Defense-through-Deception Network Security Model: Securing University Campus Network from DOS/DDOS Attack

Naagas, Marlon A.; Mique, Eusebio L.; Palaoag, Thelma D.; Cruz, Josephine S. Dela

doi:10.11591/eei.v7i4.1349

Cited by 25 publications

(18 citation statements)

References 6 publications

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“…In this case, study of Wi-Fi intrusion was done by a neural network classifier [18]. For training purposes, the CICIDS dataset [19] is used to validate the intrusion detection algorithm with recurrent neural networks [20,21]. The present research paper is mainly based on IoT scenarios and help in detecting vulnerability concerning the IoT.…”

Section: Related Workmentioning

confidence: 99%

Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

2021

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The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.

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Section: Related Workmentioning

confidence: 99%

Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

2021

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“…On the other hand, DDoS attack packet flows produce low enough variance values close to zero due to the monotonous size of packets sent by bots to the target server [16]. The formula for calculating package size variances presented in (2). (2)…”

Section: The Variance Of Packet Size (In Bytes)mentioning

confidence: 99%

“…Distributed denial of service attacks (DDoS) is a type of attack that has a quite fatal impact on the target server [1]. DDoS attacks originate from a collection of small-sized data packet streams sourced from a large number of bot computers to produce a massive data flow directed at a target server [2]. DDoS attacks are controlled by attackers who coordinate a large number of bot/slave computers intending to shut down and deplete server resources, including CPU, memory, disk storage, and bandwidth, so that legitimate clients cannot access the allocated resources [3].…”

Section: Introductionmentioning

confidence: 99%

Multischeme feedforward artificial neural network architecture for DDoS attack detection

Muhammad¹,

Foozy

Mohammed

2021

Bulletin EEI

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Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, Scaled-Conjugate and Resilient-Propagation training functions. Based on the studies conducted, it concluded that ANN Architecture sufficiently affected the Scaled-Conjugate and Resilient-Propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the Quasi-Newton training function with 6-(12)-2 neural network architecture.

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“…The available NSL-KDD dataset in [19][20][21] was utilized to evaluate the ANN model in this study. The NSL-KDD Dataset is the update version of 99KDD dataset [22][23][24]. The NSL-KDD was recorded according to a practical 125973 requests of a website in 2013.…”

Section: Testing Datasetmentioning

confidence: 99%

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

Mohammed¹,

Arif²,

Ali³

2020

IJ-AI

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<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

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Defense-through-Deception Network Security Model: Securing University Campus Network from DOS/DDOS Attack

Cited by 25 publications

References 6 publications

Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

Multischeme feedforward artificial neural network architecture for DDoS attack detection

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

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