Detecting DDoS attacks using adversarial neural network

Mustapha, Ali; Khatoun, Rida; Zeadally, Sherali; Chbib, Fadlallah; Fadlallah, Ahmad; Fahs, Walid; Attar, Ali El

doi:10.1016/j.cose.2023.103117

Cited by 36 publications

(9 citation statements)

References 15 publications

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“…The third layer ensures that all user database passwords are hashed and stored using Secure Hash Algorithm (SHA)-256 and SHA-512. For data in transit, to prevent distributed denial-of-service attacks, we use the Cloudflare-distributed denial-of-service mitigation service to ensure masking of website internet protocol address [ 69 , 70 ]. A distributed denial-of-service attack is a deliberate attempt to stymie a server, service, or network’s regular activity by saturating the target or its surrounding infrastructure with a deluge of internet traffic.…”

Section: Discussionmentioning

confidence: 99%

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

Katapally¹,

Ibrahim²

2023

JMIR Res Protoc

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Background The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making. Objective The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care. Methods The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory’s community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard’s interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data. Results The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security. Conclusions Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity. International Registered Report Identifier (IRRID) RR1-10.2196/46810

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Section: Discussionmentioning

confidence: 99%

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

Katapally¹,

Ibrahim²

2023

JMIR Res Protoc

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“…The system is aimed at being deployed for monitoring malicious attacks on the system's infrastructure. This system is designed to prevent various threats such as DDoS [29,30]MiTM attacks [31], SQL injections [12,32], phishing [33,34] and malware [35] and requires training corresponding ML models built on high-quality datasets. The scheme of threat detection with ML models in server and network infrastructure is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

The development of a model for the threat detection system with the use of machine learning and neural network methods

Ussatova,

Zhumabekova,

Karyukin

et al. 2024

ijirss

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This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

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“…In this approach, the attacker exploits node-level attributes. I. Debicha et al 17 and A. Mustapha et al 18 use network characteristics and the GAN model, respectively, to generate ADA traffic. It can evade detection while continuing to carry out its planned malevolent functions.…”

Section: Related Workmentioning

confidence: 99%

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Bhale

Biswas

Nandi

2023

Internet Technology Letters

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With the widespread adoption of Internet of Things (IoT) technologies, botnet attacks have become the most prevalent cyberattack. In order to combat botnet attacks, there has been a considerable amount of research on botnet attacks in IoT ecosystems by graph‐based machine learning (GML). The majority of GML models are vulnerable to adversarial attacks (ADAs). These ADAs were created to assess the robustness of existing ML‐based security solutions. In this letter, we present a novel adversarial botnet attack (ADBA) that modifies the graph data structure using genetic algorithms (GAs) to trick the graph‐based botnet attack detection system. According to the experiment results and comparative analysis, the proposed ADBA can be executed on resource‐constrained IoT nodes. It offers a substantial performance gain of 2.15 s, 52 kb, 92 817 mJ, 97.8%, and 27.74%–41.82% over other approaches in term of Computing Time (CT), Memory Usage (MU), Energy Usage (EU), Attack Success Rate (ASR) and Accuracy (ACC) metrics, respectively.

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Detecting DDoS attacks using adversarial neural network

Cited by 36 publications

References 15 publications

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

The development of a model for the threat detection system with the use of machine learning and neural network methods

Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

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