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A Trust Management Framework for Software Defined networks-based Internet of Things

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A Trust Management Framework for Software Defined Networks-b...

International Conference and Workshop on Computing and Communication (IEMCON)

作者： Svetlana Burikova JooYoung Lee Rasheed Hussain Iuliia Sharafitdinova Roman Dzheriev Fatima Hussain Salah Sharieh Alexander Ferworn Security and Network Engineering Innopolis University Innopolis Russia College of Engineering Australian National University Canberra Australia Networks and Blockchain Lab Innopolis University Innopolis Russia API Delivery and Operations Royal Bank of Canada Toronto Canada Department of Computer Science Ryerson University Toronto Canada

ISBN: (纸本)9781728125312

The proliferation of smart objects and their connectivity have contributed to the realization of the Internet of Things (IoT) paradigm, offering a plethora of applications and services in many sectors of our lives. There are a number of communication architectures put forth to realize commercial IoT. Among other architectures, programmable networking solves most of the network management problems through Software Defined networks (SDN). In SDN, the control plane is separated from the data plane and hence can be a perfect choice for IoT where things have to be managed from different perspectives such as communications, resource allocation, energy consumption, and so on. Addressing the problems of traditional networks through SDN indirectly advocates for its use in IoT environments. However, the "softwarization" of the network, i.e. SDN, poses new challenges to the network security. Among other security issues, lack of trust between the SDN controller and network management application (application that controls the network behavior) is one of the key security problems in SDN that may jeopardize IoT security. To fill the gaps, in this paper, we propose a trust establishment framework for SDN. The main idea is to establish direct trust between OpenFlow SDN controller and the applications. Our results show that by using the proposed trust management framework, financial losses could be considerably reduced for the networks.

关键词： Internet of Things Trust management Authentication Software Authorization computer architecture

A survey of gene selection and classification techniques based on cancer microarray data analysis 4

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A survey of gene selection and classification techniques bas...

4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Zhang, Xinman He, Tingting Ouyang, Liwei Xu, Xuebin Chen, Sushing School of Electronics and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China Guangdong xi'An Jiaotong University Academy Foshan China CISE Department for Computer Information Science Engineering University of Florida Systems Biology Lab Gainesville United States

ISBN: (纸本)9781538683392

A growing number of cancer deaths draws attractions in the areas of bioinformatics worldwide. It is worth noting that cancer microarray data is composed of tens of thousands of genes, but there are only a few dozens of genes that are really relative to cancers. Thus, it is particularly import to pick out the relevant genes by means of microarray data analysis. Gene selection and classification techniques capture the essence of cancer mechanism and represent a suite of the refreshing cancer cure. In the paper, we summarize 4 types of cancer microarrays which show up in reference providing their attributes and download websites. Furthermore, we also review most extensively studied gene selection and classification techniques. Meanwhile, comparison and analysis on accuracies of gene selection and classification methods in view of remarkably influential algorithms are outlined with unique and randomized algorithms. Thus, the most correlative cancer genes are selected, Leukemia and Colon cancer as typical representatives are listed in the survey. From the microcosmic perspectives, these emerging techniques are considerably conductive to solve the dimensional curse of cancer microarray data applied in the treatment of cancer. © 2018 IEEE.

关键词： Genes

Enjoy the untrusted cloud: A secure, scalable and efficient SQL-like query framework for outsourcing data

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arXiv 2019年

作者： Chen, Yaxing Zheng, Qinghua Liu, Dan Yan, Zheng Sun, Wenhai Zhang, Ning Lou, Wenjing Thomas Hou, Y. School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University Xi'an Shaanxi China Department of Communications and Networking Aalto University Espoo Finland Department of Computer and Information Technology Purdue University West LafayetteIN United States Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO United States Department of Computer Science Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

While the security of cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based solutions, e.g. partially/fully homomorphic encryption and searchable encryption, suffer from low performance, poor expressiveness and weak compatibility. An alternative method that utilizes hardware-assisted trusted execution environment, i.e., Intel SGX, has emerged recently. On one hand, such work lacks of supporting scalable access control over multiple data users. On the other hand, existing solutions are subjected to the key revocation problem and knowledge extractor vulnerability. In this work, we leverage the newly hardware-assisted methodology and propose a secure, scalable and efficient SQL-like query framework named QShield. Building upon Intel SGX, QShield can guarantee the confidentiality and integrity of sensitive data when being processed on an untrusted cloud platform. Moreover, we present a novel lightweight secret sharing method to enable multi-user access control in QShield, while tackling the key revocation problem. Furthermore, with an additional trust proof mechanism, QShield guarantees the correctness of queries and significantly alleviates the possibility to build knowledge extractor. We implemented a prototype for QShield and show that QShield incurs minimum performance cost. Copyright © 2019, The Authors. All rights reserved.

关键词： Outsourcing

Intelligent Eco Networking (IEN) II: A Knowledge-Driven Future Internet Infrastructure for Value-Oriented Ecosystem

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Intelligent Eco Networking (IEN) II: A Knowledge-Driven Futu...

IEEE International Conference on Hot Information-Centric Networking (HotICN)

作者： Kai Lei Shuokang Huang Jiyue Huang Huafang Liu Jin Liu PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB) School of Electronic and Computer Engineering (SECE) Peking University Shenzhen P.R. China

ISBN: (数字)9781728157818

ISBN: (纸本)9781728157825

Intelligent Eco Networking (IEN) is intent on evolving the IP-based network architecture into a Knowledge-Driven Future Internet Infrastructure based on hierarchical heterogeneous networks composed of virtualized and configurable devices, taking the valuable contents as first-class entity, d riven by knowledge intelligence, integrating the decentralized consensus trust preservation, to establish a value-oriented social ecosystem. To counter with the complicated but inefficient mechanisms of IP, a tokenized fine-grained allocation mechanism i s proposed, taking comprehensive consideration of storage, computing and networking, bringing about a collaborative and mutually beneficial value-interconnection. Knowledge-Driven Scheme (KDS), as the guideline of IEN's establishment, illustrates the way to extract knowledge from the network, the value consensus of Blockchain, and the feedback control of Federated Learning, making economy-based distributed commerce feasible.

关键词： Knowledge engineering Quality of service Internet Protocols Ecosystems

Data analytics for fog computing by distributed online learning with asynchronous update

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arXiv 2019年

作者： Li, Guangxia Zhao, Peilin Lu, Xiao Liu, Jia Shen, Yulong Shaanxi Key Laboratory of Network and System Security Xidian University China State Key Laboratory of Integrated Service Networks Xidian University China School of Computer Science and Technology Xidian University China Tencent AI Lab Tencent Inc. China Department of Electrical and Computer Engineering University of Alberta Canada

—Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

LPD communication: A sequential change-point detection perspective

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arXiv 2019年

作者： Huang, Ke-Wen Wang, Hui-Ming Towsley, Don Vincent Poor, H. School of Electronics and Information Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi710049 China College of Information and Computer Sciences University of Massachusetts AmherstMA01002 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In this paper, we establish a framework for low probability of detection (LPD) communication from a sequential change-point detection (SCPD) perspective, where a transmitter, Alice, wants to hide her signal transmission to a receiver, Bob, under the surveillance of an adversary, Willie. The new framework facilitates to model LPD communication and further evaluate its performance under the condition that Willie has no prior knowledge on when the transmission from Alice starts and that Willie wants to detect the existence of the communication as quickly as possible in real-time manner. We consider three different sequential tests for Willie, i.e., the Shewhart test, the cumulative sum (CUSUM) test, and the Shiryaev-Roberts (SR) test, to model the detection procedure. Communication is said to be covert if it stops before detection by Willie with high probability. Covert probability defined as the probability that Willie is not alerted during the communication procedure is investigated. We formulate an optimization problem aimed at finding the transmit power and transmission duration such that the total amount of information that can be transmitted is maximized subject to a high covert probability. Under Shewhart test, closed-form approximations of the optimal transmit power and transmission duration are derived, which well approximate the solutions obtained from exhaustive search. As for CUSUM and SR tests, we provide an effective algorithm to search the optimal solution. Numeric results are presented to show the performance of LPD communication. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability

HOMMO: A Hierarchical Flow Management Framework for Multi-Objective Data Center networks

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HOMMO: A Hierarchical Flow Management Framework for Multi-Ob...

IEEE Global Communications Conference

作者： Kai Lei Junlin Huang Yu Li Fan Zhang Hengky Susanto Bo Bai Gong Zhang Jin Liu Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB) School of Electronic and Computer Engineering (SECE) Peking University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Theory Lab 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In data center networks, flows with different objectives coexist and compete for limited resources. From the application-level perspective, it is hard to satisfy the demands of different flows without effective resource planning. To address the bandwidth allocation problem under multi-objective scenarios, we study a multi-objective network utility maximization (NUM) problem and propose our practical bandwidth allocation framework HOMMO, which consists of an upper layer algorithm (ULA) and a lower layer algorithm (LLA). This hierarchical design helps to strike a balance between accuracy and efficiency. Implemented in network switches, ULA is an online learning-based scheme that allocates bandwidth for aggregated flows with different performances objectives. Taking the outputs from ULA as capacity constraints, LLA acts as a fast scheduling method at packet-level. We extend NUMFabric by implementing the design of isolate queues and corresponding dequeue strategy in switches and make it a feasible solution of the LLA. Therefore, HOMMO achieves satisfactory isolation across flows with different objectives, which is equivalent to provide a network slicing solution. To evaluate the proposed framework, we implement it in ns-3 and verify the performance under various scenarios. The simulation results show that HOMMO not only quickly converges to a near-optimal solution of the multi-objective NUM problem but also guarantees a Pareto-optimal solution. Moreover, it outperforms the well-known transport protocol (i.e., DCTCP) with 2x increase on average bandwidth utilization and 1.96x improvement in global network utility at the aggregated flow level.

关键词： Channel allocation Bandwidth Data centers Scheduling Throughput Convex functions Machine learning

Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI