Neural network based intrusion detection systems with different training functions

Karataş, Gözde; Şahingöz, Özgür Koray

doi:10.1109/isdfs.2018.8355327

Cited by 58 publications

(16 citation statements)

References 12 publications

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“…KDD Cup99 was created in 1998 by DARPA to detect anomaly network traffic and was used in the 1999 KDD Cup Challenge to test IDSs [16], [17]. In its construction, nine weeks of LAN raw data which results in a TCP dump are used.…”

Section: A Kdd Cup99mentioning

confidence: 99%

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

2020

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In recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don't contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.

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Section: A Kdd Cup99mentioning

confidence: 99%

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

2020

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“…Some researchers also attempt to use the Genetic Algorithm (GA), but it was found that Immune algorithms were more effective than GA to Intrusion Detection [84]- [86]. Nonetheless, several studies have used the Neural net over the years [87]- [89]. For instance, in 2018, [89]…”

Section: E Intrusion Detectionmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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“…Besides, in intrusion detection systems, only a few number of security alarms should be detected and handled [23]- [27].…”

Section: A Importance Of Rare Events With Small Probabilitymentioning

confidence: 99%

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

et al. 2019

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In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information on big data collected by the Internet of Things (IoT). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with the importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in the IoT, are introduced which can be considered as future research directions.

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Neural network based intrusion detection systems with different training functions

Cited by 58 publications

References 12 publications

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

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