S-ADS: Spectrogram Image-based Anomaly Detection System for IoT networks

Ahmad, Zeeshan; Khan, Adnan Shahid; Aqeel, Sehrish; Julaihi, Azlina Ahmadi; Tarmizi, Seleviawati; Annuar, Noralifah Binti; Habeeb, Mohammed Sayeeduddin

doi:10.1109/aiic54368.2022.9914599

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…Deep CNN then processes these images to perform the efficient anomaly prediction task. This research extends our previous works 16,17 based on the spectrogram approach for anomaly detection systems (ADS). The proposed works used a single-stage DL approach for the traditional network 16 and IoT network, 17 respectively.…”

Section: Introductionsupporting

confidence: 76%

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

Ahmad

Khan

Zen

et al. 2023

Trans Emerging Tel Tech

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The innovative computing idea of Internet‐of‐Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices collect a massive amount of sensitive information exchanged over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network‐based anomaly detection system can provide the much‐needed efficient security solution to the IoT network by detecting anomalies at the network entry points through constant traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false‐negative rate in classifying network traffic. To this end, this paper proposes an efficient Multistage Spectrogram image‐based network Anomaly Detection System (MS‐ADS) using a deep convolution neural network that utilizes a short‐time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re‐training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%.

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Section: Introductionsupporting

confidence: 76%

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

Ahmad

Khan

Zen

et al. 2023

Trans Emerging Tel Tech

Self Cite

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“…The proposed system's performance has been analysed and compared with existing systems, in particular references. 12,18,56,60 The comparison's findings are listed below.…”

Section: Results Discussionmentioning

confidence: 99%

“…This makes it challenging for a mitigation method to differentiate between an attacker and a legitimate user. 18,[34][35][36] An SDN-based system may readily be subjected to a DoS attack by flooding the OpenFlow switch with packets having random destinations. In search of a new flow rule, every packet is now transmitted from the switch to the controller.…”

Section: Problem Statementmentioning

confidence: 99%

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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Software‐defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial‐of‐service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two‐Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K‐nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two‐Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.

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“…To overcome this issue, researchers are focusing on rule-based NIDS for the IoT security approach to tackle this attacker. 18 The role of IDS in the context of IoT is to provide a strong defense system against a range of attacks like Zero-day attacks, Malware or Botnet, Data breaches, Denial of Service attacks (DoS), privacy violations, and many more. Intrusion detection system for IoT security is classified into two broad categories namely a signature-based IDS and anomaly-based IDS (AIDS).…”

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

“…As hackers are becoming advanced by continuously changing attack patterns, our defense system should advance, which cannot be achieved by limited resources and computational limitations. To overcome this issue, researchers are focusing on rule‐based NIDS for the IoT security approach to tackle this attacker 18 …”

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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Network Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

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S-ADS: Spectrogram Image-based Anomaly Detection System for IoT networks

Cited by 5 publications

References 29 publications

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

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