A Neural Attention Model for Real-Time Network Intrusion Detection

Tan, Mengxuan; Iacovazzi, Alfonso; Cheung, Ngai-Man; Elovici, Yuval

doi:10.1109/lcn44214.2019.8990890

Cited by 29 publications

(14 citation statements)

References 22 publications

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“…claimed by Tan et al in [18] that dropping part of the attacks from the dataset would improve the detection accuracy, and we are testing this claim throughout our work. The rest of this paper will include a literature review of the previous related work, a description of the framework applied, including the dataset, the feature engineering approaches followed, including Pearson Correlation, Information Gain, Chi2, Random Forest Feature Importance RFFI, and a comparison between different machine learning classifiers including Random Forest, Decision Tree, and Gaussian Naïve Bayes.…”

Section: Introductionmentioning

confidence: 79%

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Naiem

Khedr

Idrees

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Distributed Denial of Service (DDOS) attacks aim to exploit the capacity and performance of a network's infrastructure, making the cloud environment one of the biggest targets for attackers. Many efforts are being made in the field of technology to prevent them from disrupting the services provided. Machine Learning techniques are a means to protect against DDOS attacks. Data preprocessing, feature selection, and classifiers are the main components of any prevention framework. The focus of this study is to find and enhance the feature selection approach for increasing the accuracy of the classifiers in detecting DDOS attacks from regular traffic. We used four different techniques, including Pearson Correlation Coefficient (PCC), Random Forest Feature Importance (RFFI), Mutual information (MI), and Chi-squared(X2) measure which we tested on different classifiers. The first selection approach was based on the feature’s independency level then the second iteration was based on the feature’s importance. We also examined the claim of dropping attacks from the dataset for better accuracy. The best performing set of features was from using PCC and RFFI together for feature selection with average accuracy and precision of 99.27% and 97.60%, which is higher than the use of PCC for both measures by almost 2%. The accuracy is also higher by nearly 12% from the same approach dropping 50% of the attacks.

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Section: Introductionmentioning

confidence: 79%

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Naiem

Khedr

Idrees

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…Finally, we would like to suggest some of the possible scopes, shortly for researcher and practitioner as a brainstorming concept: reducing the reaction time and maximizing VM's resource allocation considering the QoS factor; improving the load stability in WSN using RCNN learning; SVM-PSO based community Forensics and RNN techniques for Intrusion Detection. Feature selection from natural algorithm Koroniotis et al [133] Quality of service NF PSO and DL Enhance NF Al hawaitat et al [134] WS PNS PSO Jamming attack Shi et al [135] Anomaly detection P ADAID 1 Presented unsupervised clustering Usman et al [96] VM allocation VR EFPA 2 Energy-oriented allocation Singh et al [103] VM migration VR HBGA 3 Energy reduction Naik et al [130] VM allocation VR Fruit fly Reduce host migration Meng & Pan [136] Optimization VR FFOA solve MKP 4 Mosa & Paton [126] VM placement VR GA Reduce response time & maximize resources utilization Duan et al [137] Information leakage P DL Protect server Festag & Spreckelsen [138] Data leakage P DL Detection of protected health information Chari et al [125] Quality of service IA DL Generate password via cognitive information Li et al [139] Signal processing IA GA Feature extraction via EEG signal Saini & Kansal [127] WSN ACS SI Reduce energy consumption and increase network life time Chen et al [140] Biometric identification IA CNN Proposed GSLT-CNN using human brain EEG Cao & Fang [141] Multilayer defense scenario ACS SI Found proficient IPSO elucidating extensive WTA problem Aliyu et al [124] Resource allocation ACS Ant colony Illustrated faster convergence optimize makespan time Poonia [142] VAN ACS SI Found significant difference in VANET routing protocol and Swarm based protocol Verma et al [ [129] Feature extraction ID GA Reduce features to classify network packet Tan et al [148] Real time network attack intrusion ID NN Able to detect in network precisely…”

Section: Discussionmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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“…Intuitively, solutions based on white-box models should be preferable, as they are also simpler. We note that explainable models [28] based on the paradigm of attention [30] are being explored in the context of intrusion detection (e.g., [31]) and could boost IoT as well. We finally note that explainability is related also to requirements such as law compliance, accountability, fairness [28], [29].…”

Section: Holistic Evaluationmentioning

confidence: 99%

Security Assurance in Modern IoT Systems

Bena

Bondaruc

Polimeno

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Modern distributed systems consist of a multilayer architecture of IoT, edge, and cloud nodes. Together, they are revolutionizing our lives, bringing intelligence to existing processes (e.g., smart grids) and enabling novel, efficient and effective processes (e.g., remote surgery). This transition however does not come without drawbacks, due to the ever-increasing reliance on devices whose security and safety are, at least, questionable. In this context, research is in its infancy, struggling to adapt successful practices applied, for instance, in cloud systems. Security of modern IoT systems still relies on oldfashioned approaches, mostly static assessments considering only very specific parts of the target system, rather than assessing the system as a whole. In this paper, we put forward the idea of security assurance for IoT, as a higher-level assurance process evaluating the target system at different layers and different moments of its lifecycle, then implemented by a flexible assurance framework. The quality of our approach is evaluated in a realworld smart lighting system.

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A Neural Attention Model for Real-Time Network Intrusion Detection

Cited by 29 publications

References 22 publications

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Security Assurance in Modern IoT Systems

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