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A stacked ensemble approach to detect cyber attacks based on feature selection techniques

International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

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The Usage of Machine Learning on Penetration Testing Automation 3

The Usage of Machine Learning on Penetration Testing Automat...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Clintswood Lie, Delbert Giovanni Kuswandana, Lexiandy Nadia Achmad, Said Suhartono, Derwin School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The International Information System security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations. © 2023 IEEE.

关键词： cybersecurity

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Enhancing Anomaly Detection in Smart Water Metering Networks with LSTM-Autoencoder and Data Augmentation Techniques 4

Enhancing Anomaly Detection in Smart Water Metering Networks...

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Nelago Kanyama, Maria Bhunu Shava, Fungai Gamundani, Attlee M Hartmann, Andreas Namibia University of Science and Technology Department of Computer Science Windhoek Namibia Namibia University of Science and Technology Department of Cyber Security Windhoek Namibia Hydrology Institute of Groundwater Management Dresden Germany

ISBN: (纸本)9798350387988

Machine Learning (ML) based anomaly detection in Smart Water Metering Networks (SWMNs) and traditional water metering networks faces significant challenges, including small dataset sizes, unlabeled data, and class imbalance. These issues often lead to high false detection rates, model overfitting, and increased model complexity. This paper aims to enhance anomaly detection in SWMNs using a ML-based Long Short-Term Memory Autoencoder (LSTM-AE) augmented with data augmentation techniques. The dataset, collected from Location A in Windhoek, Namibia, comprised of only 72 monthly water consumption data points per household, which is insufficient for effectively training ML models. To address this limitation, we employed two interpolation-based data augmentation techniques: Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) and Cubic Spline, to generate synthetic data instances to expand the dataset. These techniques were selected based on the patterns and trends of water consumption data and were compared for their accuracy. The original and augmented datasets were then used to train the LSTM-AE model. The results demonstrate that, despite the limited data available in the water sector, data augmentation techniques can significantly enrich datasets, leading to substantial performance improvement with augmented data. The proposed approach greatly enhanced the accuracy of the LSTM-AE model in reconstructing data from small datasets. This study underscores the potential of data augmentation to overcome data scarcity in the water sector and enhance anomaly detection in SWMNs. © 2024 IEEE.

关键词： Interpolation

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cyber security in Smart Cities and Its Challenges: A Systematic Literature Review 3

Cyber Security in Smart Cities and Its Challenges: A Systema...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Ruatta, Benedicto Theodore Piekarsa, Gabriel Jonatan Christian, Jeremia Edbert, Ivan Sebastian Gunawan, Alexander Agung Santoso School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

At this time, the development of information and communication technology has mostly been used maximally to improve the quality of life of citizens around the world. The benefits that are obtained make some cities evolve into smart cities in improving living standards and provide services in the health sector because it can control information technology and communication and cybersecurity challenges that are always evolving in every era. However, Behind the developments and benefits of it all, there are risks that cannot be avoided. Risk and these threats can be overcome through the national spectrum by implementing cybersecurity in the virtual world in developing the city into a smart city. Therefore, to respond to cybersecurity in smart cities, cybersecurity must evolve in the same direction, so that the strategy that has been formed will become the basis and reference in implementing cyber security in support of smart city development and the challenges that exist. This paper will outline the relationship between the two factors, the significance of cyber security for smart cities, how such cities can be attacked, and how to defend against such attacks. And after conducting the study, the authors reach the conclusion that in order for any smart cities to develop, flourish, and ensure that everyone living there is safe from any threat, cyber security is essential. © 2023 IEEE.

关键词： cybersecurity

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Phishing Attack Awareness Among College Students 3

Phishing Attack Awareness Among College Students

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Kenneth, Alvin Hayashi, Bernard Bryan Lionardi, Jason Richie, Stevanus Achmad, Said Junior, Franz Adeta Nadia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Phishing is a form of cyber-attack that uses social engineering techniques to trick individuals into providing sensitive information or clicking on malicious links, often through email or text messages. Social engineering is the psychological manipulation of people into performing actions or disclosing sensitive information. In phishing, attackers use social engineering tactics such as creating a sense of urgency, posing as a trustworthy entity, and creating a sense of familiarity to manipulate the victim into taking the desired action. These attacks can have serious consequences, including financial loss and identity theft. In this research, we used an email phishing technique that asked respondents to change their password on an email account to find out awareness of phishing attacks on college students. The research results show that a small percentage of a certain amount of people are still unaware of phishing attacks and fell into the attacker's trap. Several respondents who are still deceived by social engineering using phishing emails indicate that it is necessary to conduct indoctrination and campaigns to raise the awareness of each student. © 2023 IEEE.

关键词： Electronic mail

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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

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Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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Demand Response in Smart Grids: Challenges, Solutions, and security Implications 2

Demand Response in Smart Grids: Challenges, Solutions, and S...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Abdelraouf, Hussien Gogolkiewicz, Ryker Kim, Nathaniel McCaughan, Nicholas Alanes, Joel Nadhaif, Ali Fouda, Mostafa M. Ibrahem, Mohamed I. Augusta University School of Computer and Cyber Sciences AugustaGA30912 United States George Mason University Cyber Security Engineering Department FairfaxVA United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798331529529

This paper discusses demand response (DR) in the context of smart grids (SGs). DR is a method of controlling electricity consumption and production, usually for financial and security reasons. The topics covered include security in DR, solutions to related attacks, and the implementation of one of these solutions. Attacks on DR involve various methods of injecting perturbations into the system to cause damage to the grid. Understanding these attacks is crucial for protecting future systems. It is also important to learn more about cyber-physical systems and how they operate. Another significant topic discussed in this paper is energy management in SGs, specifically how to produce zero marginal cost renewable energies efficiently and cleanly. The methods for achieving this outcome, including various formulas and systems, are discussed in the two referenced papers and will be explained further in this paper. These energy-saving methods also contribute to the cybersecurity aspect of this paper to encompass DR. © 2024 IEEE.

关键词： Demand response

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Intelligent Deep Learning Model for Securing KMS Through CSF Deviation Detection 13

Intelligent Deep Learning Model for Securing KMS Through CSF...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chauhan, Raj Ahmad, Shahnawaz Onyarin, Ogala Justin Kumar, Rajendra Noida Institute of Engineering and Technology Department of CSE-AIML Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India University of Delta Cyber Security Department Delta State Nigeria Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9798350380583

Cloud computing has revolutionized data content stock and administration but concurrently it has increased the vulnerability factors especially Key Management System (KMS), which is crucial since it is used to protect cypher key. Typically, there are several limitations to anomaly detection methods used currently: poor precision and insufficient speed to detect threats. This research presents an intelligent model that integrated deep learning technique into existing Cloud security Framework (CSF) to conduct proactive identification on the deviation from the baseline behavior of interactions between the applications and the KMS. This is achieved through continually accruing KMS log data, defining behavioral profiles for the applications and using clustering to categorize them appropriately. The model also continuously monitors the system for any deviations, making anomalies easily discernible for quick action either through raising alerts to stakeholders or when using the KMS API to automatically respond to the flagged issues. Studies in the multitenant cloud scenario indicate that implementation of LSTMbased model is more effective than conventional approaches, enhancing the CSF's capability in threat detection thus making it possible to quickly mount defensive measures to safeguard crucial data and cryptographic keys. © 2024 IEEE.

关键词： Cloud security

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