Cyber attack awareness and prevention in network security

Fadli, Zolkipli Mohamad; Yong, Shu See; Kee, Low Kai; Ching, Gan Hui

doi:10.11591/ijict.v11i2.pp105-115

Cited by 2 publications

(2 citation statements)

References 15 publications

(45 reference statements)

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“… data loss incidents: destroying information systems due to negligence, mishandling, natural disasters, or intentional/unintentional actions by service providers results in critical data loss with potentially severe impacts on businesses. In [4], authors explored various cyber-attacks, including distributed denial of service (DDoS) attacks, malicious domains, websites, malware, ransomware, spam emails, malicious social media messaging, business email compromise, mobile threats, and risky browsing apps. The article delves into the repercussions of cyber-attacks, outlining potential outcomes such as information loss, financial harm, business disruptions, and threats to the peace and security of affected individuals.…”

Section: Literature Reviewmentioning

confidence: 99%

Cyber-aware threats and management strategies in cloud environments

Liubchenko,

Volkov

2024

HAIT

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Thepaper provides an overview of cyber threats within cloud computing and proposes effective management strategies. The transition to cloud services has introduced significant security concerns, particularly regarding data protection and privacy. The study aims to catalogue an exhaustive inventory of threats, analyse their prevalence, and thoroughly study applicable security mechanisms.The authors conducted an in-depth literature review, focusing on articles published after 2018, to identify vulnerabilities, attack vectors, and mitigation strategies. The paper categorises various cyber threats,such as malware, phishing, man-in-the-middle attacks, denial-of-service attacks, and SQL injections, and discusses their potential to infiltrate, deceive, disrupt, and steal data. It also highlights the importance of securing internet-connected devices and recommends strategies like robust password policies and regular software updates.The paper concludes by emphasisingthe need for adaptive security strategies to combat the evolving nature of cyber threats. It advocates for a dynamic approach to security, integrating robust defencemechanisms, continuous monitoring, and rapid response protocols. By prioritisingcybersecurity, organisationscan navigate the complexities of cloud computing, ensuring their data assets' integrity, confidentiality, and availability in a digital landscape. The findings area foundation for crafting a security framework tailored to applications operating within cloud environments.

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Section: Literature Reviewmentioning

confidence: 99%

Cyber-aware threats and management strategies in cloud environments

Liubchenko,

Volkov

2024

HAIT

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“…The data and the servers responsible for storing and delivering that data across distributed and expansive networks are paramount assets. They have the capacity to provide valuable information, analytical insights, and future predictions in a timely manner [1], [2]. These components play a crucial role in leveraging the potential of networked systems for various purposes [3].…”

Section: Introductionmentioning

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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Cyber attack awareness and prevention in network security

Cited by 2 publications

References 15 publications

Cyber-aware threats and management strategies in cloud environments

Cyber-aware threats and management strategies in cloud environments

A hybrid deep learning approach for enhanced network intrusion detection

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