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Automatic Network Intrusion Detection System Using Machine Learning and Deep Learning

Automatic Network Intrusion Detection System Using Machine L...

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Artificial Intelligence and Mechatronics systems (AIMS), International Conference on

作者： Mohammed Mynuddin Sultan Uddin Khan Zayed Uddin Chowdhury Foredul Islam Md Jahidul Islam Mohammad Iqbal Hossain Dewan Mohammed Abdul Ahad Dept. of Electrical Engineering North Carolina A & T State University Data Scientist Data Bid Machine Inc. Associate Power Systems Engineer-II Open Systems International Inc. Dept. of Electrical Engineering Tuskegee University Dept. of Electrical and Computer Engineering University of North Carolina at Charlotte

In recent years, the popularity of network intrusion detection systems (NIDS) has surged, driven by the widespread adoption of cloud technologies. Given the escalating network traffic and the continuous evolution of cyber threats, the need for a highly efficient NIDS has become paramount for ensuring robust network security. Typically, intrusion detection systems utilize either a pattern-matching system or leverage machine learning for anomaly detection. While pattern-matching approaches tend to suffer from a high false positive rate (FPR), machine learning-based systems, such as SVM and KNN, predict potential attacks by recognizing distinct features. However, these models often operate on a limited set of features, resulting in lower accuracy and higher FPR. In our research, we introduced a deep learning model that harnesses the strengths of a Convolutional Neural Network (CNN) combined with a Bi-directional LSTM (Bi-LSTM) to learn spatial and temporal data features. The model, evaluated using the NSL-KDD dataset, exhibited a high detection rate with a minimal false positive rate. To enhance accuracy, K-fold cross-validation was employed in training the model. This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm in achieving superior performance across metrics like accuracy, F1-score, precision, and recall. The binary classification model trained on the NSL-KDD dataset demonstrates outstanding performance, achieving a high accuracy of 99.5% after 10-fold cross-validation, with an average accuracy of 99.3%. The model exhibits remarkable detection rates (0.994) and a low false positive rate (0.13). In the multiclass setting, the model maintains exceptional precision (99.25%), reaching a peak accuracy of 99.59% for $\text{k-value} =10$ . Notably, the Detection Rate for $\text{k-value}=10$ is 99.43%, and the mean False Positive Rate is calculated as 0.214925.

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A Study on Traceable Group Signature Scheme for Blockchain

A Study on Traceable Group Signature Scheme for Blockchain

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Information and Network Technologies (ICINT), International Conference on

作者： Hyo-Jin Song Im-Yeong Lee Dept. of Software Convergence Soonchunhyang Univ. Asan-si 31538 Republic of Korea Dept. of Computer Software Engineering Soonchunhyang Univ. Asan-si 31538 Republic of Korea

A blockchain is a decentralized database system in which a ledger containing transactions is stored and maintained identically by all through consensus among network participants without a central server. Instead of having a central server, network participants make sure that everyone stores and maintains the same ledger containing transactions through consensus. Integrity and reliability of the data stored in the blockchain are guaranteed, but on the other hand, privacy issues may arise. The pseudonymity of the public key reused in transactions causes a privacy invasion of the user’s behavioral pattern, asset size, and offline identity. Therefore, instead of assigning unique public keys to everyone, they are provided through a single signature in a blockchain environment. It can also be described as a method for ensuring anonymity by having members of the same group use the same public key in a transaction while employing the group signature, which has been studied in this paper. This paper defines the key used for group signature is defined as a composition of a “group private key” and a “partial key”. Mainly among them, we consider the leakage of the partial key. In the proposed scheme, it is possible to trace the leaker and the signer from the signature generated by the outsider with the leaked partial key. In this paper, we provide a secure traceable group signature scheme to cope with the leakage of the group signing key.

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Trusted Access to 6G Testbeds Through a Security Intent-Driven software-Defined Perimeter Framework 29

Trusted Access to 6G Testbeds Through a Security Intent-Driv...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Fernandez-Martinez, Carolina Bikos, Anastasios Verikoukis, Christos Siddiqui, Shuaib i2CAT Foundation Barcelona08034 Spain Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona08018 Spain Iquadrat Informatica Sl Barcelona08006 Spain Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona08034 Spain Industrial Systems Institute Athena Research Center Patras26504 Greece University of Patras Dept. of Computer Engineering and Informatics Patras26504 Greece

ISBN: (纸本)9798350377644

Parallel to the evolution of recent trends in the cybersecurity industry and the increase of cyberattacks in the last few years, there is renovated interest on the application of software-defined techniques to enforce best security practices, such as in the software-Defined Perimeter specification and the Zero Trust architectures. This work proposes a software-defined network segmentation and access control framework aligned with these principles. This solution captures minimal security requirements from testbed operators to establish ad-hoc, isolated connectivity and access control for end-users (experimenters) interacting with exposed endpoints from the exposed services in the cloud testbeds. The elicited architecture, design decision and initial implementation are introduced. © 2024 IEEE.

关键词： Testbeds

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Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol 23

Data Security Enhancement in Cloud Computing by Proposing A ...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications

作者： Sahar Ebadinezhad Halmat Ayub Abdulmajed Department of Computer Information Systems Near East University North Cyprus and Computer Information Systems Research and Technology Center (CISRT) Near East University North Cyprus Dept. Engineering and Computer Systems Lebanese French University (LFU) Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.

关键词： Cloud Services Cryptography Data Integrity Data Security Dual Key Encryption

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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Fo...

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Telecommunications Conference (ITC-Egypt), International

作者： Karim S. Farid A.A. Ali Sameh A. Salem Amr E. Mohamed dept. Information Systems & Communications Egyptian Electricity Holding Company Cairo Egypt dept. Electrical Power Helwan University - Faculty of Engineering Cairo Egypt dept. Computer & Systems Engineering Helwan University - Faculty of Engineering Cairo Egypt

With the development of plans to replace conventional meters with smart meters to enhance the functionality and performance of power system, forecasting power consumption is still a challenge due to nonlinearity patterns, different types of noise in the datasets, and sometimes lack of data. Artificial Intelligence plays an important role in utilizing the data sent from smart meters, particularly in the area of short-term load forecasting, which is required to meet the needs of consumers. Based on such insight, this study suggests a hybrid model based on a one-dimension convolutional neural network and gated recurrent units (CONV1D-GRU), to forecast hourly power consumption. The outcome demonstrates that the suggested model is capable of encompassing the patterns in a historical data pertaining the hourly power use. The suggested model has outperformed the competing strategies in the mean absolute percentage error (MAPE%), and the coefficient of determination $(R^{2})$ metrics.

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Automated Recipe Creation & Verification of Single Wafer Wet Etching: Ensemble Learning with Backcasting & Forecasting AIs using Scarce Data

Automated Recipe Creation & Verification of Single Wafer Wet...

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IEEE Silicon Nanoelectronics Workshop (SNW)

作者： Koki Shibata Hiroshi Horiguchi Chihiro Matsui Ken Takeuchi Dept. of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Application Software Engineering Operations SCREEN Semiconductor Solutions Co. Ltd. Japan

ISBN: (数字)9798350391633

ISBN: (纸本)9798350391640

Towards smart semiconductor fabrication, the automated process recipe determination with AI is required under scarce data (NOT big data) conditions. This paper proposes precise recipe determination by ensemble learning combining backcasting & forecasting AIs. Proposed backcasting AI predicts etching recipe to optimize the equipment arm motion, traditionally set manually, to achieve the desired etching amount. Automatically estimated etching recipe is optimized in real time and both throughput and yield improve. By means of incorporating additional features such as the differential and variance of etching results and employing ensemble learning with multiple neural networks, RMSE (Root Mean Squared Error) of the objective variable representing the periodic motion of the arm reduces by more than 40%. In addition, forecasting AI that predicts etching results from a recipe made by backcasting AI automatically validates the recipe.

关键词： Wet etching Neural networks Throughput Silicon Real-time systems Ensemble learning Artificial intelligence

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Security and Threat Detection through Cloud-Based Wazuh Deployment

Security and Threat Detection through Cloud-Based Wazuh Depl...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syed Moiz Abdul Majid Abdul Basit Mansoor Ebrahim Abdul Ahad Abro Mehwish Naeem Dept. of Computer Science Iqra University Karachi Pakistan Dept. of Cyber Security Institute of Business Administration (IBA-CICT) Karachi Pakistan Dept. of Computer Science Indus University Karachi Pakistan Dept. of Software Engineering Iqra University Karachi Pakistan University Libre de Bruxelles Brussels Belgium

In today's interconnected business landscape, security is the cornerstone of all organizations, irrespective of their size or industry. This research emphasizes the critical need to safeguard organizational assets from cyber threats and highlights the pivotal role that firewalls play in fortifying network and cyber systems. The challenge lies in maintaining updated firewall defenses to prevent server downtime and the circumvention of security measures. To address these concerns, this study introduces a novel approach by integrating Cloud Wazuh, a user-friendly solution tailored for non-technical personnel, providing streamlined deployment and customizable security rules. Leveraging Wazuh suite of tools, particularly the Host-Based Intrusion Detection System (HIDS), the research detects and mitigates potential threats, including patterns associated with attacks such as Port Scanning, Denial of Service (DoS), and Metasploit. Additionally, Wazuh offers a range of features, from Security Analytics to File Integrity Monitoring, to comprehensively protect against cyber threats. While implementing Cloud Wazuh may entail initial costs, it proves cost-effective compared to maintaining a dedicated IT security team. This research underscores how Cloud Wazuh deployment serves as a pivotal tool for organizations looking to strengthen their security measures, safeguard their data, and ensure uninterrupted operations in an increasingly digital and interconnected world.

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Karim S. Farid A. A. Ali Sameh A. Salem Amr E. Mohamed Dept. Information Systems & Communications Egyptian Electricity Holding Company Cairo Egypt Dept. Electrical Power Helwan University - Faculty of Engineering Cairo Egypt Dept. Computer & Systems Engineering Helwan University - Faculty of Engineering Cairo Egypt

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R 2 ) and the mean absolute percentage error (MAPE%) metrics.

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DDoS Detection Using Active and Idle Features of Revised CICFlowMeter and Statistical Approaches 4

DDoS Detection Using Active and Idle Features of Revised CIC...

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4th International Conference on Advanced Science and engineering, ICOASE 2022

作者： Ali, Basheer Husham Sulaiman, Nasri Al-Haddad, S.A.R. Atan, Rodziah Hassan, Siti Lailatul Mohd Department of Electrical and Electronic Engineering Serdang Malaysia Faculty of Engineering Department of Electrical and Electronic Engineering Serdang Malaysia Department of Computer and Communication Systems Engineering Serdang Malaysia Software Eng. and Information Systems Dept Serdang Malaysia School of Electrical Engineering Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9781665472227

Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows. © 2022 IEEE.

关键词： Denial-of-service attack

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On Prescription or Off Prescription? An Empirical Study of Community-Prescribed Security Configurations for Kubernetes

On Prescription or Off Prescription? An Empirical Study of C...

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International Conference on software engineering (ICSE)

作者： Shazibul Islam Shamim Hanyang Hu Akond Rahman Dept of SWEGD Kennesaw State University Marietta GA USA Company-A CA USA Dept. of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

Despite being beneficial for rapid delivery of software, Kubernetes deployments can be susceptible to security attacks, which can cause serious consequences. A systematic characterization of how community-prescribed security configurations, i.e., security configurations that are recommended by security experts, can aid practitioners to secure their Kubernetes deployments. To that end, we conduct an empirical study with 53 security configurations recommended by the Center for Internet Security (CIS), 20 survey respondents, and 544 configuration files obtained from the open source software (OSS) and proprietary domains. From our empirical study, we observe: (i) practitioners can be unaware of prescribed security configurations as $5 \% \sim 40 {\%}$ of the survey respondents are unfamiliar with 16 prescribed configurations; and (ii) for Company-A and OSS respectively, 18.0% and 17.9% of the configuration files include at least one violation of prescribed configurations. From our evaluation with 5 static application security testing (SAST) tools we find (i) only Kubescape to support all of the prescribed security configuration categories; (ii) the highest observed precision to be 0.41 and 0.43 respectively, for the Company-A and OSS datasets; and (iii) the highest observed recall to be respectively, 0.53 and 0.65 for the Company-A and OSS datasets. Our findings show a disconnect between what CIS experts recommend for Kubernetes-related configurations and what happens in practice. We conclude the paper by providing recommendations for practitioners and researchers. Dataset used for the paper is publicly available online.

关键词： Surveys Systematics DevOps Internet security Static analysis Security Open source software Guidelines Testing software engineering

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