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Van, L. X., Dung, L. H., Hoa, D. V..  2020.  Developing Root Problem Aims to Create a Secure Digital Signature Scheme in Data Transfer. 2020 International Conference on Green and Human Information Technology (ICGHIT). :25–30.
This paper presents the proposed method of building a digital signature algorithm which is based on the difficulty of solving root problem and some expanded root problems on Zp. The expanded root problem is a new form of difficult problem without the solution, also originally proposed and applied to build digital signature algorithms. This proposed method enable to build a high-security digital signature platform for practical applications.
Wang, Y., Kjerstad, E., Belisario, B..  2020.  A Dynamic Analysis Security Testing Infrastructure for Internet of Things. 2020 Sixth International Conference on Mobile And Secure Services (MobiSecServ). :1—6.
IoT devices such as Google Home and Amazon Echo provide great convenience to our lives. Many of these IoT devices collect data including Personal Identifiable Information such as names, phone numbers, and addresses and thus IoT security is important. However, conducting security analysis on IoT devices is challenging due to the variety, the volume of the devices, and the special skills required for hardware and software analysis. In this research, we create and demonstrate a dynamic analysis security testing infrastructure for capturing network traffic from IoT devices. The network traffic is automatically mirrored to a server for live traffic monitoring and offline data analysis. Using the dynamic analysis security testing infrastructure, we conduct extensive security analysis on network traffic from Google Home and Amazon Echo. Our testing results indicate that Google Home enforces tighter security controls than Amazon Echo while both Google and Amazon devices provide the desired security level to protect user data in general. The dynamic analysis security testing infrastructure presented in the paper can be utilized to conduct similar security analysis on any IoT devices.
Khandait, P., Hubballi, N., Mazumdar, B..  2020.  Efficient Keyword Matching for Deep Packet Inspection based Network Traffic Classification. 2020 International Conference on COMmunication Systems NETworkS (COMSNETS). :567–570.
Network traffic classification has a range of applications in network management including QoS and security monitoring. Deep Packet Inspection (DPI) is one of the effective method used for traffic classification. DPI is computationally expensive operation involving string matching between payload and application signatures. Existing traffic classification techniques perform multiple scans of payload to classify the application flows - first scan to extract the words and the second scan to match the words with application signatures. In this paper we propose an approach which can classify network flows with single scan of flow payloads using a heuristic method to achieve a sub-linear search complexity. The idea is to scan few initial bytes of payload and determine potential application signature(s) for subsequent signature matching. We perform experiments with a large dataset containing 171873 network flows and show that it has a good classification accuracy of 98%.
Bai, Y., Guo, Y., Wei, J., Lu, L., Wang, R., Wang, Y..  2020.  Fake Generated Painting Detection Via Frequency Analysis. 2020 IEEE International Conference on Image Processing (ICIP). :1256–1260.
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.
Wei, Hanlin, Bai, Guangdong, Luo, Zongwei.  2020.  Foggy: A New Anonymous Communication Architecture Based on Microservices. 2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS). :135–144.
This paper presents Foggy, an anonymous communication system focusing on providing users with anonymous web browsing. Foggy provides a microservice-based proxy for web browsing and other low-latency network activities without exposing users' metadata and browsed content to adversaries. It is designed with decentralized information management, web caching, and configurable service selection. Although Foggy seems to be more centralized compared with Tor, it gains an advantage in manageability while retaining anonymity. Foggy can be deployed by several agencies to become more decentralized. We prototype Foggy and test its performance. Our experiments show Foggy's low latency and deployability, demonstrating its potential to be a commercial solution for real-world deployment.
Chen, W., Cao, H., Lv, X., Cao, Y..  2020.  A Hybrid Feature Extraction Network for Intrusion Detection Based on Global Attention Mechanism. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). :481—485.
The widespread application of 5G will make intrusion detection of large-scale network traffic a mere need. However, traditional intrusion detection cannot meet the requirements by manually extracting features, and the existing AI methods are also relatively inefficient. Therefore, when performing intrusion detection tasks, they have significant disadvantages of high false alarm rates and low recognition performance. For this challenge, this paper proposes a novel hybrid network, RULA-IDS, which can perform intrusion detection tasks by great amount statistical data from the network monitoring system. RULA-IDS consists of the fully connected layer, the feature extraction layer, the global attention mechanism layer and the SVM classification layer. In the feature extraction layer, the residual U-Net and LSTM are used to extract the spatial and temporal features of the network traffic attributes. It is worth noting that we modified the structure of U-Net to suit the intrusion detection task. The global attention mechanism layer is then used to selectively retain important information from a large number of features and focus on those. Finally, the SVM is used as a classifier to output results. The experimental results show that our method outperforms existing state-of-the-art intrusion detection methods, and the accuracies of training and testing are improved to 97.01% and 98.19%, respectively, and presents stronger robustness during training and testing.
Zhang, Chi, Chen, Jinfu, Cai, Saihua, Liu, Bo, Wu, Yiming, Geng, Ye.  2020.  iTES: Integrated Testing and Evaluation System for Software Vulnerability Detection Methods. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1455–1460.
To find software vulnerabilities using software vulnerability detection technology is an important way to ensure the system security. Existing software vulnerability detection methods have some limitations as they can only play a certain role in some specific situations. To accurately analyze and evaluate the existing vulnerability detection methods, an integrated testing and evaluation system (iTES) is designed and implemented in this paper. The main functions of the iTES are:(1) Vulnerability cases with source codes covering common vulnerability types are collected automatically to form a vulnerability cases library; (2) Fourteen methods including static and dynamic vulnerability detection are evaluated in iTES, involving the Windows and Linux platforms; (3) Furthermore, a set of evaluation metrics is designed, including accuracy, false positive rate, utilization efficiency, time cost and resource cost. The final evaluation and test results of iTES have a good guiding significance for the selection of appropriate software vulnerability detection methods or tools according to the actual situation in practice.
Yuliana, Mike, Suwadi, Wirawan.  2020.  Key Rate Enhancement by Using the Interval Approach in Symmetric Key Extraction Mechanism. 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE). :1–6.
Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms.
Li, Yuekang, Chen, Hongxu, Zhang, Cen, Xiong, Siyang, Liu, Chaoyi, Wang, Yi.  2020.  Ori: A Greybox Fuzzer for SOME/IP Protocols in Automotive Ethernet. 2020 27th Asia-Pacific Software Engineering Conference (APSEC). :495—499.
With the emergence of smart automotive devices, the data communication between these devices gains increasing importance. SOME/IP is a light-weight protocol to facilitate inter- process/device communication, which supports both procedural calls and event notifications. Because of its simplicity and capability, SOME/IP is getting adopted by more and more automotive devices. Subsequently, the security of SOME/IP applications becomes crucial. However, previous security testing techniques cannot fit the scenario of vulnerability detection SOME/IP applications due to miscellaneous challenges such as the difficulty of server-side testing programs in parallel, etc. By addressing these challenges, we propose Ori - a greybox fuzzer for SOME/IP applications, which features two key innovations: the attach fuzzing mode and structural mutation. The attach fuzzing mode enables Ori to test server programs efficiently, and the structural mutation allows Ori to generate valid SOME/IP packets to reach deep paths of the target program effectively. Our evaluation shows that Ori can detect vulnerabilities in SOME/IP applications effectively and efficiently.
Fadhilah, D., Marzuki, M. I..  2020.  Performance Analysis of IDS Snort and IDS Suricata with Many-Core Processor in Virtual Machines Against Dos/DDoS Attacks. 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP). :157—162.
The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.
Alshamrani, A..  2020.  Reconnaissance Attack in SDN based Environments. 2020 27th International Conference on Telecommunications (ICT). :1—5.
Software Defined Networking (SDN) is a promising network architecture that aims at providing high flexibility through the separation between network logic (control plane) and forwarding functions (data plane). This separation provides logical centralization of controllers, global network overview, ease of programmability, and a range of new SDN-compliant services. In recent years, the adoption of SDN in enterprise networks has been constantly increasing. In the meantime, new challenges arise in different levels such as scalability, management, and security. In this paper, we elaborate on complex security issues in the current SDN architecture. Especially, reconnaissance attack where attackers generate traffic for the goal of exploring existing services, assets, and overall network topology. To eliminate reconnaissance attack in SDN environment, we propose SDN-based solution by utilizing distributed firewall application, security policy, and OpenFlow counters. Distributed firewall application is capable of tracking the flow based on pre-defined states that would monitor the connection to sensitive nodes toward malicious activity. We utilize Mininet to simulate the testing environment. We are able to detect and mitigate this type of attack at early stage and in average around 7 second.
Adamov, Alexander, Carlsson, Anders.  2020.  Reinforcement Learning for Anti-Ransomware Testing. 2020 IEEE East-West Design Test Symposium (EWDTS). :1–5.
In this paper, we are going to verify the possibility to create a ransomware simulation that will use an arbitrary combination of known tactics and techniques to bypass an anti-malware defense. To verify this hypothesis, we conducted an experiment in which an agent was trained with the help of reinforcement learning to run the ransomware simulator in a way that can bypass anti-ransomware solution and encrypt the target files. The novelty of the proposed method lies in applying reinforcement learning to anti-ransomware testing that may help to identify weaknesses in the anti-ransomware defense and fix them before a real attack happens.
Yao, Xiaoyong, Pei, Yuwen, Wu, Pingdong, Huang, Man-ling.  2020.  Study on Integrative Control between the Stereoscopic Image and the Tactile Feedback in Augmented Reality. 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE). :177—180.
The precise integrative control between the stereoscopic image and the tactile feedback is very essential in augmented reality[1]-[4]. In order to study this question, this paper will introduce a stereoscopic-imaging and tactile integrative augmented-reality system, and a stereoscopic-imaging and tactile integrative algorithm. The system includes a stereoscopic-imaging part and a string-based tactile part. The integrative algorithm is used to precisely control the interaction between the two parts. The results for testing the system and the algorithm demonstrate the system to be perfect through 5 testers' operation and will be presented in the last part of the paper.
Wickramasinghe, C. S., Marino, D. L., Grandio, J., Manic, M..  2020.  Trustworthy AI Development Guidelines for Human System Interaction. 2020 13th International Conference on Human System Interaction (HSI). :130–136.
Artificial Intelligence (AI) is influencing almost all areas of human life. Even though these AI-based systems frequently provide state-of-the-art performance, humans still hesitate to develop, deploy, and use AI systems. The main reason for this is the lack of trust in AI systems caused by the deficiency of transparency of existing AI systems. As a solution, “Trustworthy AI” research area merged with the goal of defining guidelines and frameworks for improving user trust in AI systems, allowing humans to use them without fear. While trust in AI is an active area of research, very little work exists where the focus is to build human trust to improve the interactions between human and AI systems. In this paper, we provide a concise survey on concepts of trustworthy AI. Further, we present trustworthy AI development guidelines for improving the user trust to enhance the interactions between AI systems and humans, that happen during the AI system life cycle.
Jain, Harsh, Vikram, Aditya, Mohana, Kashyap, Ankit, Jain, Ayush.  2020.  Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). :193—198.
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.
Feng, X., Wang, D., Lin, Z., Kuang, X., Zhao, G..  2020.  Enhancing Randomization Entropy of x86-64 Code while Preserving Semantic Consistency. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1–12.

Code randomization is considered as the basis of mitigation against code reuse attacks, fundamentally supporting some recent proposals such as execute-only memory (XOM) that aims at dynamic return-oriented programming (ROP) attacks. However, existing code randomization methods are hard to achieve a good balance between high-randomization entropy and semantic consistency. In particular, they always ignore code semantic consistency, incurring performance loss and incompatibility with current security schemes, e.g., control flow integrity (CFI). In this paper, we present an enhanced code randomization method termed as HCRESC, which can improve the randomization entropy significantly, meanwhile ensure the semantic consistency between variants and the original code. HCRESC reschedules instructions within the range of functions rather than basic blocks, thus producing more variants of the original code and preserving the code's semantic. We implement HCRESC on Linux platform of x86-64 architecture and demonstrate that HCRESC can increase the randomization entropy of x86-64 code over than 120% compared with existing methods while ensuring control flow and size of the code unaltered.

Wang, A., Yuan, Z., He, B..  2020.  Design and Realization of Smart Home Security System Based on AWS. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :291—295.
With the popularization and application of Internet of Things technology, the degree of intelligence of the home system is getting higher and higher. As an important part of the smart home, the security system plays an important role in protecting against accidents such as flammable gas leakage, fire, and burglary that may occur in the home environment. This design focuses on sensor signal acquisition and processing, wireless access, and cloud applications, and integrates Cypress’s new generation of PSoC 6 MCU, CYW4343W Wi-Fi and Bluetooth dual-module chips, and Amazon’s AWS cloud into smart home security System designing. First, through the designed air conditioning and refrigeration module, fire warning processing module, lighting control module, ventilation fan control module, combustible gas and smoke detection and warning module, important parameter information in the home environment is obtained. Then, the hardware system is connected to the AWS cloud platform through Wi-Fi; finally, a WEB interface is built in the AWS cloud to realize remote monitoring of the smart home environment. This design has a good reference for the design of future smart home security systems.
Agirre, I., Onaindia, P., Poggi, T., Yarza, I., Cazorla, F. J., Kosmidis, L., Grüttner, K., Abuteir, M., Loewe, J., Orbegozo, J. M. et al..  2020.  UP2DATE: Safe and secure over-the-air software updates on high-performance mixed-criticality systems. 2020 23rd Euromicro Conference on Digital System Design (DSD). :344–351.
Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity.
Liu, Zhibin, Liu, Ziang, Huang, Yuanyuan, Liu, Xin, Zhou, Xiaokang, Zhou, Rui.  2020.  A Research of Distributed Security and QoS Testing Framework. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :174—181.
Since the birth of the Internet, the quality of network service has been a widespread concerned problem. With the continuous development of communication and information technology, people gradually realized that the contradiction between the limited resources and the business requirements of network cannot be fundamentally solved. In this paper, we design and develop a distributed security quality of service testing framework called AweQoS(AwesomeQoS), to adapt to the current complex network environment. This paper puts forward the necessity that some security tests should be closely combined with quality of service testing, and further discusses the basic methods of distributed denial of service attack and defense. We introduce the design idea and working process of AweQoS in detail, and introduce a bandwidth test method based on user datagram protocol. Experimental results show that this new test method has better test performance and potential under the AweQoS framework.
Sardar, Muhammad Usama, Quoc, Do Le, Fetzer, Christof.  2020.  Towards Formalization of Enhanced Privacy ID (EPID)-based Remote Attestation in Intel SGX. 2020 23rd Euromicro Conference on Digital System Design (DSD). :604—607.

Vulnerabilities in privileged software layers have been exploited with severe consequences. Recently, Trusted Execution Environments (TEEs) based technologies have emerged as a promising approach since they claim strong confidentiality and integrity guarantees regardless of the trustworthiness of the underlying system software. In this paper, we consider one of the most prominent TEE technologies, referred to as Intel Software Guard Extensions (SGX). Despite many formal approaches, there is still a lack of formal proof of some critical processes of Intel SGX, such as remote attestation. To fill this gap, we propose a fully automated, rigorous, and sound formal approach to specify and verify the Enhanced Privacy ID (EPID)-based remote attestation in Intel SGX under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. The evaluation indicates that the confidentiality of attestation keys is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX attestation during formal specification.

Chen, Haibo, Chen, Junzuo, Chen, Jinfu, Yin, Shang, Wu, Yiming, Xu, Jiaping.  2020.  An Automatic Vulnerability Scanner for Web Applications. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1519–1524.
With the progressive development of web applications and the urgent requirement of web security, vulnerability scanner has been particularly emphasized, which is regarded as a fundamental component for web security assurance. Various scanners are developed with the intention of that discovering the possible vulnerabilities in advance to avoid malicious attacks. However, most of them only focus on the vulnerability detection with single target, which fail in satisfying the efficiency demand of users. In this paper, an effective web vulnerability scanner that integrates the information collection with the vulnerability detection is proposed to verify whether the target web application is vulnerable or not. The experimental results show that, by guiding the detection process with the useful collected information, our tool achieves great web vulnerability detection capability with a large scanning scope.
Zarzour, Hafed, Al shboul, Bashar, Al-Ayyoub, Mahmoud, Jararweh, Yaser.  2020.  A convolutional neural network-based reviews classification method for explainable recommendations. 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). :1–5.
Recent advances in information filtering have resulted in effective recommender systems that are able to provide online personalized recommendations to millions of users from all over the world. However, most of these systems ignore the explanation purpose while producing recommendations with high-quality results. Moreover, the classification of reviews given to users as explanations is not fully exploited in previous studies. In this paper, we develop a convolutional neural network-based reviews classification method for explainable recommendation systems. The convolutional neural network is used to extract the reviews features for predicting whether the reviews provided as explanations are positive or negative. Based on such additional information, users can understand not only why certain items are recommended for them but also get support to know the nature of such explanations. We conduct experiments on a dataset from Amazon. The experimental results show that our method outperforms state-of-the-art methods.
Huo, T., Wang, W., Zhao, P., Li, Y., Wang, T., Li, M..  2020.  TEADS: A Defense-Aware Framework for Synthesizing Transient Execution Attacks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :320—327.

Since 2018, a broad class of microarchitectural attacks called transient execution attacks (e.g., Spectre and Meltdown) have been disclosed. By abusing speculative execution mechanisms in modern CPUs, these attacks enable adversaries to leak secrets across security boundaries. A transient execution attack typically evolves through multiple stages, termed the attack chain. We find that current transient execution attacks usually rely on static attack chains, resulting in that any blockage in an attack chain may cause the failure of the entire attack. In this paper, we propose a novel defense-aware framework, called TEADS, for synthesizing transient execution attacks dynamically. The main idea of TEADS is that: each attacking stage in a transient execution attack chain can be implemented in several ways, and the implementations used in different attacking stages can be combined together under certain constraints. By constructing an attacking graph representing combination relationships between the implementations and testing available paths in the attacking graph dynamically, we can finally synthesize transient execution attacks which can bypass the imposed defense techniques. Our contributions include: (1) proposing an automated defense-aware framework for synthesizing transient execution attacks, even though possible combinations of defense strategies are enabled; (2) presenting an attacking graph extension algorithm to detect potential attack chains dynamically; (3) implementing TEADS and testing it on several modern CPUs with different protection settings. Experimental results show that TEADS can bypass the defenses equipped, improving the adaptability and durability of transient execution attacks.

Ullah, A., Chen, X., Yang, J..  2020.  Design and Implementation of MobilityFirst Future Internet Testbed. 2020 3rd International Conference on Hot Information-Centric Networking (HotICN). :170—174.

Recently, Future Internet research has attracted enormous attentions towards the design of clean slate Future Internet Architecture. A large number of research projects has been established by National Science Foundation's (NSF), Future Internet Architecture (FIA) program in this area. One of these projects is MobilityFirst, which recognizes the predominance of mobile networking and aims to address the challenges of this paradigm shift. Future Internet Architecture Projects, are usually deploying on large scale experimental networks for testing and evaluating the properties of new architecture and protocols. Currently only some specific experiments, like routing and name resolution scalability in MobilityFirst architecture has been performed over the ORBIT and GENI platforms. However, to move from this experimental networking to technology trials with real-world users and applications deployment of alternative testbeds are necessary. In this paper, MobilityFirst Future Internet testbed is designed and deployed on Future Networks Laboratory, University of Science and Technology of China, China. Which provides a realistic environment for MobilityFirst experiments. Next, in this paper, for MF traffic transmission between MobilityFirst networks through current networking protocols (TCP), MobilityFirst Proxies are designed and implemented. Furthermore, the results and experience obtained from experiments over proposed testbed are presented.

Susilo, Willy, Duong, Dung Hoang, Le, Huy Quoc.  2020.  Efficient Post-quantum Identity-based Encryption with Equality Test. 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). :633—640.
Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.