Detection and Defense Algorithms of Different Types of DDoS Attacks Using Machine Learning

Yusof, Mohd Azahari Mohd; Ali, Fakariah Hani Mohd; Darus, Mohamad Yusof

doi:10.1007/978-981-10-8276-4_35

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…False positive rates were compared with the Packet Threshold Algorithm-Support Vector Machine (PTA-SVM) model proposed by [18], Efficient Data Adapted Decision Tree (EDADT) proposed by [19], Lightweight detection algorithm proposed by [20], Modified K-means proposed by [21], and a logistic regression model proposed by [22]. The false positive rate for the proposed model was 0, outperforming the algorithms it was compared with, as shown in Figure 19.…”

Section: Test Results and Discussionmentioning

confidence: 99%

A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques

2019

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A denial of service (DoS) attack in a computer network is an attack on the availability of computer resources to prevent users from having access to those resources over the network. Denial of service attacks can be costly, capable of reaching $100,000 per hour. Development of easily-accessible, simple DoS tools has increased the frequency and reduced the level of expertise needed to launch an attack. Though these attack tools have been available for years, there has been no proposed defense mechanism targeted specifically at them. Most defense mechanisms in literature are designed to defend attacks captured in datasets like the KDD Cup 99 dataset from 20 years ago and from tools no longer in use in modern attacks. In this paper, we capture and analyze traffic generated by some of these DoS attack tools using Wireshark Network Analyzer and propose a signature-based DoS detection mechanism based on SVM classifier to defend against attacks launched by these attack tools. Our proposed detection mechanism was tested with Snort IDS and compared with some already existing defense mechanisms in literature and had a high detection accuracy, low positive rate and fast detection time.

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Section: Test Results and Discussionmentioning

confidence: 99%

A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques

2019

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“…Data cannot be exchange without networks; therefore, networking plays an important role in any field related to www.ijacsa.thesai.org information technology. That is why there are vicarious network attacks such as mentioned by [20], [21], [22] , and [23].…”

Section: A Network Typementioning

confidence: 99%

Multi-point Fundraising and Distribution via Blockchain

Alassaf¹,

Yusoff²

2021

IJACSA

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Trust and transparency are significant facets that are much esteemed by charitable organizations in achieving their mission and encouraging donations from the public. However, after many high-profile scandals, the faith in charities is questionable, heralding the need for an increased level of transparency among such organizations. Fortunately, leveraging Blockchain technology in charities' systems could help to rebuild the integrity of these organizations. This study aims to raise the level of integrity showcased by charities by creating a multi-point fundraising approach using smart contracts. The proposed system offers a transparent fundraising platform through its integration of charity organization evaluators. Various steps were deployed to satisfy the intended target. Firstly, the study investigated the potentials of Blockchain in improving the level of transparency. Secondly, a probing process was undertaken to choose a suitable platform as a server-side in the system. This process involved garnering salient features in Blockchain platforms based on the proposed system requirements. After the probing process, a Decision Support System (DSS) was utilized to investigate the most suitable Blockchain platform. Results garnered proved that the Ethereum platform is best for the proposed system.

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“…Attacks on the TCP/IP layer cripple online servers and have a cap on the number of requests that can be made per second. This category includes DDoS attacks, Slowloris, and zero-day attacks that exploit Apache or Windows flaws ( Yusof, Ali & Darus, 2018 ). The strategies proposed to understand DDoS assaults at the TCP/IP layer are merely a part of DDoS occurrences at the application layer.…”

Section: Introductionmentioning

confidence: 99%

Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks

Abbas,

Bouazzi,

Ojo

et al. 2024

PeerJ Computer Science

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The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity’s CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment.

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Detection and Defense Algorithms of Different Types of DDoS Attacks Using Machine Learning

Cited by 7 publications

References 15 publications

A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques

A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques

Multi-point Fundraising and Distribution via Blockchain

Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks

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