A new algorithm to enhance security against cyber threats for internet of things application

Abujassar, Radwan S.; Sayed, Mohamed; Yaseen, Husam

doi:10.11591/ijece.v13i4.pp4452-4466

Cited by 2 publications

(2 citation statements)

References 25 publications

(31 reference statements)

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“…IDS in the cloud relies on advanced algorithms and machine learning techniques to analyze massive datasets and detect patterns indicative of malicious activity. Timely and accurate intrusion detection is essential for ensuring the confidentiality, integrity, and availability (CIA) of data stored and processed within cloud environments, contributing to the overall security posture of cloudbased systems [17], [18]. The elements comprising the IDS, as depicted in Figure 1, work in tandem to aid organizations in detecting and addressing security incidents, thereby fortifying their overall cybersecurity stance.…”

Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

A hybrid deep learning approach for enhanced network intrusion detection

Prabu,

Sudhakar

2024

IJEECS

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The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.

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Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

A hybrid deep learning approach for enhanced network intrusion detection

Prabu,

Sudhakar

2024

IJEECS

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“…Presently, the escalating number and interconnectivity of computers, coupled with the escalating complexity of systems and their facile extensibility, contribute to the escalating incidence of malware infections daily. A prominent and perilous menace to organizational integrity is ransomware [1], [2]. This insidious form of malicious software effectively takes control of a computer system, encrypts files on the hard drive, or forces the computer to shut down, demanding a ransom in return for restoring normal functionality and obstructing user access to the system.…”

Section: Introductionmentioning

confidence: 99%

Mitigating ransomware attacks through cyber threat intelligence and machine learning

Kante,

Sharma,

Gupta

2024

IJEECS

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<div>In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.</div>

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A new algorithm to enhance security against cyber threats for internet of things application

Cited by 2 publications

References 25 publications

A hybrid deep learning approach for enhanced network intrusion detection

A hybrid deep learning approach for enhanced network intrusion detection

Mitigating ransomware attacks through cyber threat intelligence and machine learning

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