Intrusion Detection System in IoT Wi-Fi Networks: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Samson Kahsay Gebresilassie
,
Joseph Rafferty
,
Liming Chen
,
Zhan Cui
,
Mamun Abu-Tair

The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this research was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for deauthentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate the dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this research allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures.

  • de-authentication and disassociation attacks
  • intrusion detection system
  • convolutional neural network
  • transfer learning
  • IoT Wi-Fi networks

1. Introduction

The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies with the capabilities of gathering and exchanging data via the Internet, allowing them to communicate with one another and execute a variety of functions independently. One of the important wireless communication technologies for establishing connections and enabling communication in IoT contexts is the IEEE 802.11 wireless local area network (WLAN) [1][2][3][4][5][6], commonly called Wi-Fi. It is one of the fastest-growing technologies and a widely deployed type of network due to ease of installation, flexibility, mobility, reduced cost of ownership, and scalability [7][8]. Devices in the IoT system face various security challenges including attacks launched to target different layers of the stack [9]. Wi-Fi-enabled IoT is deployed almost everywhere, ranging from common households to large-scale critical infrastructures with a rapidly growing number of devices such as smartphones, laptops, tablets, smartwatches, IoT devices, etc., along with the advancement and growth of various services such as cloud-based data storage, social networking, contented services, online banking, e-commerce, etc. However, due to the broadcast nature of the wireless signal on an open wireless medium, protocols, and mechanism design vulnerabilities, IoT Wi-Fi networks are exposed to various attacks from hackers and intruders. The insecurity of these Wi-Fi-enabled IoT devices is increasingly growing as they become easy targets of various attacks. De-authentication and disassociation DoS attacks are among those security challenges where attackers can easily spoof the MAC address of the client or the AP while they are in the process of genuine authentication/association and de-authentication/disassociation by carrying out an attack on their behalf which can result in turning down the Wi-Fi network and denying services. Common Vulnerabilities and Exposures (CVE) recorded a number of de-authentication and disassociation attacks carried out against different Wi-Fi networks and environments [10].
There is a need for more secure Wi-Fi networks [11] and several advancements have been made. The first approach is the innovation of Wi-Fi communication technology itself, including the latest security enhancement with Wi-Fi Protected Access 3 (WPA 3) [12], and performance enhancement with IEEE 802.11ax [13] (commonly called Wi-Fi 6). However, WPA 3 has potential flaws and vulnerabilities [14], one of which is vulnerability to Denial of Service (DoS) attacks aimed at disconnecting network devices, with de-authentication and disassociation included among such successful attacks. The other approach is the development and advancement of different intrusion detection systems (IDS), a vital cybersecurity technique, deployed to operate as a second security protection, monitoring and identifying unusual activities carried out by attackers that influence the availability of the Wi-Fi network. A number of machine and deep learning-based IDS solutions have been developed over the recent years to solve some of these problems [15][16][17][18][19][20][21][22][23]. For example, the authors of [15] identified different Wi-Fi vulnerabilities by carrying out various attacks and recommended strong encryption, employee training, and improving organizational security standards. In [17], the authors proposed a Wireless Intrusion Detection System to detect attacks on Wi-Fi networks while [14] introduced a WIDS to classify Wi-Fi traffic captured with n-grams into normal or malicious using machine learning models. Machine and deep learning-based IDSs can potentially offer better security by design, and therefore enhance the security of IoT Wi-Fi network infrastructures. However, the solutions mentioned above still face difficulties, including failing to achieve better performance, lacking up-to-date datasets, and not providing end-to-end solutions. Using old datasets can cause less effective attack detection as it may not capture the current state of Wi-Fi networks (including updated security protocols, authentication methods, or encryption standards), may fail to accurately represent the current attack landscape, and can miss already known vulnerabilities and weaknesses addressed by the latest security mechanisms. In addition, according to the researchers' knowledge, current solutions fail to provide an up-to-date end-to-end solution with real-time automated Wi-Fi network traffic analysis and attack detection mechanisms. Moreover, despite their rapid growth and importance, such security risks are not properly addressed in IoT systems such as smart home Wi-Fi network environments.

2. Intrusion Detection System (IDS)

An intrusion in cyber security refers to unauthorized access or entry into a node or network system. This can be accomplished through various methods such as hacking, malware, or social engineering. An intrusion’s goal could be to steal sensitive information, disrupt operations, or gain control of a targeted node or an entire network. To protect against these types of threats, intrusion detection and prevention are critical aspects of cyber security.
An intrusion detection system (IDS) detects malicious user’s unauthorized access to systems or networks. IDSs’ primary duties are to monitor hosts and networks, evaluate computer system activity, produce warnings, and respond to suspicious behavior. The basic architecture of an IDS, as shown in Figure 1 (adapted from [24]), consists of a data collection device (sensor), an intrusion detection engine, a knowledgebase (database), a configuration device, and a response component [25]. Based on different factors, IDSs can be classified into various types among which network IDS (NIDS) is widely deployed and  the researchers' solution falls under this category. A Network Intrusion Detection System (NIDS) typically examines network traffic for patterns or anomalies that might reveal an intrusion. The NIDS is often deployed on the border router or switches and monitors network traffic flow to identify threats that occur across the network connection [26]. It operates on the OSI Model’s Network Layer or Data Link Layer. Most NIDSs are independent of the operating system (OS), allowing them to be used in various OS scenarios. Furthermore, NIDSs can identify specific protocol types and network assaults. The disadvantage is that they only monitor traffic moving via a certain network section.
Figure 1. Basic architecture of IDSs.

3. Machine and Deep Learning for IDS

Both traditional machine learning (TML) and deep learning approaches prove their capability for the advancement of IDSs. However, because of the speed and amount of IoT-produced data, TML approaches continue to face several challenges in extracting relevant features from the massive and unstructured data created by IoT devices as they need well-crafted feature engineering. Deep learning methods have become the preferred approach in recent years due to their ability to learn features automatically, efficiently handle large-scale datasets, and adapt and learn new data with less extensive retraining, as well as their flexibility and capability of capturing nonlinear relationships in data.

3.1. Convolutional Neural Networks

A convolutional neural network (CNN) is one of the most often utilized forms of neural networks in computer vision. It proved to be effective in many challenges such as face recognition [27], object detection [28], picture classification [25], image restoration [29], image captioning [30], industrial applications [31], audio recognition [32], and so on, which makes it critically useful in image analysis. The basic architecture of a CNN, as shown in Figure 2 (adapted from [33]), consists of different types of layers among which the main layers are convolutional, pooling, rectification, and flatten layers. At the core of a CNN is the convolutional layer whose units are organized into output feature maps following the convolution operation using filters or kernels. The output feature maps are then passed through the pooling layer to reduce their dimensions and the number of overfitting parameters which in turn accelerates neural network performance and leads to faster training. At the same time, it keeps the majority of the dominant information (or features) in every stage of the pooling process. Furthermore, pooling filters aid in learning the most important features and removing outliers and inconsistencies. Classifiers are often composed of fully connected layers, and they carry out classification tasks according to the identified features. In general, in contrast to other DL or ML algorithms that can be over-fitted with massive amounts of data, CNNs can instantly determine the sort of attack.
Figure 2. The basic architecture of a CNN.

3.2. Transfer Learning

Transfer learning (TL) [34] is a powerful method for exploiting deep neural networks on smaller datasets. The goal of transfer learning is to transfer the structure and parameters of large-scale dataset-trained models (e.g., AlexNet, VGGNet, and ResNet) to new tasks and use the weights trained on the large dataset as the initial weights for the new task [18]. As a result, TL leverages the knowledge learned from the source domain to solve the problem in the target domain without the need to learn from scratch with a massive amount of data. As opposed to training new models from the beginning using new datasets, we may leverage the patterns learned from datasets like ImageNet (millions of photos of various things) as the foundation of the new problem, as shown in Figure 3.
Figure 3. Use of transfer learning and CNNs.

4. IoT and the Wi-Fi Protocol

The Internet of Things (IoT) is a network of billions of interconnected physical devices embedded with sensors, software, and communication technologies with the capabilities of gathering and exchanging data via the Internet, allowing them to communicate with one another and execute a variety of functions independently. Wi-Fi has become part of our daily lives and more popular for use in many areas of application including smart home equipment due to its low cost, ubiquity, and ease of connecting. The continuous decline in the price of Wi-Fi chipsets has contributed significantly to its expansion. With the rapidly increasing IoT advancement and billions of connected objects, Wi-Fi applications reached new heights. More than 37 billion Wi-Fi-enabled devices were shipped in 2021, and the global value of Wi-Fi is estimated to be $4.9 trillion by 2025 [35]. The increasing deployment of IoT devices is the primary driver of this market growth.
Wi-Fi-enabled devices communicate with an intermediary device such as an access point (AP), a networking device linked to a wired or cellular network through radio signals over the airwaves, via Wi-Fi. The AP effectively turns Internet data into radio waves and transmits them into the surrounding area.

De-Authentication and Disassociation Attacks

An IoT Wi-Fi network has many vulnerabilities such as an open wireless medium, lack of robust encryption protocols, the unprotected nature of the management frames, and insufficient validation of de-authentication frames. These security challenges make an IoT Wi-Fi network vulnerable to various attacks such as Denial of Service (DoS), spoofing, eavesdropping, Man-In-The-Middle Attack, and many others [36]. The de-authentication and disassociation DoS attacks are the attack scenarios that this work focuses on. The security evaluation of a network generally involves different phases such as exploration, analysis, attack, and operation. For the attack scenarios, network traffic associated with the association/disassociation process of the 802.11 Wi-Fi networks is the focus of the analysis and attack detection.

5. De-Authentication DoS Attack

De-authentication attacks are classified as management frame attacks and fall under the Denial of Service attack which targets disrupting the communication between users (stations) and the Wi-Fi AP [37]. Normally, a de-authentication frame is used to gracefully end a connection between a connected client and an access point. The AP or the station can invoke the de-authentication due to shutdown, out of coverage, or other reasons. When the AP receives this frame, it also transmits the de-authentication frame back to the client. While this is a normal process for de-authentication, an attacker can take advantage of this process. An attacker exploits this regular process by first waiting for a client to authenticate with the AP and then launching attacks by spoofing the MAC address of a target client and sending the de-authentication frame to the AP on behalf of the victim. This disconnects the connection of the station to the AP. The attacker then spoofs this client’s MAC address and delivers the de-authentication frame to the AP. The entire process is shown in Figure 4 (adapted from [38]).
Figure 4. An attacker carrying out a de-authentication DoS Attack.

6. Disassociation DoS Attack

An existing association is terminated via a disassociation frame. Any of the two associated stations can initiate a disassociation notice. Disassociation cannot be denied since it is a notification rather than a request. The receiving station clears the appropriate states and keys from its memory in response to the disassociation notice. Stations often dissociate when they leave the network or when they relocate and want to join another network. If an AP cannot manage all of its associated stations or is restarting, it broadcasts a broadcast disassociation to disconnect all of them. Because the disassociation frame is neither encrypted nor authenticated, it is vulnerable to spoofing. Anyone may spoof the source address (SA) using MAC spoofing tools and techniques such as Aircrack-ng, MAC changer, Scapy, or custom scripts. By sending a faked disassociation frame, the attacker can force the target victim client to dissociate, as shown in Figure 5 (adapted from [38]).
Figure 5. An attacker carrying out a disassociation DoS Attack.

7. Log Collection and Parsing

Small to large-scale systems generate logs on a regular basis to record system states and runtime information, each of which includes a date and a log message describing what happened. This useful information might be used for a variety of purposes (for example, anomaly detection), and thus logs are gathered first for later use. Logs are unstructured plain text and consist of constant and variable parts [39]. Logs consist of constant parts predefined in the source code and they remain the same in different occurrences. The remaining parts of the log are variable parts that change depending on the different occurrences and are generated dynamically. Log parsing extracts a group of event templates to create a structured and well-established log format from the raw logs. More specifically, each log message can be parsed into an event template (constant part) with some specific parameters (variable part).

8. Elastic Stack

The Elastic stack is an open-source project that primarily consists of Elasticsearch, Logstash, and Kibana (ELK) used for data preprocesses, data search, and data visualization [40]. ELK is preferred as it is open-source with the capabilities of log collection, processing, and visualization. Being open-source and other vital features of ELK help develop effective and efficient vendor-independent solutions including log collection, log parsing, visualization, and feature extraction.

This entry is adapted from the peer-reviewed paper 10.3390/electronics12173731

References

  1. IEEE 802.11-1997; IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  2. IEEE 802.11a-1999; IEEE Standard for Telecommunications and Information Exchange between Systems—LAN/MAN Specific Requirements—Part 11: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High Speed Physical Layer in the 5 GHz Band. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  3. 802.11g-2003; IEEE Standard for Information Technology—Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Further Higher Data Rate Extension in the 2.4 GHz Band. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  4. 802.11n-2009; IEEE Standard for Information Technology—Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC)and Physical Layer (PHY) Specifications Amendment 5: Enhancements for Higher Throughput. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  5. 802.11ac-2013; IEEE Standard for Information Technology—Telecommunications and Information Exchange between Systems–Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications—Amendment 4: Enhancements for Very High Throughput for Operation in Bands below 6 GHz. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  6. 802.11ai-2016; IEEE Standard for Information Technology—Telecommunications and Information Exchange between Systems—Local and Metropolitan Area Networks—Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 1: Fast Initial Link Setup. IEEE: Toulouse, France. Available online: https://standards.ieee.org (accessed on 12 May 2023).
  7. Gu, J.; Zhao, J.; Li, W. Research on WLAN Security Technology Based on IEEE 802.11. In Proceedings of the 2011 3rd International Conference on Advanced Computer Control, ICACC, Harbin, China, 18–20 January 2011; pp. 234–237.
  8. Juhász, K.; Póser, V.; Kozlovszky, M.; Bánáti, A. WiFi Vulnerability Caused by SSID Forgery in the IEEE 802.11 Protocol. In Proceedings of the SAMI 2019—IEEE 17th World Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia, 24–26 January 2019; pp. 333–338.
  9. Yao, X.; Farha, F.; Li, R.; Psychoula, I.; Chen, L.; Ning, H. Security and Privacy Issues of Physical Objects in the IoT: Challenges and Opportunities. Digit. Commun. Netw. 2021, 7, 373–384.
  10. CVE Records for Deatuthentication and Disassociation Attacks. Available online: https://cve.mitre.org/cgi-bin/cvekey.cgi?keyword=deauthentication+attack (accessed on 28 June 2023).
  11. A. Reyes, A.; D. Vaca, F.; Castro Aguayo, G.A.; Niyaz, Q.; Devabhaktuni, V. A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System. Electronics 2020, 9, 1689.
  12. WPA3 Specification. Available online: https://www.wi-fi.org/downloads-public/WPA3%2BSpecification%2Bv3.1.pdf/35332 (accessed on 12 May 2023).
  13. IEEE Std 802.11ax-2021 (Amendment to IEEE Std 802.11-2020); IEEE Standard for Information Technology–Telecommunications and Information Exchange between Systems Local and Metropolitan Area Networks–Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 1: Enhancements for High-Efficiency WLAN. IEEE: Toulouse, France, 2021; pp. 1–767.
  14. Dalal, N.; Akhtar, N.; Gupta, A.; Karamchandani, N.; Kasbekar, G.S.; Parekh, J. A Wireless Intrusion Detection System for 802.11 WPA3 Networks. In Proceedings of the 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India, 4–8 January 2022; Volume 3.
  15. Baras, K.; Moreira, A. Anomaly Detection in University Campus WiFi Zones. In Proceedings of the 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2010, Mannheim, Germany, 29 March–2 April 2010; pp. 202–207.
  16. Simbana, S.; Lopez, G.; Tipantuna, C.; Sanchez, F. Vulnerability Analysis Toolkit for IEEE 802.11 Wireless Networks: A Practical Approach. In Proceedings of the 3rd International Conference on Information Systems and Computer Science, INCISCOS, Quito, Ecuador, 13–15 November 2018; pp. 227–232.
  17. Seraphim, B.I.; Palit, S.; Srivastava, K.; Poovammal, E. A Survey on Machine Learning Techniques in Network Intrusion Detection System. In Proceedings of the 2018 4th International Conference on Computing Communication and Automation, ICCCA, Greater Noida, India, 14–15 December 2018; pp. 1–5.
  18. Satam, P.; Hariri, S. WIDS: An Anomaly Based Intrusion Detection System for Wi-Fi (IEEE 802.11) Protocol. IEEE Trans. Netw. Serv. Manag. 2021, 18, 1077–1091.
  19. Yousefnezhad, N.; Malhi, A.; Främling, K. Security in Product Lifecycle of IoT Devices: A Survey. J. Netw. Comput. Appl. 2020, 171, 102779.
  20. Zarpelão, B.B.; Miani, R.S.; Kawakani, C.T.; de Alvarenga, S.C. A Survey of Intrusion Detection in Internet of Things. J. Netw. Comput. Appl. 2017, 84, 25–37.
  21. Danziger, M.; De Lima Neto, F.B. A Hybrid Approach for IEEE 802.11 Intrusion Detection Based on AIS, MAS and Naïve Bayes. In Proceedings of the 2010 10th International Conference on Hybrid Intelligent Systems, Atlanta, GA, USA, 23–25 August 2010; pp. 201–204.
  22. Thing, V.L.L. IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach. In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 19–22 March 2017; pp. 1–6.
  23. Riyaz, B.; Ganapathy, S. A Deep Learning Approach for Effective Intrusion Detection in Wireless Networks Using CNN. Soft Comput. 2020, 24, 17265–17278.
  24. Lawal, M.A.; Shaikh, R.A.; Hassan, S.R. Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks. IEEE Access 2020, 8, 43355–43374.
  25. Sharma, N.; Jain, V.; Mishra, A. An Analysis of Convolutional Neural Networks for Image Classification. Procedia Comput. Sci. 2018, 132, 377–384.
  26. Narayana Rao, K.; Venkata Rao, K.; Pvgd, P.R. A Hybrid Intrusion Detection System Based on Sparse Autoencoder and Deep Neural Network. Comput. Commun. 2021, 180, 77–88.
  27. Lawrence, S.; Giles, C.L.; Tsoi, A.C.; Back, A.D. Face Recognition: A Convolutional Neural-Network Approach. IEEE Trans. Neural. Netw. 1997, 8, 98–113.
  28. Zhiqiang, W.; Jun, L. A Review of Object Detection Based on Convolutional Neural Network. In Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China, 26–28 July 2017; pp. 11104–11109.
  29. Chang, Y.; Yan, L.; Fang, H.; Zhong, S.; Liao, W. HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network. IEEE Trans. Geosci. Remote Sens. 2019, 57, 667–682.
  30. Devlin, J.; Cheng, H.; Fang, H.; Gupta, S.; Deng, L.; He, X.; Zweig, G.; Mitchell, M. Language Models for Image Captioning: The Quirks and What Works 2015. arXiv 2015, arXiv:1505.01809.
  31. Yang, J.; Li, J. Application of Deep Convolution Neural Network. In Proceedings of the 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 15–17 December 2017; IEEE: Chengdu, China, 2017; pp. 229–232.
  32. Torfi, A.; Iranmanesh, S.M.; Nasrabadi, N.; Dawson, J. 3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition. IEEE Access 2017, 5, 22081–22091.
  33. Idrissi, I.; Azizi, M.; Moussaoui, O. Accelerating the Update of a DL-Based IDS for IoT Using Deep Transfer Learning. IJEECS 2021, 23, 1059.
  34. Pan, S.J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359.
  35. Global Economic Value of Wi-Fi_2021-2025—Wi-Fi Alliance. Available online: https://www.wi-fi.org/downloads-public/Global_Economic_Value_of_Wi-Fi_2021-2025_202109.pdf/37347 (accessed on 12 May 2023).
  36. Waliullah, M.; Moniruzzaman, A.B.M.; Rahman, M.S. An Experimental Study Analysis of Security Attacks at IEEE 802.11 Wireless Local Area Network. Int. J. Future Gener. Commun. Netw. 2015, 8, 9–18.
  37. Mahini, H.; Mousavirad, S.M. WiFi Intrusion Detection and Prevention Systems Analyzing: A Game Theoretical Perspective. Int. J. Wirel. Inf. Netw. 2020, 27, 77–88.
  38. Milliken, J.; Selis, V.; Yap, K.M.; Marshall, A. Impact of Metric Selection on Wireless Deauthentication Dos Attack Performance. IEEE Wirel. Commun. Lett. 2013, 2, 571–574.
  39. Tyagi, M.; Narvare, S.; Agrawal, C. A Survey of Different Dos Attacks on Wireless Network. Comput. Eng. Intell. Syst. 2018, 9, 23–32.
  40. Agrawal, A.; Dixit, A.; Shettar, N.; Kapadia, D.; Karlupia, R.; Agrawal, V.; Gupta, R. Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms. arXiv 2019, arXiv:1902.04843v3.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback