Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model

Thajeel, Isam Kareem; Samsudin, Khairulmizam; Hashim, Shaiful Jahari; Hashim, Fazirulhisyam

doi:10.1016/j.jksuci.2023.01.012

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…A popular injection attack is XSS which injects malicious code into valid sites or in web applications [62], [63], [64]. This form of attack occurs when attackers insert JavaScript or code into the databases of websites, which makes users to download them.…”

Section: Cross Site Scripting (Xss)mentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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Section: Cross Site Scripting (Xss)mentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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“…Another challenge of deep learning methods is that the feature selection phase is entrusted to the first layers, reducing the model's accuracy. Separating the feature selection phase and applying learning on the selected features increases the modeling accuracy [27]. Networks like CNN have a set of parameters in intrusion detection, and if these parameters are not chosen optimally, then the modeling error increases.…”

Section: Introductionmentioning

confidence: 99%

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

Al-Sarray,

Rahebi,

Demirhan

2024

Preprint

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The Software-defined Networking (SDN) system plays a crucial role in efficiently overseeing the Internet network by segregating the control and data planes. In the SDN, the controller manages and determines the policy in sending data and setting the SDN switches. Despite the significant advantages, the SDN network has security challenges. DDoS attacks are the main challenge in SDN networks. DDoS attacks primarily target the SDN controller to disrupt network performance. Intrusion detection systems in SDN networks need confidential methods for message exchange and coordination of controllers so that they can exchange the blacklist of attacking addresses with each other. In this manuscript, we introduce an approach utilizing 1D CNN and LSTM networks for detecting attacks in the SDN network, incorporating blacklist information hidden in images. In the first stage, game theory and deep learning based on GAN are used to increase attack detection accuracy in each SDN controller to balance the data set. In the second stage, each controller uses 1D CNN to extract the primary features, and the Siberian tiger optimization (STO) algorithm is applied to enhance the efficiency of this deep learning network. In the third step, the STO algorithm selects the optimal features. Finally, the LSTM network classifies traffic by receiving the selected features. SDN controllers use image encryption to increase privacy and security for exchanging and sharing blacklists. The tests performed in Python and on datasets UNSW-NB15, CIC-IDS2017, and NSL-KDD are 99.49%, 99.86%, and 99.91%. The proposed method GAN-CL-STO demonstrates higher accuracy in detecting network attacks compared to the CNN-LSTM, HODNN+CRF, 1D CNN, PSO-1D CNN, and 1D CNN+BiLSTM methods. The suggested approach for identifying attacks in SDN is more accurate than WOA, HHO, and COA feature selection methods.

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“…The outcomes of this transformation have been remarkable, with substantial gains in operational efficiency, reduced overheads, and enhanced product quality. The role of AI in this phase has been pivotal, automating complex processes, optimizing supply chains, and facilitating predictive maintenance to minimize downtime [7]. In contrast, Industry 5.0 heralds a shift towards a more profound collaboration between humans and machines.…”

Section: Introductionmentioning

confidence: 99%

Building Trust in AI-Human Partnerships: Exploring Preferences and Influences in the Manufacturing Industry

Żywiołek

2024

Management Systems in Production Engineering

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The incorporation of artificial intelligence (AI) into industrial processes has seen substantial development, characterized by the shift from Industry 4.0 to the future concept of Industry 5.0. The article identifies a significant gap in knowledge regarding how openness in AI engagement influences consumer trust and confidence in news media. This gap highlights the need for further exploration into the relationship between transparency in AI processes and consumer perceptions. The research utilises a combination of qualitative and quantitative approaches, gathering insights from academic literature, industry viewpoints, and actual data. We conduct an extensive analysis of existing literature to investigate the process of incorporating artificial intelligence into news creation and its influence on the level of confidence consumers have in the news. We have identified a significant lack of knowledge about the impact of openness in AI engagement on consumer views and trust in news media. Expanding on this discrepancy, we suggest a systematic methodology that incorporates controlled experiments and surveys to evaluate the influence of different degrees of openness on consumer trust and involvement with AI-generated news content. In addition, the paper examines the difficulties in establishing confidence in artificial intelligence (AI) inside the European Union, including several aspects such as technological, ethical, social, and legal considerations. The document presents a thorough plan to guarantee the secure development and execution of AI, with a focus on the significance of transparency, ethics, and teamwork. The study’s results provide vital insights for politicians, news organisations, and industrial businesses as they navigate the intricate process of integrating AI. Comprehensive Plan for Secure AI Development, to address the challenges outlined, the article presents a thorough plan for ensuring the secure development and execution of AI within the European Union. This plan emphasizes the significance of transparency, ethics, and collaboration in building trust and confidence in AI technologies.

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Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model

Cited by 10 publications

References 42 publications

Assessment of existing cyber-attack detection models for web-based systems

Assessment of existing cyber-attack detection models for web-based systems

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

Building Trust in AI-Human Partnerships: Exploring Preferences and Influences in the Manufacturing Industry

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