Les systèmes de détection d'intrusion basés sur du machine learning (original) (raw)
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Intrusion Detection Using Machine Learning
Innovations and Systemic Approaches
Intrusion detection has received enormous attention from the beginning of computer network technology. It is the task of detecting attacks against a network and its resources. To detect and counteract any unauthorized activity, it is desirable for network and system administrators to monitor the activities in their network. Over the last few years a number of intrusion detection systems have been developed and are in use for commercial and academic institutes. But still there have some challenges to be solved. This chapter will provide the review, demonstration and future direction on intrusion detection. The authors’ emphasis on Intrusion Detection is various kinds of rule based techniques. The research aims are also to summarize the effectiveness and limitation of intrusion detection technologies in the medical diagnosis, control and model identification in engineering, decision making in marketing and finance, web and text mining, and some other research areas.
Journal of Intelligent & Fuzzy Systems
Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a network has been fundamental since few years. With increasing use of machine learning techniques, it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called 'adversarial learning' have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by the attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poisoned learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning-based classification algorithms.
Machine Learning for Intrusion Detection Systems
2019
In recent decade most of technologies are evolved and there security handling also improved. In which, IDS is the software which is used to detect unauthorized intruders in the network. Even though the highly secure devices and there security feature are developed day-by-day. The malicious hackers update their techniques to crack the security by identifying the vulnerability in the network. Lots of intrusion detection algorithms are used in networking devices, most of the IDS attacks are introduced in common networking devices such as router, switches, networking tapes etc. Researchers found various algorithms for detection of intruders in the network. At last, we arrives Machine Learning algorithms for detection of intruders in the network. Machine Learning approaches are rapidly emerging in various extents nowadays, But most of the algorithms results in the sarcastic manner due to its redundancies. In this paper, we surveyed huge number of existing systems regarding IDS and its im...
With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework " s security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.
A Literature Survey and Comprehensive Study of Intrusion
With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. As the threat becomes a serious matter year by year, intrusion detection technologies are indispensable for network and computer security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. In order to detect the intrusion, various approaches have been developed and proposed over the last decade. In this paper, a detailed survey of intrusion detection based various techniques has been presented. Here, the techniques are classified as follows: i) papers related to Neural network ii) papers related to Support vector machine iii) papers related to K-means classifier iv) papers related to hybrid technique and v) paper related to other detection techniques. For comprehensive analysis, detection rate, time and false alarm rate from various research papers have been taken.
Machine Learning: A Solution for Intrusion Detection
Millions of users share resources and send and receive data daily through Internet. However, they are certainly at risk of data theft and other attacks due to this connectivity. Researchers are showing increasing trends in security related attacks. Network security has thus become one of the most active research fields. Intrusion Detection Systems (IDS) are commonly used for detection of attacks in a Network due to its ability to detect unknown attacks. Many techniques, ranging from statistical approaches to Artificial Intelligence (AI) based approaches have been presented in literature. AI based techniques have gained a lot of popularity in research community due to its various benefits. In this paper, we present a survey of Intrusion Detection Systems based on machine learning techniques. KEYWORDS: ANN, Markov Model, Bayesian Network, Intrusion Detection System
An Introduction to Intrusion-Detection Systems
Intrusion-detection systems aim at detecting attacks against computer systems and networks or, in general, against information systems. Indeed, it is difficult to provide provably secure information systems and to maintain them in such a secure state during their lifetime and utilization. Sometimes, legacy or operational constraints do not even allow the definition of a fully secure information system. Therefore, intrusion-detection systems have the task of monitoring the usage of such systems to detect any apparition of insecure states. They detect attempts and active misuse either by legitimate users of the information systems or by external parties to abuse their privileges or exploit security vulnerabilities. This paper is the first in a two-part series; it introduces the concepts used in intrusion-detection systems around a taxonomy.
IJERT-A Survey Paper on Machine Learning Approaches to Intrusion Detection
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/a-survey-paper-on-machine-learning-approaches-to-intrusion-detection https://www.ijert.org/research/a-survey-paper-on-machine-learning-approaches-to-intrusion-detection-IJERTV10IS010040.pdf This electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. For any nation, government, or cities to compete favorably in today's world, it must operate smart cities and e-government. As trendy as it may seem, it comes with its challenges, which is cyber-attacks. A lot of data is generated due to the communication of technologies involved and lots of data are produced from this interaction. Initial attacks aimed at cyber city were for destruction, this has changed dramatically into revenue generation and incentives. Cyber-attacks have become lucrative for criminals to attack financial institutions and cart away with billions of dollars, led to identity theft and many more cyber terror crimes. This puts an onus on government agencies to forestall the impact or this may eventually ground the economy. The dependence on cyber networked systems is impending and this has brought a rise in cyber threats, cyber criminals have become more inventive in their approach. This proposed dissertation discusses various security attacks classification and intrusion detection tools which can detect intrusion patterns and then forestall a break-in, thereby protecting the system from cyber criminals. This research seeks to discuss some Intrusion Detection Approaches to resolve challenges faced by cyber security and e-governments; it proffers some intrusion detection solutions to create cyber peace. It discusses how to leverage on big data analytics to curb security challenges emanating from internet of things. This survey paper discusses machine learning approaches to efficient intrusion detection model using big data analytic technology to enhance computer cyber security systems.