Analysis of Intrusion Detection Approaches for Network Traffic Anomalies with Comparative Analysis on Botnets (2008–2020)

Ahmad, Sultan; Jha, Sudan; Alam, Afroj; Alharbi, Meshal; Nazeer, Jabeen

doi:10.1155/2022/9199703

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…Basturk and Ozbek, 2016 investigated and defined the complexity of the STBC-OFDM system with their parameters such as BER performance as 10 −3.6 and the Mean square Error (MSE) 10 −4.5 for 64-bit QAM modulation. In STBC -EABC -PTS, the optimal phase factor has been removed and the PAPR is minimized for the number of Subcarriers as 128 dB, 256 dB, and 512 dB in the MIMO -OFDM system [9][10][11][12]. In 2018, Yasir Amer proposed the Gray-PF-PTS algorithm to find the 5G waveform performance for maintaining the PAPR value based on the Filtered -OFDM (F -OFDM).…”

Section: Figure 2 Block Diagram Of Mimo -Ofdm Systemmentioning

confidence: 99%

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Kumutha¹,

Thanarajan²,

Alharbi³

2022

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Nowadays, the 5G parameters play an eminent role in the massive Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system for enriching high signal to noise ratio (SNR). 5G application has emerged in the role of artificial intelligence for involving the reduction of Peak to Average Power Ratio (PAPR) and Bit Error Rate (BER). In MIMO – OFDM system, the high PAPR is a tremendous drawback during the transmission of bit symbol with the number of sub-carriers in the signal. To avoid Intercarrier Interference (ICI) during transmission of the number of sub-carriers, the Omar Pigeon Space-Time (OPST) algorithm is implemented. Then, to overcome high PAPR in the uplink, the Hybrid Space-Time - Hadamard matrix (HST-HM) techniques are proposed and the Bit Error Rate (BER) is decreased abruptly. 5G parameters and specifications are incorporated in this OPST algorithm for avoiding interference during the data bit transmission in the MIMO – OFDM system. Realtors Property Resource (RPR) mobile app is developed for an experimental display of the information that occurs in the real-time uplink MIMO – OFDM system. Thus, the descriptive analysis and simulated results of PAPR, SNR, and BER are executed using the proposed system of HST with HM in the 5G Communication. The RPR mobile executes the outcomes through the OPST algorithm with a better system performance of the MIMO-OFDM system based on the 5G.

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Section: Figure 2 Block Diagram Of Mimo -Ofdm Systemmentioning

confidence: 99%

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Kumutha¹,

Thanarajan²,

Alharbi³

2022

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“…Malicious actors can capitalize on vulnerabilities in IoT devices to proliferate malware across heterogeneous IoT networks, comprised of diverse devices with varying communication technologies and capabilities. Malware can propagate between devices, forming a botnet that is capable of executing malicious actions, such as DDoS attacks and spam email delivery [7]. A notable illustration of such an attack on Internet of Things networks is the Mirai botnet [8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach

Casado-Vara,

Severt,

Díaz-Longueira

et al. 2024

Mathematics

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With the progress and evolution of the IoT, which has resulted in a rise in both the number of devices and their applications, there is a growing number of malware attacks with higher complexity. Countering the spread of malware in IoT networks is a vital aspect of cybersecurity, where mathematical modeling has proven to be a potent tool. In this study, we suggest an approach to enhance IoT security by installing security updates on IoT nodes. The proposed method employs a physically informed neural network to estimate parameters related to malware propagation. A numerical case study is conducted to evaluate the effectiveness of the mitigation strategy, and novel metrics are presented to test its efficacy. The findings suggest that the mitigation tactic involving the selection of nodes based on network characteristics is more effective than random node selection.

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“…Including all parameters in the detection process negatively affects the efficiency and cost of IDSs. The selection of the parameters that highly contribute to anomaly detection is inevitable for building robust IDSs [2] . In this context, feature selection techniques come into the picture to train and build machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

Eljialy,

Uddin,

Ahmad

2024

Tsinghua Sci. Technol.

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Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network's incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

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Analysis of Intrusion Detection Approaches for Network Traffic Anomalies with Comparative Analysis on Botnets (2008–2020)

Cited by 13 publications

References 39 publications

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach

Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

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