Increasing performance Of intrusion detection system using neural network

Kumar, Satendra; Yadav, Anamika

doi:10.1109/icaccct.2014.7019145

Cited by 40 publications

(11 citation statements)

References 5 publications

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“…Intrusion is the act of intruding or of entering into a place or virtual place without proper permission [1] [2]. For System security and confidentiality intrusion detection plays an important role.…”

Section: Introductionmentioning

confidence: 99%

“…Hackers can pass malicious traffic through ports that are commonly left open by system such as SMTP, HTTP etc. So the need for sophisticated IDS arises [2]. Based on the method of detection used IDSs can be classified as signature based or misuse based IDSs in which known attacks can be classified easily, it search network traffic for malicious packet.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of NSL-KDD dataset using ANN

Ingre

Yadav

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

Self Cite

294

114

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Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated using ANN. The result obtained for both binary class as well as five class classification (type of attack). Results are analyzed based on various performance measures and better accuracy was found. The detection rate obtained is 81.2% and 79.9% for intrusion detection and attack type classification task respectively for NSL-KDD dataset. The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of NSL-KDD dataset using ANN

Ingre

Yadav

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

Self Cite

294

114

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show abstract

“…The paper on Artificial network already said that the IDE should be depend on the cumulative data which can we collect at the different host of networks gather to one placed or we can shared data between different host might have taking advance futuristic approach given to the system by making analysis of different data set available with the system. NSL KDD dataset have most of the solutions [6], [7], [8] For more detailed analysis of the KDD Cup 1999, see [9].…”

Section: Related Workmentioning

confidence: 99%

Reveiw Paper on Intrusion Detection Based on Ann by Network Traffic Parameter

2018

IJMTER

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The Network Intrusion Detection is depending on different parameter of network, which is selected based on either signature or anomaly. In this paper we have review performance of anomaly based intrusion detection parameter like SYN flood attack, TCP/ UDP flood attack, nMap scanning attack, and also for non-malicious communication. The paper try to examine the different parameter of ID based result on the benchmark NSL-KDD dataset shows that the performance (accuracy and detection rate) of the ANN based IDS model is at par or in some cases even better than other IDS models based on parameter of Network.

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“…For example, in [12,13] the authors used support vector machines (SVM) to detect anomalies using KDD dataset [14]. In [15], the authors utilized artificial neural networks to build IDS models for anomaly detection using the same dataset. In [16], the author used cascaded classifiers to detect and classify anomalies in KDD dataset even if such anomalies were unequally distributed.…”

Section: A Related Workmentioning

confidence: 99%

Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments

Salman

Bhamare

Erbad

et al. 2017

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)

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Cloud computing has been widely adopted by application service providers (ASPs) and enterprises to reduce both capital expenditures (CAPEX) and operational expenditures (OPEX). Applications and services previously running on private data centers are now being migrated to private or public clouds. Since most of the ASPs and enterprises have globally distributed user bases, their services need to be distributed across multiple clouds, spread across the globe which can achieve better performance in terms of latency, scalability and load balancing. The shift has eventually led the research community to study multi-cloud environments. However, the widespread acceptance of such environments has been hampered by major security concerns. Firewalls and traditional rule-based security protection techniques are not sufficient to protect user-data in multi-cloud scenarios. Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99% detection accuracy and categorization accuracy of 93.6%, with the inability to categorize some attacks. Further, we argue that such categorization can be applied to multi-cloud environments using the same machine learning techniques.

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Increasing performance Of intrusion detection system using neural network

Cited by 40 publications

References 5 publications

Performance analysis of NSL-KDD dataset using ANN

Performance analysis of NSL-KDD dataset using ANN

Reveiw Paper on Intrusion Detection Based on Ann by Network Traffic Parameter

Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments

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