Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

Haque,; Tan, SC; Yusoff, Z.; Nisar, Kashif; Kwang, LC; Kaspin, Rizaludin; Chowdhry, BS; Buyya, Rajkumar; Majumder, Subhashis; Gupta, Manoj; Memon, Shuaib K.

doi:10.32604/cmc.2021.016591

Cited by 16 publications

(3 citation statements)

References 55 publications

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“…Automation: Machine learning can automate the detection and mitigation of DDoS and Delay attacks, allowing security teams to respond quickly and effectively [3]. This can reduce the impact of an attack and prevent data loss or service disruptions.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Al-Abadi,

Mohamed,

Fakhfakh

2023

IJRITCC

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Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Al-Abadi,

Mohamed,

Fakhfakh

2023

IJRITCC

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“…They discussed the latest research on medical data utilizing this technology, as well as several potential medical use cases in which this technology might play a key part in streamlining the process. They've also presented a networking protocol based IoMT delivery system [23].…”

Section: Literature Studymentioning

confidence: 99%

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Rana

Nisar

et al. 2022

Sustainability

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Healthcare, one of the most important industries, is data-oriented, but most of the research in this industry focuses on incorporating the internet of things (IoT) or connecting medical equipment. Very few researchers are looking at the data generated in the healthcare industry. Data are very important tools in this competitive world, as they can be integrated with artificial intelligence (AI) to promote sustainability. Healthcare data include the health records of patients, drug-related data, clinical trials data, data from various medical equipment, etc. Most of the data management processes are manual, time-consuming, and error-prone. Even then, different healthcare industries do not trust each other to share and collaborate on data. Distributed ledger technology is being used for innovations in different sectors including healthcare. This technology can be incorporated to maintain and exchange data between different healthcare organizations, such as hospitals, insurance companies, laboratories, pharmacies, etc. Various attributes of this technology, such as its immutability, transparency, provenance etc., can bring trust and security to the domain of the healthcare sector. In this paper, a decentralized access control model is proposed to enable the secure interoperability of different healthcare organizations. This model uses the Ethereum blockchain for its implementation. This model interfaces patients, doctors, chemists, and insurance companies, empowering the consistent and secure exchange of data. The major concerns are maintaining a history of the transactions and avoiding unauthorized updates in health records. Any transaction that changes the state of the data is reflected in the distributed ledger and can be easily traced with this model. Only authorized entities can access their respective data. Even the administrator will not be able to modify any medical records.

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“…The data captured and processed within the IoT network is of a sensitive nature that demands security from possible intrusions. Different security mechanisms such as firewalls, authentication schemes, different encryption methods, antiviruses, etc., are currently used to protect sensitive data from possible security attacks [12][13][14] on vulnerable devices [15][16][17] like the distributed denial of service (DDoS) attacks [18][19][20][21] as the first line of defense. IoT can implement using software-defined networking [SDN] [22][23][24], future network architecture [25][26][27][28], named data networking (NDN) [29][30][31] and cloud computing network [32] with voice over IP (VoIP) [33][34][35][36] fiber optics [37][38][39], worldwide interoperability for microwave access (WiMAX) [40][41][42], deep learning (DL) [43], artificial intelligence (AI), and machine learning (ML) [44].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Using Deep Neural Network for IoT Architecture

et al. 2021

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The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

Abstract: the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

Cited by 16 publications

References 55 publications

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Anomaly Detection Using Deep Neural Network for IoT Architecture

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