Development of Incident Response Tool for Cyber Security Training Based on Virtualization and Cloud

Park, Y.S.; Choi, C.S.; Jang, Changbok; Shin, D.G.; Cho, G.C.; Kim, Hwa Soo

doi:10.1109/iwbis.2019.8935723

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…The model design of cyber security is constructed on integral, confidential, and available information [25]. The purpose of cyber security is to protect cyber space such as personal information, e-government data, e-marketing websites, e-business reports, and various applications from unauthorized penetration [26]. In addition, it covers the security of software and equipment that ensure and guarantee the privacy and integrity of the data under protection from a variety of threats and attacks [27].…”

Section: Cyber Securitymentioning

confidence: 99%

Cyber security: performance analysis and challenges for cyber attacks detection

Salih

Abdulrazzaq

2023

IJEECS

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Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.

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Section: Cyber Securitymentioning

confidence: 99%

Cyber security: performance analysis and challenges for cyber attacks detection

Salih

Abdulrazzaq

2023

IJEECS

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“…cyber-attacks detection methods. The authors of [25] presented a cyber-security tools for detecting and minimizing the effects of attacks in Korean government. These tools had the ability in doing training in terms of cyber-security issues that can face cloud based web applications.…”

Section: Introductionmentioning

confidence: 99%

Systematic Review on Cyber-Security Applications

2022

IJCCCE

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Cybersecurity systems have been taken into account in modern information systems and methods. This is due to the increase in electronic attacks on storage information in terms of transmission, reception and storage. Therefore, the need to produce such systems in a complete way to prevent their penetration into the network has increased. In addition, artificial intelligence (AI) methods are used in cybersecurity systems as classifiers, attack detectors, and components for predicting threats that surround the network. This requires more information about threats and vulnerabilities to be covered to avoid any errors. In this paper, a systematic review is conducted to cover cybersecurity used in various applications, including systems based on wireless, cloud, and mobile sensor networks (WSN). The systematic review approach is adopted on a two-way basis to produce a clear view of the research work to date and to provide a field that can be used for future work. Index Terms— WSN, Cyber-Security, Cyber Applications, AI.

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“…In this context, computer security has become a major challenge, and work in this area of research is increasing. Various tools and mechanisms are developed to ensure a level of safety that meets the demands of modern life [1]. These tools include the Intrusion Detection System (IDS).…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Networks and Deep Neural Networks Based on Intrusion Detection System

Zarai¹,

Kachout²,

Hazber³

et al. 2020

OALib

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The computer security has become a major challenge. Tools and mechanisms have been developed to ensure a level of compliance. These include the Intrusion Detection Systems (IDS). The principle of conventional IDS is to detect attempts to attack a network and to identify abnormal activities and behaviors. The reasons, including the uncertainty in searching for types of attacks and the increasing complexity of advanced cyber-attacks, IDS calls for the need for integration of methods such as Deep Neuron Networks (DNN) and Recurring Neuron Networks (RNN) more precisely long-term memory (LSTM). In this submission, DNN and LSTM were used to predict attacks against the Network Intrusion Detection System (NIDS). In this memory, we used four hidden layers for all deep learning algorithms, forty-one layers of inputs and two layers of outputs and with 100 iterations. In fact, learning is kept constant at 0.01 while the other parameters are optimized. After that for DNN, the number of neurons of the first hidden layer was further increased to 1280 but did not give any appreciable increase in accuracy. Therefore, the number of neurons has been set to 1024 and the LSTM we set the number of neurons of all hidden layers to 32. The results were compared and concluded that a three-layer LSTM performs better than all other conventional machine learning and deep learning algorithms.

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Development of Incident Response Tool for Cyber Security Training Based on Virtualization and Cloud

Cited by 5 publications

References 1 publication

Cyber security: performance analysis and challenges for cyber attacks detection

Cyber security: performance analysis and challenges for cyber attacks detection

Systematic Review on Cyber-Security Applications

Recurrent Neural Networks and Deep Neural Networks Based on Intrusion Detection System

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