| مشخصات مقاله | |
| ترجمه عنوان مقاله | تقویت سیستم دفاعی انکار سرویس (DoS) در برابر حملات خصمانه در شبکه های خانه هوشمند اینترنت اشیا |
| عنوان انگلیسی مقاله | Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks |
| انتشار | مقاله سال 2021 |
| تعداد صفحات مقاله انگلیسی | 12 صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه الزویر |
| نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | Scopus – Master Journals List – JCR |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| ایمپکت فاکتور(IF) |
4.438 در سال 2020 |
| شاخص H_index | 92 در سال 2020 |
| شاخص SJR | 0.861 در سال 2020 |
| شناسه ISSN | 0167-4048 |
| شاخص Quartile (چارک) | Q1 در سال 2020 |
| فرضیه | ندارد |
| مدل مفهومی | ندارد |
| پرسشنامه | ندارد |
| متغیر | ندارد |
| رفرنس | دارد |
| رشته های مرتبط | مهندسی فناوری اطلاعات، کامپیوتر |
| گرایش های مرتبط | اینترنت و شبکه های گسترده، شبکه های کامپیوتری، امنیت اطلاعات |
| نوع ارائه مقاله |
ژورنال |
| مجله | کامپیوترها و امنیت – Computers & Security |
| دانشگاه | Cardiff University, School of Computer Science & Informatics, Cardiff, UK |
| کلمات کلیدی | اینترنت اشیا (IoT) ، خانه های هوشمند، شبکه سازی، یادگیری ماشینی تحت نظارت، یادگیری ماشین خصمانه، تشخیص حمله، سیستم های تشخیص نفوذ |
| کلمات کلیدی انگلیسی | Internet of things (IoT) – Smart homes – Networking – Supervised machine learning – Adversarial machine learning – Attack detection – Intrusion detection systems |
| شناسه دیجیتال – doi |
https://doi.org/10.1016/j.cose.2021.102352 |
| کد محصول | E15913 |
| وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
| دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
| سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
| فهرست مطالب مقاله: |
| Abstract Keywords Introduction Related work Attacking a supervised machine learning detector Adversarial machine learning Generating adversarial samples Evaluating the model on adversarial samples Defending against adversarial machine learning Conclusion Limitations and Future Work CRediT authorship contribution statement Declaration of Competing Interest Acknowledgements Appendix A. Supplementary materials Research Data References |
| بخشی از متن مقاله: |
| abstract Machine learning based Intrusion Detection Systems (IDS) allow flexible and efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However, this has also created an additional attack vector; the machine learning models which support the IDS’s decisions may also be subject to cyberattacks known as Adversarial Machine Learning (AML). In the context of IoT, AML can be used to manipulate data and network traffic that traverse through such devices. These perturbations increase the confusion in the decision boundaries of the machine learning classifier, where malicious network packets are often miss-classified as being benign. Consequently, such errors are bypassed by machine learning based detectors, which increases the potential of significantly delaying attack detection and further consequences such as personal information leakage, damaged hardware, and financial loss. Given the impact that these attacks may have,this paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning classifiers used for detecting Denial of Service attacks in an IoT smart home network. The analysis explores which DoS packet features to perturb and how such adversarial samples can support increasing the robustness of supervised models using adversarial training. The results demonstrated that the performance of all the top performing classifiers were affected, decreasing a maximum of 47.2 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks. Introduction The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate everyday tasks, they also introduce tremendous security flaws. Current insufficient security mea-sures employed to defend smart devices make IoT the ‘weakest’ link to breaking into a secure infrastructure, and therefore an attractive target to attackers. As the number of IoT devices increases exponentially (Gubbi et al., 2013), the number of unknown vulnerabilities and threats also increases, resulting in perimeter defences becoming weaker. Intrusion Detection Systems (IDSs) have emerged as successful attack detection and identification methods in IoT networks. In particular, due to the rapid increase in the development of IoT devices, their heterogeneity, and the amount of data that is produced from such technologies, machine learning techniques have been integrated to support IDSs in IoT networks to defend against a greater array of attacks (e.g. Amouri et al., 2018; Anthi et al., 2018; Doshi et al., 2018; McDermott et al., 2018; Meidan et al., 2018; Shukla, 2017). |