Using a Prediction Model to Manage Cyber Security Threats

Jaganathan, Venkatesh; Cherurveettil, Priyesh; Sivashanmugam, Premapriya Muthu

doi:10.1155/2015/703713

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…A regression model, on the other hand, is beneficial for statistically predicting cyberattacks or predicting the impact of an attack, such as worms, viruses, or other malicious software [49]. Regression techniques could be effective for a quantitative security model, such as phishing in a specific period or network packet parameters [9].…”

Section: Classification and Regression Analysis In Cybersecuritymentioning

confidence: 99%

“…Therefore, we can conclude that classification techniques can be used to build the prediction and classification model [4] utilizing relevant data in the domain of cyber security, whereas regression techniques are primarily used to determine the model's impact [49] by determining predictor strength, time-series causes, or the effect of the relations, taking into account the security attributes and the outcome. Thus designing an effective classification and regression algorithm or data-driven model utilizing relevant cyber data could be a potential research direction to get better outcome in a particular problem domain.…”

Section: Classification and Regression Analysis In Cybersecuritymentioning

confidence: 99%

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Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

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Section: Classification and Regression Analysis In Cybersecuritymentioning

confidence: 99%

Section: Classification and Regression Analysis In Cybersecuritymentioning

confidence: 99%

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Sarker

2022

Ann. Data. Sci.

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“…The CSE-CIC IDS2018 dataset is accessible in two formats: PCAP and CSV. The CSV format is primarily used in AI, while the PCAP format is utilized to extract new features [62,63].…”

Section: Explanation Of the Id Datamentioning

confidence: 99%

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Khan

2021

Processes

184

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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“…The dataset is published in CSV and PCAP formats with approximately 5 million records. CSV format is mainly used in the field of AI, and PCAP format is used to extract new features [17]. as shown in the figure The process of extracting CSV files from PCAP and then preprocessing the data before training it on the network as shown In Figure (1).…”

Section: The Datasetmentioning

confidence: 99%

Detecting network attacks Model based on a long short-term memory LSTM

Jasi¹,

Jawhar²

2022

Technium

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Nowadays, network-connected devices such as mobile phones and IoT devices are increasing, the types and numbers of these devices are increasing, the impact of successful attacks is increasing and the fear is growing due to the security effects when using them. In addition, a broader attack surface is available to identify and respond to these network attacks, different systems are used to prevent and stop Some of these systems consist of two layers, the first layer which provides Security and Intrusion Prevention is the firewall, while the second layer is the network intrusion detection system or attack detection system, if only the first layer represented by the firewall is used we cannot prevent attack, that's why attack detection or malware detection systems are used along with a firewall.

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Using a Prediction Model to Manage Cyber Security Threats

Cited by 19 publications

References 12 publications

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Detecting network attacks Model based on a long short-term memory LSTM

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