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Classification of Internet of Things Cybersecurity Attacks Using a Hybrid Deep Learning Approach 2nd

Classification of Internet of Things Cybersecurity Attacks U...

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2nd international conference on Innovations of Intelligent Informatics, Networking and Cybersecurity, 3INC 2024

作者： Kareem, Eman Karkawi Manaa, Mehdi Ebady Department of Information Networks College of IT University of Babylon Babylon Iraq Intelligent Medical Systems Department College of Sciences Al-Mustaqbal University Babylon51001 Iraq College of Information Technology University of Babylon Babylon Iraq

ISBN: (纸本)9783031810640

IoT devices play an integral part of our digital life today, yet pose significant security risks. These risks allow attackers to carry out various cyberattacks, mainly distributed denial of service (DDoS) attacks. To address these challenges, this paper proposes a hybrid deep learning approach that uses deep neural networks (DNNs) and long short-term memory (LSTM) networks to classify IoT cybersecurity attacks. The binary classification technique of LSTM is applied, whereby the models indicated sufficient results in terms of accuracy and performance, with the LSTM model showing an accuracy of 0.999 and the DNN model showing an accuracy of 0.999. To enhance detection capabilities, we used a comprehensive approach that includes 15 different attack categories. Although the DNN model equals the LSTM model with an accuracy of 0.95 in some scenarios, the hybrid model, which integrates the DNN feature outputs and the LSTM-derived features, showed an overall accuracy of 95.36. We further conducted extensive tests on the Edge-IIoT dataset to validate the effectiveness of our hybrid model. The proposed solution effectively exploits the strengths of the DNN and LSTM architectures, providing a robust framework for detecting and classifying DDoS attacks within IoT networks. These results contribute to a promising approach to mitigate the risks associated with IoT vulnerabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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DDoS Attacks: Detection Techniques, Challenges, and Modern Practices

DDoS Attacks: Detection Techniques, Challenges, and Modern P...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Ramesh Redekar Rajesh Bharati Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Internet, one of the essential components of today's civilization, serves numerous purposes for individuals, businesses, and society. However, its extensive use has sparked concerns, especially regarding privacy and cybersecurity. Furthermore, cyber dangers are becoming more dangerous, intense, and complicated. distributed Denial of Service (DDoS) attacks have evolved as a prevalent and substantial danger to cybersecurity that may disable the network infrastructures of targeted companies and providers. To guard from DDoS assaults, a many security measures are used, like firewalls and intrusion detection systems. Improving the protective capabilities of IDS frameworks using machine and deep learning, other associated technology, is a popular topic currently. Nevertheless, regardless of considerable improvements, identifying DDoS assaults using machine and deep learning, other associated techs remain a difficulty, particularly when dealing with new DDoS attack. Consequently, this review aims to comprehensively discuss about DDoS attacks by going through the contemporary efforts made in the literature to counter the danger due to the DDoS attacks. First, we investigate certain DDoS attack-related solutions suggested by today’s investigators. Finally, we delve deeper by identifying the domains wherein the DDoS attacks are prone to take place; common challenges in recognizing the DDoS attacks in the IoT circumstance; advantages of Software-Defined Networking (SDN); state-of-the-art practices in the academic community to counter DDoS attack attempts in any networks of IoT or SDN or web-connected devices.

关键词： Deep learning Privacy Reviews Prevention and mitigation Intrusion detection Denial-of-service attack Internet of Things Object recognition computer crime Software defined networking

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20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

20th International Conference on Data Science, ICDATA 2024, ...

引用

20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858550

The proceedings contain 37 papers. The special focus in this conference is on Data Science. The topics include: Trust as a Classification Tool: Analyzing Collaboration in Senate Floor Speeches on Gun Legislation Post-Uvalde and Sandy Hook;an Application of Convolutional Neural networks to Chaotic systems;artificial Intelligence Data Reduction Algorithm for Streaming Readout in High Energy Physics Experiment;user Behavior Based Implicit Personality Detection in Recommendation systems;proactive Management of Delays in the French Railway Network: A Seasonal Machine Learning Based Approach;CNN to BiLSTM: Enhancing Setswana Named Entity Recognition;Assessing the Impact of Homelessness on COVID-19 Hospitalization Rates in Patients with Underlying Medical Conditions Through Explainable AI;leveraging Ensemble Learning Paradigms for B2B E-Commerce Fraud Detection;exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies;explainable Machine Learning Approach for Intelligent Edits of Medicaid Home Healthcare Services Claims;authAttLyzer-V2: Unveiling Code Authorship Attribution Using Enhanced Ensemble Learning Models and Generating Benchmark Dataset;Real-Time AI Voice Clone Detection: A Deep Learning Approach to Safeguard Authenticity;Enhancing security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model;generating Culturally Appropriate Avatars;NLP-Guided Synthesis: Transitioning from Sequential Programs to distributed Programs;Generative AI Techniques for the Simulation of Groundwater Well Data at Hanford Site;Enhancing Financial Analysis with Generative AI: Utilizing Large Language Models for Efficient Data Extraction;Leveraging NLP and Large Language Models for Clinical Documentation Improvement: A Medical AI Chatbot Approach;Harnessing LLMs to Build an Autonomous Marketing Agent;Generative AI Use in One Health, One System as Healthcare is Global;dataDock: An Open Source Data Hub for Research;big Data Techniques i

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Comparative Analysis of Communication Protocols in Blockchain-IoT systems for Optimizing IoT Data Flow Efficiency

Comparative Analysis of Communication Protocols in Blockchai...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Mnar Alnaghes Hong Shen School of Computer Science University of Adelaide Adelaide Australia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The Internet of Things (IoT) is considered an evolving technology, consisting of different types of connected devices that have sensors, specialized hardware, and software. However, considerable challenges related to user data privacy, security, and device integrity are presented by this swift expansion, with digital identity being identified as a significant concern in the online landscape. Established by the World Wide Web Consortium (W3C) [2], Decentralized Identifiers (DIDs) represent a standardized format for defining identity. A peer-to-peer model for managing identity and credentials through self-sovereign identity is facilitated by DIDs, in conjunction with blockchain technology and public-key cryptography. The development of verifiable credentials is enabled by the combination of DIDs and blockchain technology, allowing for the verification of the identities of transacting parties through distributed identity registration and the identification of cryptographic keys necessary for validating electronic authentication during transactions. This integrated framework is regarded as both reliable and trusted. Device registration, verification, and credential revocation are enabled by it. In addition, security is ensured, the privacy of participating nodes is respected, and data integrity is upheld to address several existing challenges, such as data scalability and confidentiality. Trusted management and transaction of IoT data are ensured by the presented IoT data platform. Blockchain, decentralized identifiers, and public key cryptography are integrated by the authors to develop this platform. Self-sovereign identity is granted to each participating device, and dynamic changes in components like consensus algorithms and databases are supported by key features. Different IoT communication protocols are evaluated within a decentralized blockchain infrastructure for identity verification by the authors, focusing on interactions between IoT devices and networked se

关键词： Wireless communication Data privacy Wireless sensor networks Protocols W3C Public key cryptography Blockchains Peer-to-peer computing Internet of Things Optimization

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A Powered-Up Classification of Disabling distributed Network Cloud-Based Attacks Using MLPNN-BP and MLPNN-LM 1

引用

3rd international conference on Machine Intelligence and Smart systems, MISS 2023

作者： Singh, Dhiraj Mishra, Rahul Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DDoS attacks are so named because they involve multiple computers working together to disable a service so that it can be taken offline entirely. By diminishing victim resources, the assaults prevent legitimate users from accessing the targeted services. In a classification problem, the classes are already defined and do not overlap before the method is applied, making classification itself a very straightforward problem to solve. Back Propagation (BP), Multi-Layer Perceptron Neural Network (MLP-NN), Levenberg-Marquardt (LM) method, MLPNN-BP, MLPNN-LM, and Radial Basis Function (RBF) were all proposed in this paper (RBF). It is possible that the BP algorithm can be employed successfully for training Neural networks (NN). After the learning phase, the MLP-NN is able to generalise to completely new data. During the training phase, the weights are optimised to minimise the occurrence of any specified error functions. The weight configuration used as a starting point by the LM algorithm is used by all MLP-NNs in this combination method. After the LM training is complete, MLP-NN will use the validation data's MSE to compute. RBF was developed to address the interpolation issue with multiple variables. This is an example of a local approximation of a NN, and it has a high approximation power, a high classification accuracy, and a fast learning rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radial basis function networks

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Scalable Intrusion Detection Using Recurrent Neural networks in distributed Data Engineering systems 3rd

Scalable Intrusion Detection Using Recurrent Neural Networks...

引用

3rd international conference on Advancements in Smart Computing and Information security, ASCIS 2024

作者： Jeyaram, A. Muthukumaravel, A. Chennai India Chennai India

ISBN: (纸本)9783031862892

Traditional Intrusion Detection systems (IDS) frequently rely on anomaly and signature-based methods, which have limits in terms of accuracy, scalability, and flexibility. Anomaly-based systems generate a high number of false positives, causing operational problems, but signature-based solutions cannot protect against zero-day assaults. To overcome these problems, the proposed system improves IDS performance in dispersed data engineering contexts by utilizing Long Short-Term Memory (LSTM) networks, which are a type of Recurrent Neural Network (RNN). Furthermore, network data has temporal correlations, which the LSTM-based IDS can utilize to reduce false positives and improve detection accuracy. The methodology ensures maximum fault tolerance and resiliency while scaling to handle terabytes of real-time data by distributing the LSTM model across a cluster and cloud-based architecture. Research results reveal that the proposed approach outperforms existing IDS approaches. For instance, the KDD Cup 99 dataset, achieved 92.5% accuracy, which is higher than other top algorithms in this scenario defined as: Precision (88.4%), and recall (91.2%). F1 score: 89.7%. However, the scores acquired from the NSL-KDD dataset were 91.2% F1 score and 93.2% accuracy, which is rather low in comparison to our results but can provide us with an elementary understanding of the outliers present in both approaches. Furthermore, the techniques greatly reduced false positives and false negatives by overcoming modern cybersecurity challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Recent Trends in computer networks and distributed systems security

Recent Trends in Computer Networks and Distributed Systems S...

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2nd international conference on security in computer networks and distributed systems, snds 2014

作者： Pérez, Gregorio Martínez Thampi, Sabu M. Ko, Ryan Shu, Lei University of Murcia Spain Indian Institute of Information Technology and Management - Kerala Trivandrum India The University of Waikato Hamilton New Zealand Guangdong University of Petrochemical Technology Maoming China

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Protocol for secure submissions into learning management systems

Protocol for secure submissions into learning management sys...

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international conference on security in computer networks and distributed systems, snds 2012

作者： Mattam, Manjunath MSIT Division International Institute of Information Technology Gachibowli Hyderabad Andhra Pradesh 500 032 India

ISBN: (纸本)9783642341342

This paper proposes a model (architecture and protocol) that will help in securing assignment submissions into learning management systems. A client server architecture that uses cryptography is proposed to transform a regular assignment deliverable into a secure deliverable. A protocol is devised between client and server such that faculty and students can securely obtain symmetric keys to either encrypt or decrypt an assignment deliverable. © 2012 Springer-Verlag.

关键词： Cryptography

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snds: A distributed monitoring and protocol analysis system for wireless sensor network

SNDS: A distributed monitoring and protocol analysis system ...

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2nd international conference on networks security, Wireless Communications and Trusted Computing, NSWCTC 2010

作者： Kuang, Xin Shen, Jianhua Department of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9780769540115

Monitoring a large-scale wireless sensor networks (WSN) is very difficult, not only because it is large and complex, much of difficulty comes from the lack of visual analysis tools. This paper describes snds (Sensor Network distributed Sniffer), a distributed monitoring and protocol analysis system for large and complex sensor networks. snds is based on the use of sniffers co-deployed with the target sensor network. We mainly study and solve the following difficulties: monitoring sensor networks with large traffic, synchronizing the time among the distributed sniffers accurately, analyzing the protocols efficiently and flexibly. We test the stability and time synchronization accuracy of the whole system. The results show that the snds has the capability to work stably, synchronize the time accurately and analyze the wireless protocols efficiently. © 2010 IEEE.

关键词： Wireless sensor networks

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Experimental DRM model using mobile code and white-box encryption

Experimental DRM model using mobile code and white-box encry...

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international conference on security in computer networks and distributed systems, snds 2012

作者： Ghita, Stefan-Vladimir Patriciu, Victor-Valeriu Bica, Ion Computer Science Department Military Technical Academy Bucharest 050141 Romania

ISBN: (纸本)9783642341342

Due to severe DRM failures and the urgent demand for a better solution to solve the existing DRM systems vulnerabilities, this paper discusses the current DRM status, stressing system security objectives and requirements. Based on the analysis of the developed DRM solutions and emerging state of the art DRM technologies, this paper attempts to account for the causes of DRM failures and proposes a novel DRM security paradigm. We present an experimental DRM architectural rework aiming to cope with the existing limitations and weaknesses by using novel technological breakthroughs. © 2012 Springer-Verlag.

关键词： Mobile agents

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