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Computer Science, Interdisciplinary Applications
Betul Sultan Yildiz et al.
Summary: The paper introduces a novel metaheuristic algorithm named chaotic Henry gas solubility optimization (CHGSO), which integrates chaotic maps into the optimization process to enhance convergence rate. The performance of CHGSO was evaluated on various constrained optimization problems in mechanical design and manufacturing, showing robust optimization capability.
ENGINEERING WITH COMPUTERS
(2022)
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Computer Science, Hardware & Architecture
Alaeddine Mihoub et al.
Summary: This paper investigates the detection of DoS/DDoS attacks in IoT using machine learning techniques. A new architecture is proposed, consisting of two components: DoS/DDoS detection and mitigation. The detection component provides fine-granularity detection, identifying the specific type of attack and packet type used. Evaluation on the Bot-IoT dataset shows promising results, with a Looking-Back-enabled Random Forest classifier achieving an accuracy of 99.81%.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
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Computer Science, Hardware & Architecture
P. V. Shalini et al.
Summary: SDN separates the data plane from the control plane, enabling centralized control and faster data transmission. However, it faces challenges in network security, especially in the detection of DDoS attacks.
Article
Computer Science, Software Engineering
Bekhzod Olimov et al.
Summary: This paper explores different weight initialization strategies and proposes a weight initialization-based ReLU activation function, which performs better on Fashion-MNIST and CIFAR-10 databases.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xiang-Gui Guo et al.
Summary: This article investigates an adaptive event-triggered fault detection and isolation scheme for nonlinear networked control systems under DoS attacks, utilizing a novel triggering mechanism and a switching state-feedback controller. The proposed approach demonstrates effectiveness in simulation cases and offers advantages in saving communication resources.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Dan Tang et al.
Summary: The new low-rate DoS attack detection scheme utilizes the SADBSCAN algorithm to cluster network traffic based on characteristics and determine the presence of attacks using cosine similarity. Experimental results show improved detection accuracy, reduced false negative rate, and adaptability to large-scale complex network environments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Minzhao Lyu et al.
Summary: DNS is a critical service for enterprise operations but is often targeted by malicious actors. Current solutions are not robust against attackers who change their identity or rates to evade detection. This paper proposes a method combining hierarchical graph structure and machine learning to detect distributed DNS attacks at different levels, even with low rates and stealthy patterns.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2021)
Article
Computer Science, Hardware & Architecture
Ismael Amezcua Valdovinos et al.
Summary: SDN is a network paradigm that separates control and data planes, with the controller controlling the network logic and switches following rules to distribute traffic. The article systematically explores existing DDoS detection and mitigation strategies in SDN.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xinfeng Shao et al.
Summary: This paper investigates the problems of decentralized adaptive neural network secure control for a class of nonstrict-feedback nonlinear interconnected large-scale systems under denial-of-service attacks. A novel dynamic gain observer is constructed to solve the difficulty of sampling in the presence of DoS attacks. The stability is proven based on Lyapunov stability theory and the improved average dwell time method.
Proceedings Paper
Computer Science, Information Systems
Odnan Ref Sanchez et al.
Summary: DDoS attacks disrupt global network services by overwhelming victim hosts with requests, and are currently on the rise. Traditional Machine Learning methods used for developing DDoS detectors show satisfactory results, with Random Forests achieving a detection accuracy as high as 99%.
PROCEEDINGS OF THE 2021 IEEE 7TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION (NETSOFT 2021): ACCELERATING NETWORK SOFTWARIZATION IN THE COGNITIVE AGE
(2021)
Article
Computer Science, Information Systems
Abimbola O. Sangodoyin et al.
Summary: Software-defined networks (SDNs) provide robust network architectures for Internet of Things (IoT) applications but are also attractive targets for cyber attackers. Vulnerable to Distributed Denial of Service (DDoS) flooding attacks, machine learning algorithms such as Quadratic Discriminant Analysis (QDA), Gaussian Naive Bayes (GNB), k-nearest neighbor (k-NN), and Classification and Regression Tree (CART) are investigated for detecting and classifying these attacks on SDNs, with CART showing the best overall performance.
Article
Computer Science, Information Systems
Josy Elsa Varghese et al.
Summary: The rapid expansion of the Internet has led to increased network traffic and security challenges, particularly in dealing with Distributed Denial-of-Service (DDoS) attacks. A novel DDoS detection framework based on DPDK is proposed in this study, which incorporates intelligence at the data layer to efficiently detect and combat DDoS attacks.
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(2020)
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Tan Nguyen et al.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2019)
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Computer Science, Hardware & Architecture
Xiaoyu Liang et al.
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Computer Science, Information Systems
Trung Phan et al.
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Telecommunications
Seyyed Meysam Tabatabaie Nezhad et al.
IEEE COMMUNICATIONS LETTERS
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Computer Science, Information Systems
P. Garcia-Teodoro et al.
COMPUTERS & SECURITY
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Computer Science, Hardware & Architecture
AD Wood et al.
