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1. Denial of Service (DoS) Attack Detection: Performance Comparison of Supervised Machine Learning Algorithms

   https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00088  

   Li, Zhuolin; Zhang, Hao; Shahriar, Hossain; Lo, Dan; Qian, Kai; Whitman, Michael; Wu, Fan (August 2020, Proc. of 18th IEEE International Conference on Dependable, Autonomic and Secure Computing)

   null (Ed.)

   Denial of Service (DoS) is one of the common attempts in security hacking for making computation resources unavailable or to impair geographical networks. In this paper, we detect Denial of Service (DoS) attack from publicly available datasets using Logistic regression, Naive Bayes algorithm and artificial neural networks. The results from our experiments indicate that the accuracy, ROC curve and balanced accuracy of artificial neural network were higher than Naive Bayes algorithm and logistic regression for slightly imbalanced distribution dataset. 

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2. Detection of Denial-of-Service Attacks in a Software-Defined LEO Constellation Network

   Agnew, Dennis; McNair, Janise (March 2023, Government Microcircuit Applications and Critical Technology Conference (GOMAC Tech))

   Satellite communication (SATCOM) is a critical infrastructure for tactical networks--especially for the intermittent communication of submarines. To ensure data reliability, recent SATCOM research has begun to embrace several advances, such as low earth orbit (LEO) satellite networks to reduce latency and increase throughput compared to long-distance geostationary (GEO) satellites, and software-defined networking (SDN) to increase network control and security. This paper proposes an SD-LEO constellation for submarines in communication networks. An SD-LEO architecture is proposed, to Denial-of-Service (DoS) attack detection and classification using the extreme gradient boosting (XGBoost) algorithm. Numerical results demonstrate greater than ninety-eight percent in accuracy, precision, recall, and F1-scores. 

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3. System Call Processing Using Lightweight NLP for IoT Behavioral Malware Detection

   Carter, John; Mancoridis, Spiros; Nkomo, Malvin; Weber, Steven; Dandekar, Kapil R. (February 2023, Ubiquitous Security (UbiSec 2022))

   Although much of the work in behaviorally detecting malware lies in collecting the best explanatory data and using the most efficacious machine learning models, the processing of the data can sometimes prove to be the most important step in the data pipeline. In this work, we collect kernel-level system calls on a resource-constrained Internet of Things (IoT) device, apply lightweight Natural Language Processing (NLP) techniques to the data, and feed this processed data to two simple machine learning classification models: Logistic Regression (LR) and a Neural Network (NN). For the data processing, we group the system calls into n-grams that are sorted by the timestamp in which they are recorded. To demonstrate the effectiveness, or lack thereof, of using n-grams, we deploy two types of malware onto the IoT device: a Denial-of-Service (DoS) attack, and an Advanced Persistent Threat (APT) malware. We examine the effects of using lightweight NLP on malware like the DoS and the stealthy APT malware. For stealthier malware, such as the APT, using more advanced, but far more resource-intensive, NLP techniques will likely increase detection capability, which is saved for future work. 

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4. Hyperdimensional Cognitive Computing for Lightweight Cyberattack Detection in Industrial Internet of Things

   https://doi.org/10.1115/DETC2023-110699  

   Jalil_Piran, Fardin; Barkam, Hamza Errahmouni; Imani, Mohsen; Imani, Farhad (August 2023, American Society of Mechanical Engineers)

   Abstract Although the connectivity offered by industrial internet of things (IIoT) enables enhanced operational capabilities, the exposure of systems to significant cybersecurity risks poses critical challenges. Recently, machine learning (ML) algorithms such as feature-based support vector machines and logistic regression, together with end-to-end deep neural networks, have been implemented to detect intrusions, including command injection, denial of service, reconnaissance, and backdoor attacks, by capturing anomalous patterns. However, ML algorithms not only fall short in agile identification of intrusion with few samples, but also fail in adapting to new data or environments. This paper introduces hyperdimensional computing (HDC) as a new cognitive computing paradigm that mimics brain functionality to detect intrusions in IIoT systems. HDC encodes real-time data into a high-dimensional representation, allowing for ultra-efficient learning and analysis with limited samples and a few passes. Additionally, we incorporate the concept of regenerating brain cells into hyperdimensional computing to further improve learning capability and reduce the required memory. Experimental results on the WUSTL-IIOT-2021 dataset show that HDC detects intrusion with the accuracy of 92.6%, which is superior to multi-layer perceptron (40.2%), support vector machine (72.9%), logistic regression (84.2%), and Gaussian process classification (89.1%) while requires only 300 data and 5 iterations for training. 

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5. Denial of service attacks in edge computing layers: Taxonomy, vulnerabilities, threats and solutions

   https://doi.org/10.1016/j.adhoc.2023.103322  

   Uddin, Ryhan; Kumar; Sathish; Chamola, Vinay (October 2023, Ad hoc networks)

   Edge computing has emerged as the dominant communication technology connecting IoT and cloud, offering reduced latency and harnessing the potential of edge devices. However, its widespread adoption has also introduced various security vulnerabilities, similar to any nascent technology. One notable threat is the denial of service (DoS) attack, including its distributed form, the distributed denial of service (DDoS) attack, which is the primary focus of this research. This paper aims to explore the impact of different types of DoS and DDoS attacks on edge computing layers by examining the vulnerabilities associated with various edge peripherals. Addition ally, existing detection and prevention mechanisms are investigated to address these weaknesses. Furthermore, a theoretical architecture is proposed to mitigate distributed denial of service attacks targeting edge systems. By comprehensively analyzing and addressing the security concerns related to DoS and DDoS attacks in edge computing, this research aims to contribute to the development of robust and secure edge computing systems. 

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