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Secure and Reliable Indoor Localization Based on Multitask Collaborative Learning for Large-Scale Buildings

ieee INTERNET OF THINGS JOURNAL 2022年第22期9卷 22291-22303页

作者： Wang, Chun Luo, Juan Liu, Xuan He, Xiangjian Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Hunan Xiangjiang Artificial Intelligence Acad Changsha 410082 Peoples R China Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia

Accurate and reliable indoor location estimate is crucial for many Internet-of-Things (IoT) applications in the era of smart buildings. However, the positioning accuracy and security of the existing positioning works cannot meet the demands in the large-scale smart buildings scenarios covering multiple multifloor buildings. Therefore, in this article, we focus on the reliable and accurate localization under multibuilding and multifloor environments. We propose two novel designs, including a two-step reliable feature selector and a multitask collaborative positioning model. First, we design a two-step reliable feature selector based on an access point (AP) confidence model and manifold learning, to help select the most representative and reliable fingerprint features. Second, we propose a multitask cooperative positioning model, which consists of a multiscale feature fusion module to adaptively fuse multiscale features and a multitask joint learning module to effectively constrain the cumulative error of multiscale position. Finally, based on the above two, we propose a reliable multibuilding and multifloor localization method (RMBMFL), which can achieve accurate and reliable location estimates with low computational complexity in a smart building complex. We did real-world experiments in a 20 000 m(2) site that covers three multistory buildings to evaluate the performance of the proposed RMBMFL. The experimental results show that RMBMFL achieves a building identification accuracy and a floor identification accuracy of 99%, and a room-level indoor localization with an average positioning error within 2 m, and outperforms state-of-the-art solutions.

关键词： Attention multibuilding/multifloor multiscale feature fusion multitask learning reliable indoor localization smart buildings Wi-Fi fingerprint

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Cyber security Issues in the Industrial applications of Digital Twins

Cyber Security Issues in the Industrial Applications of Digi...

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ieee symposium Series on computational intelligence (SSCI)

作者： Sunzida Siddique Mohd Ariful Haque Rakib Hossain Rifat Roy George Khalil Shujaee Kishor Datta Gupta Computer Science and Engineering Daffodil International University Dhaka Bangladesh Cyber Physical Systems Clark Atlanta University Atlanta GA Computer Science and Engineering BRAC University Dhaka Bangladesh

Transformative developments have been brought in across several industries. Digital twin technologies are one of them. This revolutionary innovation has enhanced efficiency, optimized production, and elevated product design to new heights. Nevertheless, as industries embrace the potential of digital twins, cybersecurity concerns come to the forefront due to the convergence of physical and virtual realms. By addressing cybersecurity challenges effectively, industries can fully capitalize on the transformative capabilities of digital twin technology, driving competitiveness and resilience in the face of evolving digital landscapes. Our research explores the various industrial applications of digital twin technology. It also highlights the urgent need for strong cybersecurity measures. Secure data transmission, access control, encryption, and threat detection become crucial elements that must be ensured for digital twin systems in the industrial sector. Our study fills cybersecurity gaps in digital twin applications, offering actionable information for strong security.

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DECEPT-CTI: A Framework for Enhancing Cyber Deception Strategies through NLP-based Extraction of CTI from Unstructured Reports 22

DECEPT-CTI: A Framework for Enhancing Cyber Deception Strate...

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22nd International symposium on Network Computing and applications, NCA 2024

作者： Sayari, Amal Rekhis, Slim Djemaiel, Yacine Mabrouk, Ali Mahouachi, Wiem LR11TIC04 Communication Networks and Security Research Lab. & LR11TIC02 Green and Smart Communication Systems Research Lab Tunisia SAMA PARTNERS Tunisia

ISBN: (纸本)9798331510183

In today’s dynamic threat landscape, organizations encounter significant challenges when dealing with the vast, evolving, unregulated landscape of cyber threat intelligence (CTI). Firstly, the verbosity of CTI resources infused with a significant amount of irrelevant and unstructured text. Secondly, there is a need for automated systems capable of synthesizing information about threat entities, attack semantics, and malicious actions, which can help rapidly identify and understand emerging threats. Thirdly, organizations face the challenge of injected poisoned or noisy data by threat actors, which can affect not only the quality of CTI but also the efficacy of threat mitigation strategies. Finally, they must harmonize CTI data from various resources and understand the interactions between different threat elements, to understand their threat profiles better, determine the presence of attack vectors related to existing and emerging cyber-attacks, and mount effective cyber defense strategies. To address these challenges, we introduce DECEPT-CTI, a framework that recommends the appropriate cyber deception strategies based on the provided CTI knowledge. First, we automatically extract relevant CTI sentences and identify low-level and high-level IoCs from unstructured CTI reports. Then, we propose a multi-label classification-based model for automated mapping of extracted CTI data to MITRE ATT&CK tactics and techniques. Subsequently, we apply an analytical scheme based on MITRE D3FEND and MITRE Engage to select the most suitable cyber deception strategies using the extracted CTI data. Our evaluation encompasses various transformers: SecBERT, SecRoBERTa, SecBERT-BiLSTM-CRF, and SecRoBERTa-BiLSTM-CRF to perform the low-level and high-level IoCs identification and extraction task, and the multi-label classification task, demonstrating good results. ©2024 ieee.

关键词： Cyber attacks

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Rallying Adversarial Techniques against Deep Learning for Network security

Rallying Adversarial Techniques against Deep Learning for Ne...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Clements, Joseph Yang, Yuzhe Sharma, Ankur A. Hu, Hongxin Lao, Yingjie Clemson Univ Clemson SC 29634 USA Univ Buffalo Buffalo NY USA

ISBN: (纸本)9781728190488

Recent advances in artificial intelligence and the increasing need for robust defensive measures in network security have led to the adoption of deep learning approaches for network intrusion detection systems (NIDS). These methods have achieved superior performance against conventional network attacks, enabling unique and dynamic security systems in real-world applications. Adversarial machine learning, unfortunately, has recently shown that deep learning models are inherently vulnerable to adversarial modifications on their input data. In this work, we explore the potential of adversarial entities to compromise such vulnerabilities to compromise deep learning-based NIDS systems. Specifically, we show that by modifying on average as little as 1.38 of an observed packet's input features, an adversary can generate malicious inputs that effectively fool a target deep learning-based NIDS. Therefore, it is crucial to consider the performance from the conventional network security perspective and the adversarial machine learning domain when designing such systems.

关键词： Network Intrusion Detection System Adversarial Machine Learning Adversarial Examples Deep Learning

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‘Parenting the Internet’ App: a Web Guardian for Protecting Neurodiverse Users Online

‘Parenting the Internet’ App: a Web Guardian for Protectin...

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International symposium on computational intelligence and Informatics

作者： Ryan Jones M A Hannan Bin Azhar School of Engineering Technology and Design Canterbury Christ Church University Canterbury United Kingdom

ISBN: (数字)9798350353433

ISBN: (纸本)9798350353440

The rising prevalence of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) necessitates tailored digital interventions for safeguarding and enhancing the well-being of affected individuals. This paper presents the development and evaluation of a parental control application, ‘Parenting the Internet’ (PTI), designed specifically for individuals with ASD and ADHD. By conducting a comparative analysis of internet usage and safety concerns between neurodiverse and neurotypical participants through detailed questionnaires, the study identifies the unique challenges faced by these groups. Key findings include preferences for specific app features, the importance of a user-friendly interface, and robust security measures. The evaluation highlights strengths in customisation, usability, and scalability while identifying areas for improvement such as educational content expansion and enhanced automation. This research contributes to the development of inclusive digital safety solutions tailored to the needs of neurodiverse users, with implications for broader applications.

关键词： Autism Automation Atmospheric measurements Scalability Particle measurements Safety security Usability Informatics computational intelligence

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Simplification on mobile devices reduces personal (cyber) safety 15

Simplification on mobile devices reduces personal (cyber) sa...

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ieee 15th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Szarvak, Aniko Poser, Valeria Kozlovszky, Miklos Univ Obuda Doctoral Sch Appl Math & Appl Informat Budapest Hungary Univ Obuda Inst Biomat & Appl Artificial Intelligence BMI Budapest Hungary Univ Obuda BioTech Res Ctr EKIK Budapest Hungary

ISBN: (纸本)9781728195438

Due pandemic reasons, the phishing attack numbers are 6 times bigger than normally, approximately. If we assume the defense stays in the same level, this means 6 times more loss than before. Phishing attacks are targeting not just companies but also private individuals, however actual data is not available on individual attack, so the real effect on individuals, their cumulative loss with business is unknown. This study targets the review of mobile attacks and available prevention assets and materials. This paper collects evidences on how simplified - mobile - interfaces according to PC reduces the opportunity to identify cyberattacks as phishing attack. The goal of this document is to point out minor differences between PC-s and mobiles and their user behavior. Mobile devices are widely used and widely targeted by attackers and mobile devices in contrast with PC provides fewer opportunity and feedback to users to avoid attacks.

关键词： awareness phishing information technology safety awareness information security computer systems

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A Distributed Multi-User Access Control Middleware for Critical applications

A Distributed Multi-User Access Control Middleware for Criti...

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ieee symposium Series on computational intelligence (SSCI)

作者： Alexander Williams Arunava Roy Dipankar Dasgupta Center for Information Assurance University of Memphis

We present a novel access control middleware for distributed multi-user review. The system uses a fuzzy inference system trained on real world access control rules to evaluate and select reviewers as an extension to a more traditional access control system. The method is intended for high security need specific requests, as a supplement to regular access control methods. In this way, it models a multi-person access system common in mechanical controls like missile launches, bank vault opening, and other high criticality domains. The proposed method improves security by increasing the number of compromised users needed to perform an attack, taking advantage of situational awareness of peer users in a system. We evaluate the proposed system with an example implementation based on a real-world organization, and show that the system can be used to effectively implement a secure resource access control system. Our work contributes to the growing body of research into fuzzy-logic access control, ML in access control, and multi-user authentication systems.

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Feature Selection Methods for Intrusion Detection Systems in IoT

Feature Selection Methods for Intrusion Detection Systems in...

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Ons Saadallah Ikrame Nouar Department of Data Science and Engineering ELTE - Eotvös Loránd University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

IoT systems enhance daily life through smart applications but are vulnerable due to the increasing number of con-nected devices. To address security concerns, Intrusion Detection Systems (IDS) are crucial for detecting and preventing attacks. While various IDS techniques use computational datasets, many features are irrelevant, prompting the use of feature selection (FS) methods, such as wrapper and embedded techniques, to improve performance. This paper evaluates the impact of FS methods on machine learning models like Naive Bayes, Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors, and SVM for binary and multi-class classification of network attacks. It also explores FS effects on a CNN-LSTM model using the BoTNeTIoT dataset.

关键词： Training Support vector machines Performance evaluation Logistic regression Intrusion detection Nearest neighbor methods Feature extraction security Random forests Machine intelligence

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CTIFuser: Cyber Threat intelligence Fusion via Unsupervised Learning Model

CTIFuser: Cyber Threat Intelligence Fusion via Unsupervised ...

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International symposium on Parallel and Distributed Processing with applications, ISPA

作者： Chunyan Ma Zhengwei Jiang Jun Jiang Peian Yang Mengjiao Cui Fangming Dong Huamin Feng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Beijing Electronic Science and Technology Institute Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Cyber attack campaigns are becoming increasingly complex and severe, causing significant impacts on institutions and individuals. Cyber Threat intelligence (CTI) provides important evidential knowledge about attackers and is critical to the shift from reactive to proactive defense against cyber attacks. Attack detection based on Indicators of Compromise (IOCs), a type of CTI, is vulnerable to the limitation of insufficient context of attack scenarios. In contrast, attack behavior intelligence is associated with information on attackers’ techniques, targets, and intentions, providing a solid foundation for security practitioners to conduct attack investigations or other applications. Many current CTI mining systems are limited to extracting CTI from a single source, leading to challenges such as fragmented attack behavior view and low-value density. To address these issues, we propose an unsupervised fusion framework named CTIFuser, which includes a comprehensive pipeline of four subtasks aimed at mining and fusing multi-source attack behaviors at the attack technique level. In our evaluation of 739 real-world CTI reports from 542 sources, experimental results demonstrate that CTIFuser can obtain a complete view of the attack behaviors at the attack technique level.

关键词： Distributed processing Pipelines Solids Cyber threat intelligence security Cyberattack Unsupervised learning

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Enhancing Intrusion Detection Systems with representation methods: A comparative study

Enhancing Intrusion Detection Systems with representation me...

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computational intelligence in security, Defence and Biometrics (CISDB), ieee symposium on

作者： Bruno H. Meyer Aurora T. R. Pozo Michele Nogueira Wagner M. Nunan Zola Department of Informatics Federal University of Paraná Brazil Department of Computer Science Federal University of Minas Gerais Brazil

ISBN: (数字)9798331508296

ISBN: (纸本)9798331508302

This paper presents a comparative analysis of three data representation methods for improving Intrusion Detection Systems (IDS). The methods compared are autoencoders, Generative Adversarial Networks (GANs), and contrastive learning. Additionally, a baseline approach using raw input data is evaluated. The study is conducted on three well-known IDS datasets: NSL-KDD, Ton-IoT, and Bot-IoT, each with distinct characteristics. Our results demonstrate that representational methods significantly enhance classification performance, particularly when ample unlabeled data is available. Among the methods, GANs achieved the highest f1-score improvements in the Ton-IoT dataset, while contrastive learning excelled in the Bot-IoT dataset. The experiments also reveal that the choice of classifier impacts performance, with Random Forest performing best on raw data and Multi-Layer Perceptrons (MLP) excelling with transformed data. The study highlights the importance of selecting appropriate representation learning techniques and classifiers based on dataset characteristics. It emphasizes the potential of unsupervised learning methods to utilize large volumes of unlabeled data, a common scenario in real-world cybersecurity applications. The findings provide a foundation for future research in leveraging unsupervised learning for IDS and other cybersecurity challenges.

关键词： Representation learning Autoencoders Intrusion detection Data visualization Contrastive learning NSL-KDD Generative adversarial networks Computer security Unsupervised learning Random forests

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