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2nd International Conference on cyber Resilience, ICCR 2024

作者： Waseem, Ahsan Kamil, Mohd Azhan Umar Bhardwaj, Abhisehek Razim, Abdul Qadeer, Mohammed Abdul Ghazal, Taher M. Aligarh Muslim University Department Of Electrical Engineering Aligarh India Aligarh Muslim University Department Of Computer Engineering Aligarh India Stony Brook University Department Of Electrical And Computer Engineering New York United States Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configuration for receiving circularly polarized signals, using a Software Defined Radio (SDR) at 137.62 MHz and a V-dipole antenna. The directional characteristics of the V-dipole maximize capture efficiency by improving signal reception during particular satellite orbits. The flexible SDR, in particular the RTL SDR USB dongle, is used to detail signal decoding for Automatic Picture Transmission (APT), allowing for effective processing and tracking. High-quality APT photos are produced by this hardware-software integration, highlighting NOAA's crucial role in international weather forecasting. The study also emphasizes how simple and inexpensive it is to use the V-dipole antenna to receive NOAA weather satellite signals. © 2024 IEEE.

关键词： Weather forecasting

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN Ensembles

Guardian of the Ensembles: Introducing Pairwise Adversariall...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Shukla, Shubhi Dalui, Subhadeep Alam, Manaar Datta, Shubhajit Mondal, Arijit Mukhopadhyay, Debdeep Chakrabarti, Partha Pratim Centre for Computational and Data Sciences Iit Kharagpur India Iit Kharagpur Computer Science and Engineering Department India Center for Cyber Security New York University Abu Dhabi United Arab Emirates Iit Kharagpur Department of Artificial Intelligence India Iit Patna Computer Science and Engineering Department India

ISBN: (纸本)9798331510831

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL © 2025 IEEE.

关键词： adversarial attack decision-boundary diversity defense ensemble models

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Information spreading in mobile social networks: Identifying the influential nodes using FPSO algorithm 10

Information spreading in mobile social networks: Identifying...

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10th International Conference on Information Technology: IoT and Smart City, ICIT 2022

作者： Zhang, Xinxin Xu, Li Xu, Zhenyu Sun, Hung-Min Lee, Chia-Wei College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China The Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan

ISBN: (纸本)9781450397438

The most influential nodes are capable of generating maximum influence and widest information spread to their connected nodes in mobile social networks. Hence identifying high influential users plays an important role in monitoring network public opinion. However, influence maximization is an open and challenging issue, and existing works only focus on online social networks but fail to characterize the user social relations and social behavior. In this paper, firstly, we present a novel method based on location and preference relationships to calculate the social influence of users, and the initial nodes are identified by community partition. Secondly, according to the stochastic information spreading process in MSNs, we propose the Fluctuate Particle Swarm Optimization (FPSO)-algorithm to solve the problem of changing communication relationship between users at the optimization stage. Finally, we use extensive experiments on the real datasets to prove the effectiveness of the proposed FPSO-algorithm. The experimental evaluation shows that in the real networks, our proposal achieves better performance than other related methods. © 2022 ACM.

关键词： Social networking (online)

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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computer Modeling in engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2024年第4期9卷 330-339页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui UniversityHefei 230000China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032China School of Computer science School of Cyber Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210000China

In the realm of the Internet of things(IoT),radio frequency identification(RFID)technology is essential for linking physical objects to digital *** introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity(Wi-Fi)***,this trend faces challenges,notably interference from strong Wi-Fi signals,which impacts RFID-based sensing *** paper proposes the WiFID(Wi-Fi and RFID)algorithm,which enables Wi-Fi devices to sense weak RFID harmonic *** subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic *** validation on the universal software radio peripheral(USRP)platform demonstrates that WiFID successfully detects 90% of RFID harmonics at−30 dBm,with only a minimal 4%decrease in Wi-Fi throughput.

关键词： RFID harmonic wireless sensing Wi-Fi interference avoidence

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Revolutionizing FANETs with Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4295-4310页

作者： Mohammed, Yasir I. Hassan, Rosilah Hasan, Mohammad Kamrul Islam, Shayla Abbas, Huda Saleh Khan, Muhammad Asghar Khan, Muhammad Attique Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Department of Computer Science Computer Science and Engineering College Taibah University Madinah Saudi Arabia Department of EE College of Engineering Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges;this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks. © 2020 IEEE.

关键词： Drones

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Towards Demystifying Android Adware: Dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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