) to analyze intrusion in a network, to identify the cause of the attack and to provide future detection. This paper proposed the use of behaviour malware analysis framework to analyze intrusion data, apply clustering algorithm on the analyzed data and generate IOC from the clustered data for IOCRule, which will be implemented into Snort Intrusion Detection System (IDS) for malicious code detection.

Malicious Code Intrusion Detection using Machine Learning and Indicators of C...

IJCSIS Research Publications

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Divya Gowda

A hybrid intrusion detection system for cloud computing environments

Mohamed Jelidi

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IRJET Journal

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A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan

Optimized Intrusion Detection System using Deep Learning Algorithm

ijtsrd

50320130403001 2-3

IAEME Publication

50320130403001 2-3

IAEME Publication

information security (Audit mechanism, intrusion detection, password manageme...

Zara Nawaz

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Bt33430435

IJERA Editor

Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.

The Practical Data Mining Model for Efficient IDS through Relational Databases

IJRES Journal

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IOSR Journals

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IJERA Editor

Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.

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IAEME Publication

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iaemedu

A review of anomaly based intrusions detection in multi tier web applications

IAEME Publication

With the rapid advancement of computer technology during the last couple of decades. Computer systems are commonly used in manufacturing, corporate, as well as other aspects of human living. As a result, constructing dependable infrastructures is a major challenge for IT managers. On the contrary side, this same rapid advancement of technology has created numerous difficulties in building reliable networks which are challenging tasks. There seem to be numerous varieties of attacks that affect the accessibility, authenticity, as well as secrecy of communications systems. In this paper, an in-depth and all-inclusive description of artificial intelligence methods used for the detection of network intrusions is discussed in detail.

Analysis of Artificial Intelligence Techniques for Network Intrusion Detectio...

IIJSRJournal

An intrusion detection system detects various malicious behaviors and abnormal activities that might harm security and trust of computer system. IDS operate either on host or network level via utilizing anomaly detection or misuse detection. Main problem is to correctly detect intruder attack against computer network. The key point of successful detection of intrusion is choice of proper features. To resolve the problems of IDS scheme this research work propose “an improved method to detect intrusion using machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three algorithms J48, J48Graft and Random forest gives much better results than other machine learning algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.

AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS

ieijjournal1

AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS

ieijjournal

Abstract There is a huge advancement in Computer networking in the past decade. But with the advancement, the threats to the computer networks are also increased. Today one of the biggest threats to the computer networks is the Distributed Denial of Service (DDoS) flooding attack. This paper emphasizes the application layer DDoS flooding attacks because these (layer seven) attacks are growing rapidly and becoming more severe problem. Many researchers used machine-learning techniques for intrusion detection, but some shows poor detection and some methods take more training time. From a survey, it is found that Naïve Bayes (NB) algorithm provides faster learning/training speed than other machine learning algorithms. Also it has more accuracy in classification and detection of attack. So we are proposing a network intrusion detection system (IDS) which uses a machine learning approach with the help of NB algorithm. Keywords: DDoS (Distributed Denial of Service) flooding attack, Machine Learning, Naïve Bayes, Network Intrusion Detection

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