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Automatic APT Attack Reconstruction Supporting Lateral Movement 2nd

Automatic APT Attack Reconstruction Supporting Lateral Movem...

2nd International Conference on security and information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Fan, Chun-I Wu, Wei-Chen Shie, Cheng-Han Kuo, Hsin-Nan Lee, Bo-Yi Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan National Center for High-Performance Computing Hsinchu Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9789819777853

This work proposes a framework for generating datasets that allows users to adjust the APT attack techniques within it. The framework utilizes the MITRE ATT&CK framework to label the attack traffic based on the Tactics, Techniques, and Procedures (TTP), which facilitating researchers in using the datasets for model training. Meanwhile, this work uses the framework to reconstruct the APT29 attack process and collect a dataset to validate the framework’s feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Advanced Persistent Threat (APT) Dataset generation The APT29 attack Threat intelligence

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InvMetrics: Measuring Privacy Risks for Split Model-Based Customer Behavior Analysis

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4168-4177页

作者： Deng, Ruijun Hu, Shijing Lin, Junxiong Yang, Jirui Lu, Zhihui Wu, Jie Huang, Shih-Chia Duan, Qiang Fudan University Sch. of Comp. Science and the Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China Fudan University School of Computer Science Shanghai200433 China Shanghai Blockchain Engineering Research Center Shanghai200433 China National Taipei University of Technology Department of Electronic Engineering Taipei City10608 Taiwan Pennsylvania State University Information Sciences and Technology Department AbingtonPA19001 United States

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC, partitions the CBA models between the customer device and the edge server in a layer-wise manner to support efficient distributed learning. However, the split-model architecture (SMA) is vulnerable to data reconstruction attacks for privacy leakage in intermediate data, of which the risk measurements remain unexplored. In this paper, we propose a privacy risk measurement framework, called InvMetrics, for split model-based CBA systems, which assess the degree of privacy leakage from both the CBA owners' and the regulators' perspectives. For CBA owners, we propose a privacy metric, Distance Loss (DLoss), based on distance correlation, which is computationally efficient, and thus eligible for being deployed on the customers' devices. For third-party evaluators, we propose Uncertainty Loss (ULoss) based on entropy, which can measure privacy risk without accessing raw data. Evaluation results on three CBA datasets and one image dataset demonstrate that InvMetrics framework with DLoss and ULoss can accurately measure privacy risk and is more efficient than the state-of-the-art. © 1975-2011 IEEE.

关键词： Data privacy

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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POFM: Profit-Oriented Flow Management in SDN-Based Data center Networks

POFM: Profit-Oriented Flow Management in SDN-Based Data Cent...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Wang, You-Chiun Liang, Jhong-Ting Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9798350353464

In a data center network (DCN), there are numerous hosts connected by well-organized switches, usually involving a large amount of data transmission to offer cloud services. How to manage flows in a DCN by deciding packet routes is critical. This paper applies the software-defined networking (SDN) technique to flow management in fat-tree DCNs. Unlike previous studies, we consider that flows have different values and associate flows with profits to reflect their values. The practical profit acquired from each flow will depend on the proportion of successfully sent packets. The paper proposes a profit-oriented flow management (POFM) scheme to maximize the profit obtained while reducing packet loss in flows. In POFM, once a link becomes congested, some flows on that link may be rerouted to other paths. When no alternative path can be found, POFM adjusts the data rates of flows on the congested link based on multiple factors, including their demanded rates, profits, and transmission durations. With simulations, we show that POFM can effectively raise the overall profit and decrease the packet loss rate in the DCN. ©2024 IEEE.

关键词： Packet loss

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Efficient Convolutional Neural Network Based Side Channel Attacks Based on AES Cryptography 21

Efficient Convolutional Neural Network Based Side Channel At...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Aman, Azana Hafizah Abdulkadir, Rabiu Aliyu Islam, Shayla Farhan, Basim Abdulkareem Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350318821

In recent years, profiled side-channel attacks have emerged as a particularly potent kind of side-channel attack that can circumvent cryptographic equipment's security. Convolutional neural networks (CNNs) have been widely used as deep learning infrastructure for attacks in recent research that has examined a new kind of profiled attack based on deep learning. The design of CNNs will have a significant impact on attack effectiveness. However, image recognition fields are frequently the foundation of the CNN architecture currently used for profiled attacks. Additionally, it is still challenging to choose the proper parameters and CNN infrastructure concerning adaptation to profiled assault types. In the current study, an effective CNN-based profiled attack was suggested that can be used against masking-protected cryptographic devices. The Grey Wolf Optimization (GWO) approach obtains the CNN architecture parameters proposed in this research. The characteristics of intriguing areas on the power trace determine these parameters. The proposed attacks were tested using experiments over a tracing set gathered from an intelligent card Atmega8515 processing the ASCAD public dataset. © 2023 IEEE.

关键词： Side channel attack

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Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Reception and Signal Decoding 2

Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Recep...

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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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Speed-Aware Flow Management with Packet Classification to Mitigate Congestion in VANETs

Speed-Aware Flow Management with Packet Classification to Mi...

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2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

作者： Wang, You-Chiun Ho, Cheng-En National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan National Sun Yat-sen University Information Security Research Center Kaohsiung Taiwan

ISBN: (纸本)9798350316803

Due to service diversity, there may be many packet flows with different urgency and importance in a vehicular ad hoc network (VANET). In a traffic jam, numerous vehicles vie to send packets, which leads to network congestion. Moreover, when accidents occur, the vehicles detecting them will broadcast safety-critical messages to warn others, which could worsen congestion and thus cause the loss of many messages. To this end, the paper proposes a speed-aware flow management with packet classification (SFM-PC) scheme. In SFM-PC, packets will be classified by their services. According to its speed, each vehicle gauges the traffic conditions locally and adjusts the transmission rate of packets by itself. On receiving the packets of safety-critical messages, vehicles use packet consolidation to reduce the dissemination of duplicate messages. Through simulations, we demonstrate that the SFM-PC scheme can efficiently mitigate congestion in a VANET, where it has a much lower packet loss rate than other methods. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Adaptive Countermeasure by Credibility to Defend SDN Controllers Against BlackNurse-SC Attacks 12

Adaptive Countermeasure by Credibility to Defend SDN Control...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Wang, You-Chiun Wang, Pin-Yuan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan National Sun Yat-sen University Information Security Research Center Kaohsiung Taiwan

ISBN: (纸本)9798350341102

Software-defined networking (SDN) facilitates network management by using a controller to monitor the network status and regulate switches. Owing to its central control nature, the controller often becomes the target of attacks. BlackNurse-SC, an emerging type of DDoS attack, drains the controller of its computing resources via ICMP error messages and causes a disruption in the SDN network. To defend the controller against BlackNurse-SC attacks, we propose an adaptive countermeasure by credibility (ACC) that checks if hosts have suspected attacking behavior and assesses their credibility. Low-credibility hosts will have their ICMP packets blocked as a penalty, and the penalty duration is adjusted depending on the degree of credibility. In addition, the credibility of a host can be restored when it doesn't send ICMP error messages for a while. Simulation results reveal that the ACC scheme can protect a controller from BlackNurse-SC attacks effectively. © 2023 IEEE.

关键词： Controllers

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang Xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

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Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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