Segregation of IoT Traffic with Machine Learning Techniques

Khedkar, Shilpa

doi:10.17762/turcomat.v12i2.1806

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Anomaly could be categorized as three distinct kinds: point, collective, and contextual. Each type of attack corresponds to specific security breaches, including Denial of Service (DoS), Probe, User to Root (U2R), and Remote to Local (R2L) attacks [11]. Efficiently spotting and categorizing these anomalies necessitates the adaptation of network intrusion detection systems (NIDS) to dynamic network settings, encompassing novel protocols and behaviors.…”

Section: Introductionmentioning

confidence: 99%

Multi Level Deep Learning Model for Network Anomaly Detection

S. Derweesh,

A. Hameed Alazawi,

H. Al-Saleh

2024

J. Al-Qadisiyah Comp. Sci. Math.

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The increasing use of internet-based solutions and services in private and corporate sectors has resulted in a significant increase in personal internet involvement. This shift, concurrently exposed a heightened susceptibility to potential vulnerabilities, given the ability of malicious actors to exploit external networks, network services, or corporate infrastructures utilized for personal purposes. In recent times, there has been considerable interest in harnessing deep learning methodologies for enhancing cybersecurity, owing to their utilization of sophisticated learning algorithms for addressing pertinent online security challenges. Machine Learning (ML) and Deep Learning (DL) paradigms have been extensively applied across diverse dimensions of cybersecurity, encompassing tasks such as vulnerability assessment, malware classification, spam detection, and spoofing identification. In this paper, for hierarchical intrusion detection is proposed a novel multi-stage approach, The proposed system comprises two distinct classification modalities: multi-class classification and binary classification, contingent upon the nature of the attack within the dataset. the KDD99 dataset was leveraged to assess the classification performance of the proposed model. Both classification approaches involve main preprocessing steps, such as feature selection, feature normalization, building a Convolutional Neural Network (CNN) classifier apply on KDD99 dataset, deploying the CNN classifier for anomaly detection.

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Section: Introductionmentioning

confidence: 99%

Multi Level Deep Learning Model for Network Anomaly Detection

S. Derweesh,

A. Hameed Alazawi,

H. Al-Saleh

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…In recent years, object detection has been increasingly used in many practical applications, such as autonomous driving, video surveillance, and robotics. Several approaches, particularly based on Convolution Neural Networks (CNN), have shown great performance improvements on some public datasets, e.g., Pascal VOC [1][2][3][4]. Despite their success, these methods have restricted their understanding to the limited number of categories present in the training data, based on the closed world assumption, (i.e., the number of categories is assumed to be fixed and known as a priori).…”

Section: Introductionmentioning

confidence: 99%

A Framework for Open World Object Detection

Shaheen,

Hanif,

Hasan

et al. 2023

Artificial Intelligence Evolution

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Open World Object Detection (OWOD) is a computer vision task that focuses on real-world scenarios where object detection algorithms need to not only detect known and labeled objects but also handle novel and unknown objects that were not seen during training. This distinguishes OWOD from traditional object detection benchmarks, where the scope is limited to detecting only known object classes. The main challenge in OWOD lies in detecting and classifying unknown objects, which were not part of the training data. In standard object detection, objects not overlapping with labeled objects are automatically classified as background. However, these approaches are not suitable for OWOD, as unknown objects may be wrongly predicted as background due to the lack of specific supervision for distinguishing unknown objects from the background. The paper proposes a novel framework for Open World Object Detection called Open World Object Detection based on Non-Parametric classification (OWOD-NP). This method aims to address the challenges of identifying unknown objects and extending the knowledge base by incrementally introducing new object categories. OWOD-NP incorporates a non-parametric learning approach based on mean prototypes and rejection criteria into a standard detector model. The non-parametric learning model allows the system to detect whether the perceived region contains an unknown object and perform incremental learning in an end-to-end manner. The extensive experiments conducted on the benchmark dataset of Pascal Visual Object Classes (VOC) validate the effectiveness of OWOD-NP. Compared to the standard faster RCNN model, OWOD-NP achieves approximately 14% higher mean Average Precision (mAP) in class incremental scenarios. This improvement showcases the capability of OWOD-NP to handle open-world object detection tasks more efficiently. By combining non-parametric learning with object detection, OWOD-NP provides a promising solution for open-world scenarios, where the environment is dynamic and new objects may appear over time. The ability to detect and classify both known and unknown objects makes OWOD-NP a valuable approach for real-world applications in robotics, autonomous systems, and other computer vision tasks. It allows for continuous adaptation and learning, enabling the system to extend its knowledge and cope with ever-changing environments effectively.

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Segregation of IoT Traffic with Machine Learning Techniques

Cited by 2 publications

References 24 publications

Multi Level Deep Learning Model for Network Anomaly Detection

Multi Level Deep Learning Model for Network Anomaly Detection

A Framework for Open World Object Detection

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