Yazar "Seyfollahi, Ali" seçeneğine göre listele

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    Detecting and mitigating security anomalies in software-defined networking (SDN) using gradient-boosted trees and floodlight controller characteristics
    (Elsevier b.v., 2025) Jafarian, Tohid; Ghaffari, Ali; Seyfollahi, Ali; Arasteh, Bahman
    Cutting-edge and innovative software solutions are provided to address network security, network virtualization, and other network-related challenges in highly congested SDN-powered networks. However, these networks are susceptible to the same security issues as traditional networks. For instance, SDNs are significantly vulnerable to distributed denial of service (DDoS) attacks. Previous studies have suggested various anomaly detection techniques based on machine learning, statistical analysis, or entropy measurement to combat DDoS attacks and other security threats in SDN networks. However, these techniques face challenges such as collecting sufficient and relevant flow data, extracting and selecting the most informative features, and choosing the best model for identifying and preventing anomalies. This paper introduces a new and advanced multi-stage modular approach for anomaly detection and mitigation in SDN networks. The approach consists of four modules: data collection, feature selection, anomaly classification, and anomaly response. The approach utilizes the NetFlow standard to gather data and generate a dataset, employs the Information Gain Ratio (IGR) to select the most valuable features, uses gradient-boosted trees (GBT), and leverages Representational State Transfer Application Programming Interfaces (REST API) and Static Entry Pusher within the floodlight controller to construct an exceptionally efficient structure for detecting and mitigating anomalies in SDN design. We conducted experiments on a synthetic dataset containing 15 types of anomalies, such as DDoS attacks, port scans, worms, etc. We compared our model with four existing techniques: SVM, KNN, DT, and RF. Experimental results demonstrate that our model outperforms the existing techniques in terms of enhancing Accuracy (AC) and Detection Rate (DR) while simultaneously reducing Classification Error (CE) and False Alarm Rate (FAR) to 98.80 %, 97.44 %, 1.2 %, and 0.38 %, respectively.
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    Detecting and mitigating security anomalies in software-defined networking (SDN) using gradient-boosted trees and floodlight controller characteristics
    (Elsevier, 2024) Jafarian, Tohid; Ghaffari, Ali; Seyfollahi, Ali; Arasteh, Bahman
    Cutting-edge and innovative software solutions are provided to address network security, network virtualization, and other network-related challenges in highly congested SDN-powered networks. However, these networks are susceptible to the same security issues as traditional networks. For instance, SDNs are significantly vulnerable to distributed denial of service (DDoS) attacks. Previous studies have suggested various anomaly detection techniques based on machine learning, statistical analysis, or entropy measurement to combat DDoS attacks and other security threats in SDN networks. However, these techniques face challenges such as collecting sufficient and relevant flow data, extracting and selecting the most informative features, and choosing the best model for identifying and preventing anomalies. This paper introduces a new and advanced multi-stage modular approach for anomaly detection and mitigation in SDN networks. The approach consists of four modules: data collection, feature selection, anomaly classification, and anomaly response. The approach utilizes the NetFlow standard to gather data and generate a dataset, employs the Information Gain Ratio (IGR) to select the most valuable features, uses gradient-boosted trees (GBT), and leverages Representational State Transfer Application Programming Interfaces (REST API) and Static Entry Pusher within the floodlight controller to construct an exceptionally efficient structure for detecting and mitigating anomalies in SDN design. We conducted experiments on a synthetic dataset containing 15 types of anomalies, such as DDoS attacks, port scans, worms, etc. We compared our model with four existing techniques: SVM, KNN, DT, and RF. Experimental results demonstrate that our model outperforms the existing techniques in terms of enhancing Accuracy (AC) and Detection Rate (DR) while simultaneously reducing Classification Error (CE) and False Alarm Rate (FAR) to 98.80 %, 97.44 %, 1.2 %, and 0.38 %, respectively.
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    An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm
    (Springer, 2023) Salehnia, Taybeh; Seyfollahi, Ali; Raziani, Saeid; Noori, Azad; Ghaffari, Ali; Alsoud, Anas Ratib; Abualigah, Laith
    Nowadays, cloud and fog computing have been leveraged to enhance Internet of Things (IoT) performance. The outstanding potential of cloud platforms accelerates the processing and storage of aggregated big data from IoT equipment. Emerging fog-based schemes can improve service quality to IoT applications and mitigate excessive delays and security challenges. Also, since energy consumption can directly cause CO2 emissions from fog and cloud nodes, an efficient task scheduling method reduces energy consumption. In this regard, the growing need for an efficient task scheduling mechanism considering the optimal management of IoT resources is increasingly felt. IoT's task scheduling based on fog-cloud computing plays a crucial role in responding to users' requests. Optimal task scheduling can improve system performance. Therefore, this study uses an IoT task request scheduling method on resources by the Multi-Objective Moth-Flame Optimization (MOMFO) algorithm. It enhances the quality of IoT services based on fog-cloud computing to reduce task requests' completion and system throughput times and energy consumption. If energy consumption is diminished, the percentage of CO2 emissions is also reduced. Then, the proposed scheduling method to solve the task scheduling problem is evaluated using the datasets. A comparison between the proposed scheme and Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Salp Swarm Algorithms (SSA), Harris Hawks Optimizer (HHO), and Artificial Bee Colony (ABC) is performed to assess the performance. According to experiments, the proposed solution has reduced the completion time of IoT tasks and throughput time, thus cutting down the delay due to the processing of tasks, energy consumption, and CO2 emissions and increasing the system's performance rate.
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    RM-RPL: reliable mobility management framework for RPL-based IoT systems
    (Springer, 2023) Seyfollahi, Ali; Mainuddin, Md; Taami, Tania; Ghaffari, Ali
    This paper represents the Reliable Mobility Management of RPL (RM-RPL) protocol, specifically developed to overcome the limitations of the Routing Protocol for Low-Power and Lossy Networks (RPL) in mobile IoT environments. RM-RPL incorporates a sophisticated mechanism to prevent the formation of loops, enabling mobile nodes to operate as both routers and parents within the network. It introduces a novel objective function that optimizes the selection of parent nodes and includes a mechanism to adjust the protocol's behavior when nodes are stationary. Furthermore, an algorithm is devised to acknowledge critical packets properly. The proposed model provides superior support for mobility, efficient routing, and dependable data transmission, rendering it highly suitable for diverse IoT applications. Through comprehensive evaluations, RM-RPL demonstrates exceptional performance in challenging scenarios characterized by large-scale networks, high density, and dynamic conditions. Comparative analysis reveals that RM-RPL significantly enhances the packet delivery ratio and exhibits commendable power consumption, end-to-end delay, and handover delay.