Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems
Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based...
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MDPI AG
2024-12-01
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| author | Meaad Ahmed Qutaiba Alasad Jiann-Shiun Yuan Mohammed Alawad |
| author_facet | Meaad Ahmed Qutaiba Alasad Jiann-Shiun Yuan Mohammed Alawad |
| author_sort | Meaad Ahmed |
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| description | Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based intrusion detection systems (IDSs) are mainly designed to detect malicious network traffic. Unfortunately, ML models have recently been demonstrated to be vulnerable to adversarial perturbation, and therefore enable potential attackers to crash the system during normal operation. Among different attacks, generative adversarial networks (GANs) have been known as one of the most powerful threats to cybersecurity systems. To address these concerns, it is important to explore new defense methods and understand the nature of different types of attacks. In this paper, we investigate four serious attacks, GAN, Zeroth-Order Optimization (ZOO), kernel density estimation (KDE), and DeepFool attacks, on cybersecurity. Deep analysis was conducted on these attacks using three different cybersecurity datasets, ADFA-LD, CSE-CICIDS2018, and CSE-CICIDS2019. Our results have shown that KDE and DeepFool attacks are stronger than GANs in terms of attack success rate and impact on system performance. To demonstrate the effectiveness of our approach, we develop a defensive model using adversarial training where the DeepFool method is used to generate adversarial examples. The model is evaluated against GAN, ZOO, KDE, and DeepFool attacks to assess the level of system protection against adversarial perturbations. The experiment was conducted by leveraging a deep learning model as a classifier with the three aforementioned datasets. The results indicate that the proposed defensive model refines the resilience of the system and mitigates the presented serious attacks. |
| format | Article |
| id | doaj-art-f1e9059fee1949d88c4c41dd7db0d7b6 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-f1e9059fee1949d88c4c41dd7db0d7b62024-12-27T14:10:49ZengMDPI AGBig Data and Cognitive Computing2504-22892024-12-0181219110.3390/bdcc8120191Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity SystemsMeaad Ahmed0Qutaiba Alasad1Jiann-Shiun Yuan2Mohammed Alawad3Department of Computer Science, Tikrit University, Al-Qadesiyya District, Tikrit 34001, IraqDepartment of Cybersecurity, Tikrit University, Al-Qadesiyya District, Tikrit 34001, IraqDepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USADepartment of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USACybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based intrusion detection systems (IDSs) are mainly designed to detect malicious network traffic. Unfortunately, ML models have recently been demonstrated to be vulnerable to adversarial perturbation, and therefore enable potential attackers to crash the system during normal operation. Among different attacks, generative adversarial networks (GANs) have been known as one of the most powerful threats to cybersecurity systems. To address these concerns, it is important to explore new defense methods and understand the nature of different types of attacks. In this paper, we investigate four serious attacks, GAN, Zeroth-Order Optimization (ZOO), kernel density estimation (KDE), and DeepFool attacks, on cybersecurity. Deep analysis was conducted on these attacks using three different cybersecurity datasets, ADFA-LD, CSE-CICIDS2018, and CSE-CICIDS2019. Our results have shown that KDE and DeepFool attacks are stronger than GANs in terms of attack success rate and impact on system performance. To demonstrate the effectiveness of our approach, we develop a defensive model using adversarial training where the DeepFool method is used to generate adversarial examples. The model is evaluated against GAN, ZOO, KDE, and DeepFool attacks to assess the level of system protection against adversarial perturbations. The experiment was conducted by leveraging a deep learning model as a classifier with the three aforementioned datasets. The results indicate that the proposed defensive model refines the resilience of the system and mitigates the presented serious attacks.https://www.mdpi.com/2504-2289/8/12/191network-based intrusion detection systemszeroth-order optimizationkernel density estimationgenerative adversarial networksDeepFooldeep learning |
| spellingShingle | Meaad Ahmed Qutaiba Alasad Jiann-Shiun Yuan Mohammed Alawad Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems Big Data and Cognitive Computing network-based intrusion detection systems zeroth-order optimization kernel density estimation generative adversarial networks DeepFool deep learning |
| title | Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems |
| title_full | Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems |
| title_fullStr | Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems |
| title_full_unstemmed | Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems |
| title_short | Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems |
| title_sort | re evaluating deep learning attacks and defenses in cybersecurity systems |
| topic | network-based intrusion detection systems zeroth-order optimization kernel density estimation generative adversarial networks DeepFool deep learning |
| url | https://www.mdpi.com/2504-2289/8/12/191 |
| work_keys_str_mv | AT meaadahmed reevaluatingdeeplearningattacksanddefensesincybersecuritysystems AT qutaibaalasad reevaluatingdeeplearningattacksanddefensesincybersecuritysystems AT jiannshiunyuan reevaluatingdeeplearningattacksanddefensesincybersecuritysystems AT mohammedalawad reevaluatingdeeplearningattacksanddefensesincybersecuritysystems |