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A Multi-attention Based CNN-BiLSTM Intrusion Detection Model for In-vehicle networks 21

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Gao, Kai Huang, Hao Liu, Linhong Du, Ronghua Zhang, Jinlai Changsha University of Science & Technology College of Automotive and Mechanical Engineering Changsha410114 China

ISBN: (纸本)9798350329223

The development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. This paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area Network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. The model achieved a detection accuracy of 99.992%. The reduction of features effectively simplifies the model and improves its computation efficiency. © 2023 IEEE.

关键词： Anomaly detection

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CNN-LSTM Model for Mitigation of DDoS Attacks in Software-Defined networks

CNN-LSTM Model for Mitigation of DDoS Attacks in Software-De...

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2024 international conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Jimoh, Ebunayo Rachael Oladele, Tinuke Omolewa Oladele, Oluwabunmi Titilope Akinlolu, Azeez Olawale Akanbi, Mulikat Bola Olabanke, Olasehinde-Williams University of Ilorin Faculty of Communication and Information Sciences Department of Computer Science Ilorin Nigeria University of Ilorin Faculty of Communication and Information Sciences Department of Information Technology Ilorin Nigeria Fountain University Department of Mathematical and Computer Sciences Osogbo Nigeria Kwara State Polytechnic Computer Science Department Kwara State Ilorin Nigeria University of Ilorin Centre for Open and Distance Learning Department of Computer Science Ilorin Nigeria

ISBN: (纸本)9798350358155

The Internet, as the world's largest computer network, has evolved beyond a mere repository of information to become an indispensable tool driving modern society. Its dynamic nature enables communication, interaction, and access to diverse information sources, shaping how we navigate the digital landscape. However, this ubiquity also makes it vulnerable to distributed Denial of Service (DDoS) attacks, where malicious actors deliberately disrupt system resources. As network operators transition to Software Defined networks (SDN) for improved visibility and control, the centralized architecture of SDN becomes susceptible to DDoS attacks, compromising network integrity. In this study, we developed a novel approach for mitigating DDoS attacks by integrating a CNN-LSTM model, achieving promising results with a loss of 0.0131, Accuracy of 0.9952, Precision score of 0.9918, recall score of 0.9961, and F1_score of 0.9939. This fusion of CNN-LSTM models into SDN-based DDoS mitigation systems holds significant potential for enhancing network security and resilience. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure Edge IoT Intrusion Detection Framework for Industrial IoT via Blockchain Integration 4

Secure Edge IoT Intrusion Detection Framework for Industrial...

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4th international conference on Blockchain Technology and Information security, ICBCTIS 2024

作者： Goel, Punit Khatri, Dignesh Kumar Gangu, Krishna Ayyagiri, Aravind Mokkapati, Chandrasekhara Hussien, Rami Riad Maharaja Agrasen Himalayan Garhwal University Department of Management Uttarakhand India Gujarat University Department of Computer Science & Engineering Gujarat India Chaitanya Bharathi Institute of Technology Osmania University Department of Mechanical Engineering Hyderabad India Wichita State University Department of Computer Science & Engineering DublinCA United States Jawaharlal Nehru Technological University Department of Cs & It Hyderabad India The Islamic University Najaf Iraq

ISBN: (纸本)9798331528324

In essence, blockchain is a distributed, secure ledger that contains a hierarchical network of blocks that maintains a record of all transactions. Bringing cloud capabilities closer to computation tasks is the goal of edge computing. It is possible to overcome existing security and scalability issues with blockchain and edge computing. security is strengthened by the integration of Blockchain technology into Industrial IoT (IIoT) intrusion detection frameworks. In the proposed framework, the decentralized and tamper-proof nature of Blockchain is leveraged to enhance trust and integrity at the edge of the network in the detection of malicious activities. It aims to identify and mitigate cyber threats in IIoT systems for a robust security solution. The framework's performance is measured using various IIoT scenarios, including detection accuracy, response time, delay time, and overall performance score. A combination of blockchain technology and edge-based intrusion detection significantly improves the security posture of IIoT networks, producing high detection accuracy and minimal delays. A more resilient, trustworthy and secure IIoT network can be achieved with this study, which advances secure IIoT architectures. © 2024 IEEE.

关键词： Network intrusion

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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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2024 international conference on Communication, computer Sciences and Engineering, IC3SE 2024

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Banasthali Vidyapith Department Of Computer Science And Engineering Rajasthan Tonk India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Vaddeswaram India Dpg Institute Of Technology And Management Department Of Computer Science And Engineering Gurugram India

ISBN: (纸本)9798350366846

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural networks (CNNs) to detect distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection. © 2024 IEEE.

关键词： Denial-of-service attack

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An Intense Systematic Review of Mobile Agent Based Harmful Threats and Attack Detection systems in IOT networks 3

An Intense Systematic Review of Mobile Agent Based Harmful T...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Vigenesh, M. Ravivarman, G. Singh, Prabhdeep Vaseem Akram, Shaik Sharma, Deepti Tiwari, Mohit Karpagam Academy of Higher Education Department of CSE Coimbatore India Karpagam Academy of Higher Education Department of EEE Tamil Nadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Uttaranchal University Department of Management Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350399264

A breach is detected by an intrusion detection system (IDS) by analysing events that are deemed risky from a security standpoint in systems and networks. Due to a surge in occurrences involving network security, organisations must implement IDS for the protection of sensitive data. Both intrusions coming from outside a network and those coming from within a distributed network are challenging to identify. IDS should be in charge of processing a sizable volume of data without overwhelming networks and monitoring systems. Due to the flaws and restrictions in centralised IDS, mobile agents (MA) were developed. By identifying harmful activity, these agents can carry out specified actions. From earlier works of literature, it was deduced that most of the existing IDS based on MA are not significantly effective © 2023 IEEE.

关键词： Internet of things

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A High-Payload Image Steganography Based on Shamir’s Secret Sharing Scheme 1

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7th international conference on Microelectronics and Telecommunication Engineering, ICMETE 2023

作者： Tyagi, Sanjive Alsaad, Maysara Mazin Garg, Sharvan Kumar Department of CSE Swami Vivekanand Subharti University Uttar Pradesh Meerut India Department of Computer Science Gujarat University Ahmedabad India Department of CSE Swami Vivekanand Subharti University Uttar Pradesh Meerut India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

This paper introduces an image steganography system that hides a high capacity of confidential data by utilizing a distributed computing scheme that offers the embedding of smaller, equally segmented confidential images across multiple cover images. The proposed strategy developed a strong security mechanism associated with self-synchronized methods by utilizing Shamir’s Secret Sharing (SSS) Scheme. To ensure extraordinary security for hidden confidential data within the cover image, double-layer security has been designed. In the primary layer, the Secret Distributing Scheme (SDS) divides a single larger secret image into identically smaller sub-images, and the SSS scheme then creates an encoded structure of smaller sub-images for assigning and restoring the distributed decomposed confidential images within the proposed shareholders. The SSS scheme will reorder the distributed confidential decomposed images into a predefined order by the authorized recipient at the moment of revealing secrets by interpolating polynomials. The next layer is steganography, where the image is segmented into 2 × 2 squares of pixels and navigated in a crisscross way;the pixel-value difference is computed among non-overlapping Red, Green, Blue (RGB) pixels in a diagonal direction within a focused square of pixel area. Secret bits are hidden within the RGB pixels of the cover image by using the presented innovative pixel-value differencing (PVD) strategy. The results of the research indicate that the presented system provides an inventive mechanism in the form of a double layer of security and vastly enhances embedding capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Comparison of Machine Learning Models for Classification of DDoS Attacks 5

Comparison of Machine Learning Models for Classification of ...

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5th international conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Prima, Felix Dylan, Leonardo Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

distributed Denial of Service (DDoS) is one of the major threats for security networks and systems. The DDoS attack is flooding a network with huge packets to weaken the performance of the network. The ability to accurately and classify these attacks is crucial for mitigating the security problem. In this paper, machine learning models are compared in a classification task for detecting benign (normal) and DDoS attack traffic. This paper used 14 different popular classifier models. The models are trained and tested by using 'DDoS Dataset' from Kaggle which is the extraction from public IDS Datasets which captured in different years from 2016-2018. The experiment result shown that all the models have good scores in precision, recall, and F1 score with Random Forest algorithm shown the best result at 100% accuracy. © 2023 IEEE.

关键词： Denial-of-service attack

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Impacts of High Denisty Cloud Computing on Data Protection and security Management for 6G Networking 2

Impacts of High Denisty Cloud Computing on Data Protection a...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Mallikarjunaradhya, Vinay Yennapusa, Haritha Palle, Ranadeep Reddy Suganyadevi, K. Gupta, Nidhi Legal Tech Platform Thomson Reuters Toronto Canada University of Central Missouri Department of Computer Science AustinTX United States University of Houston Department of Computer Science AustinTX United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Data protection and security management in the 6G era is a key challenge due to the emergence of high density cloud computing. The availability of high density cloud computing technologies creates a platform for massive data storage, processing, and analytics capabilities at a scale that has never been seen before. High density cloud computing facilitates the deployment of complex distributed systems consisting of hundreds or thousands of nodes in a single deployment. This can have a significant impact on data protection and security management as it increases the potential threat to the system from malicious actors. To protect data in this environment, organizations must adopt more advanced security practices such as access control, encryption, and defensive layers to protect against malicious actors. The use of encryption can provide a layer of security by making it difficult for outsiders to access information without authorization. Access control can also prevent unauthorized users from accessing certain parts of the system, while defensive layers can help detect and block malicious attacks. The results of this study showed that high density cloud computing can potentially improve the efficiency and performance of data protection and security management in 6G networks. © 2024 IEEE.

关键词： 6G Networking Cloud Data Storage Data Protection Network Reliability Network security security Management

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Comparing the Supersonic Cloud Computing Model to Enhance the Networking and security in Traditional Data Centers 2

Comparing the Supersonic Cloud Computing Model to Enhance th...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Palle, Ranadeep Reddy Mallikarjunaradhya, Vinay Yennapusa, Haritha Suganyadevi, K. Gupta, Nidhi University of Houston Department of Computer Science AustinTX United States Thomson Reuters Legal Tech Platform Toronto Canada University of Central Missouri Department of Computer Science AustinTX United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The Comparing the supersonic cloud computing model to enhance the networking and security in Traditional Data Centers study aimed to analyse the differences between traditional data centers and cloud computing models. The researchers' findings demonstrate that the supersonic cloud model consists of a number of services and functions that can improve network and security performance across all levels of the data center. Furthermore, the supersonic model offers more options for load balancing through routing protocols and more secure network access. In addition, the supersonic model allows for more flexibility in the resources used, such as virtual servers and storage systems. Lastly, the supersonic model allows for greater scalability through the use of distributed simultaneous networks. These benefits pave the way to the eventual goal of improving existing data center operation and security. The objective of this study was to assess the potential of supersonic cloud computing model (SCC) to improve the networking and security in traditional data centers. The findings indicated that SCC improved the network efficiency by 10.4%, reduced network latency by 11 %, and improved packet delivery rates by 8.6%. The security was also improved, with overall firewall rules increased by 12 %, and 5% fewer security violations. Overall, SCC showed promising performance in improving the networking and security of traditional data centers. © 2024 IEEE.

关键词： Data Centers Infrastructure Scalability Service Level Agreements Supersonic

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IoV Cyber Defense: Advancing DDoS Attack Detection with Gini Index in Tree Models 4

IoV Cyber Defense: Advancing DDoS Attack Detection with Gini...

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4th international conference on Emerging Trends in networks and computer Communications, ETNCC 2024

作者： Dilshad, Muhammad Almas, Bushra Tariq, Noshina Jazri, Husin Bin Alwakid, Ghadah Naif Khan, Jan Sher Kumar, Pranav Kumar, Rahul Institute of Information Technology QuaidiAzam University Islamabad Pakistan Air University Department of Avionic Engineering Islamabad Pakistan School of Computer Science Taylor's University Subang Jaya Malaysia Jouf University Department of Computer Sciences Sakaka Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

In today's ever-changing world of cybersecurity, where smart devices and the Internet of Vehicles (IoV) are everywhere, it is crucial to have a strong defense against attacks. This paper introduces an intelligent way to detect the distributed Denial of Service (DDoS) attacks, a severe threat that can disrupt access to essential resources. Traditional methods to handle such attacks include intrusion detection systems (IDS). However, we need new and creative solutions to increasingly complex data. Our proposed strategy is an Artificial Intelligence (AI)-based technique that uses a Decision Tree (Dt) algorithm with feature selection using the Gini index. The proposed DDoS detection system has depicted an impressive accuracy of 99.4% while tested with the CIC-DDoS2019 dataset, surpassing baseline and previous benchmarks like Random Forest and XGBoost. Our improved Gini index method smartly selects 39 essential security features from a pool of 88, cutting down on unnecessary data and preventing over-fitting. This approach efficiently demonstrates its practical use in real-world network security situations, especially IoVs. © 2024 IEEE.

关键词： Network intrusion

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