Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

Basheri, Mohammed; Ragab, Mahmoud

doi:10.32604/csse.2023.037130

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Afterwards, in the upgrade position, it is essential to verify the searching space boundaries; it could be led to employ in Eq. ( 7), 𝑝𝑜𝑠 𝑖 𝑛𝑒𝑤 (𝑡 + 1) ⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ = max (min (𝑝𝑜𝑠 𝑖 𝑛𝑒𝑤 (𝑡 + 1) ⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ + 𝑢𝑏) , 𝑙𝑏) (7) A novel temporary position is exchanged once the estimation function is enhanced. Then, 𝑃𝑜𝑠 𝑖 (𝑡 + 1) is equivalent to 𝑠 𝑖 𝑛𝑒𝑤 (𝑡 + 1).…”

Section: B Feature Selection Using Rkoamentioning

confidence: 99%

“…In the meantime, Intrusion detection can resolve these issues and ensure network data security [6]. Intrusion detection can be an effective technique, which attempts to alert and identify the attempted intrusions into the system or network [7]. However, the number of online businesses across traffic is increasing every day and therefore, the network features have also become highly difficult thus carrying further challenges for intrusion detection [8].…”

Section: Introductionmentioning

confidence: 99%

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Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

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The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying and responding to great security attacks and threats that can cooperate with the integrity, availability, and privacy of the network and its data. Machine learning (ML) algorithms are deployed for detecting difficult patterns and subtle anomalies in IoT data. Artificial intelligence (AI) driven methods are learned and adapted from novel data for improving detection accuracy over time. In this article, we introduce a Red Kite Optimization Algorithm with an Average Ensemble Model for Intrusion Detection (RKOA-AEID) technique for Secure IoT-based WSN. The purpose of the RKOA-AEID methodology is to accomplish security solutions for IoT-assisted WSNs. To accomplish this, the RKOA-AEID technique performs pre-processing to scale the input data using min-max normalization. In addition, the RKOA-AEID technique performs an RKOA-based feature selection approach to elect an optimum set of features. For intrusion detection, an average ensemble learning model is used. Finally, the Lévy-fight chaotic whale optimization Algorithm (LCWOA) can be executed for the optimum hyperparameter chosen for the ensemble models. The performance evaluation of the RKOA-AEID algorithm can be tested on the benchmark WSN-DS dataset. The extensive experimental outcomes stated the higher outcome of the RKOA-AEID algorithm with other approaches with an improved accuracy of 98.94%.

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Section: B Feature Selection Using Rkoamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The utmost care is to be taken with web-related gadgets, as more IoT gadgets depend on the web. It is ubiquitous in the workplace that the IoT gadgets deployed in an entity can be utilized for applying privacy and security features [ 6 ]. For instance, wearable gadgets that send and collect users’ health data to connected smartphones must avoid data leakage to ensure privacy.…”

Section: Introductionmentioning

confidence: 99%

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Khadidos

Alyoubi

et al. 2023

Sensors

Self Cite

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Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

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Enhancing Cybersecurity in the Internet of Things Environment Using Artificial Orca Algorithm and Ensemble Learning Model

Allafi,

Alzahrani

2024

IEEE Access

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Cybersecurity in the Internet of Things (IoT) is the practice of implementing measures to secure networks and connected devices from data breaches, cyber threats, and unauthorized access. It is essential owing to the increasing interconnectivity of devices, ranging from smart home appliances to industrial sensors. The potential attack surface expands, necessitating strong cybersecurity measures to protect sensitive data, ensure privacy, and prevent disruptions to critical services with these increasing number of IoT devices. Artificial intelligence (AI) technologies, particularly deep learning (DL) and machine learning (ML) approaches, hold the potential to mitigate and identify cyberattacks on IoT networks. DL demonstrates promise for effectively preventing and detecting security threats within IoT devices. Despite the importance of Intrusion Detection Systems (IDS) in maintaining confidentiality by detecting suspicious activities, classical IDS solutions might face difficulties in the IoT platform. Therefore, this study presents an Artificial Orca Algorithm with Ensemble Learning cyberattack detection and classification (AOAEL-CDC) methodology in an environment of IoT. The presented AOAEL-CDC technique exploited the feature selection (FS) approach with an ensemble learning approach for cyberattack recognition and identification in the IoT atmosphere. In the developed AOAEL-CDC model, the feature selection takes place using the AOA technique. For the cyberattack detection process, the ensemble learning process is carried out by the use of three models such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and extreme learning machine (ELM). Finally, the hyperparameter range of the DL techniques takes place using the marine predator's algorithm (MPA). To examine the performance analysis of the AOAEL-CDC methodology, a series of simulations take place using a benchmark dataset. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.31%.

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Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

Cited by 3 publications

References 24 publications

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Enhancing Cybersecurity in the Internet of Things Environment Using Artificial Orca Algorithm and Ensemble Learning Model

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