Biblio

Found 439 results

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2021-09-07
Ahmed, Faruk, Mahmud, Md Sultan, Yeasin, Mohammed.  2020.  Assistive System for Navigating Complex Realistic Simulated World Using Reinforcement Learning. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
Finding a free path without obstacles or situation that pose minimal risk is critical for safe navigation. People who are sighted and people who are blind or visually impaired require navigation safety while walking on a sidewalk. In this paper we develop assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We train the reinforcement model in a simulated robotic environment which is used to avoid sidewalk obstacles. A conversational agent is built by training with real conversation data. The reinforcement learning model along with a conversational agent improved the obstacle avoidance experience about 2.5% from the base case which is 78.75%.
2021-06-01
Xu, Lei, Gao, Zhimin, Fan, Xinxin, Chen, Lin, Kim, Hanyee, Suh, Taeweon, Shi, Weidong.  2020.  Blockchain Based End-to-End Tracking System for Distributed IoT Intelligence Application Security Enhancement. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1028–1035.
IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.
2021-06-24
Połap, Dawid, Srivastava, Gautam, Jolfaei, Alireza, Parizi, Reza M..  2020.  Blockchain Technology and Neural Networks for the Internet of Medical Things. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :508–513.
In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
2021-02-23
Shah, A., Clachar, S., Minimair, M., Cook, D..  2020.  Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). :759—760.
This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunneling Obfuscations dataset. The dataset captured legitimate and obfuscated malicious TCP communications on selected vulnerable network services. The multiclass classification NIDS is able to distinguish obfuscated and direct network intrusion with up to 95% accuracy.
2021-09-21
Barr, Joseph R., Shaw, Peter, Abu-Khzam, Faisal N., Yu, Sheng, Yin, Heng, Thatcher, Tyler.  2020.  Combinatorial Code Classification Amp; Vulnerability Rating. 2020 Second International Conference on Transdisciplinary AI (TransAI). :80–83.
Empirical analysis of source code of Android Fluoride Bluetooth stack demonstrates a novel approach of classification of source code and rating for vulnerability. A workflow that combines deep learning and combinatorial techniques with a straightforward random forest regression is presented. Two kinds of embedding are used: code2vec and LSTM, resulting in a distance matrix that is interpreted as a (combinatorial) graph whose vertices represent code components, functions and methods. Cluster Editing is then applied to partition the vertex set of the graph into subsets representing nearly complete subgraphs. Finally, the vectors representing the components are used as features to model the components for vulnerability risk.
2021-01-22
Mani, G., Pasumarti, V., Bhargava, B., Vora, F. T., MacDonald, J., King, J., Kobes, J..  2020.  DeCrypto Pro: Deep Learning Based Cryptomining Malware Detection Using Performance Counters. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). :109—118.
Autonomy in cybersystems depends on their ability to be self-aware by understanding the intent of services and applications that are running on those systems. In case of mission-critical cybersystems that are deployed in dynamic and unpredictable environments, the newly integrated unknown applications or services can either be benign and essential for the mission or they can be cyberattacks. In some cases, these cyberattacks are evasive Advanced Persistent Threats (APTs) where the attackers remain undetected for reconnaissance in order to ascertain system features for an attack e.g. Trojan Laziok. In other cases, the attackers can use the system only for computing e.g. cryptomining malware. APTs such as cryptomining malware neither disrupt normal system functionalities nor trigger any warning signs because they simply perform bitwise and cryptographic operations as any other benign compression or encoding application. Thus, it is difficult for defense mechanisms such as antivirus applications to detect these attacks. In this paper, we propose an Operating Context profiling system based on deep neural networks-Long Short-Term Memory (LSTM) networks-using Windows Performance Counters data for detecting these evasive cryptomining applications. In addition, we propose Deep Cryptomining Profiler (DeCrypto Pro), a detection system with a novel model selection framework containing a utility function that can select a classification model for behavior profiling from both the light-weight machine learning models (Random Forest and k-Nearest Neighbors) and a deep learning model (LSTM), depending on available computing resources. Given data from performance counters, we show that individual models perform with high accuracy and can be trained with limited training data. We also show that the DeCrypto Profiler framework reduces the use of computational resources and accurately detects cryptomining applications by selecting an appropriate model, given the constraints such as data sample size and system configuration.
2021-03-29
Moti, Z., Hashemi, S., Jahromi, A. N..  2020.  A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :48–53.
Nowadays, with the increasing use of computers and the Internet, more people are exposed to cyber-security dangers. According to antivirus companies, malware is one of the most common threats of using the Internet. Therefore, providing a practical solution is critical. Current methods use machine learning approaches to classify malware samples automatically. Despite the success of these approaches, the accuracy and efficiency of these techniques are still inadequate, especially for multiple class classification problems and imbalanced training data sets. To mitigate this problem, we use deep learning-based algorithms for classification and generation of new malware samples. Our model is based on the opcode sequences, which are given to the model without any pre-processing. Besides, we use a novel generative adversarial network to generate new opcode sequences for oversampling minority classes. Also, we propose the model that is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to classify malware samples. CNN is used to consider short-term dependency between features; while, LSTM is used to consider longer-term dependence. The experiment results show our method could classify malware to their corresponding family effectively. Our model achieves 98.99% validation accuracy.
2021-08-11
Nan, Satyaki, Brahma, Swastik, Kamhoua, Charles A., Njilla, Laurent L..  2020.  On Development of a Game‐Theoretic Model for Deception‐Based Security. Modeling and Design of Secure Internet of Things. :123–140.
This chapter presents a game‐theoretic model to analyze attack–defense scenarios that use fake nodes (computing devices) for deception under consideration of the system deploying defense resources to protect individual nodes in a cost‐effective manner. The developed model has important applications in the Internet of Battlefield Things (IoBT). Our game‐theoretic model illustrates how the concept of the Nash equilibrium can be used by the defender to intelligently choose which nodes should be used for performing a computation task while deceiving the attacker into expending resources for attacking fake nodes. Our model considers the fact that defense resources may become compromised under an attack and suggests that the defender, in a probabilistic manner, may utilize unprotected nodes for performing a computation while the attacker is deceived into attacking a node with defense resources installed. The chapter also presents a deception‐based strategy to protect a target node that can be accessed via a tree network. Numerical results provide insights into the strategic deception techniques presented in this chapter.
2021-04-09
Ravikumar, G., Singh, A., Babu, J. R., A, A. Moataz, Govindarasu, M..  2020.  D-IDS for Cyber-Physical DER Modbus System - Architecture, Modeling, Testbed-based Evaluation. 2020 Resilience Week (RWS). :153—159.
Increasing penetration of distributed energy resources (DERs) in distribution networks expands the cyberattack surface. Moreover, the widely used standard protocols for communicating DER inverters such as Modbus is more vulnerable to data-integrity attacks and denial of service (DoS) attacks because of its native clear-text packet format. This paper proposes a distributed intrusion detection system (D-IDS) architecture and algorithms for detecting anomalies on the DER Modbus communication. We devised a model-based approach to define physics-based threshold bands for analog data points and transaction-based threshold bands for both the analog and discrete data points. The proposed IDS algorithm uses the model- based approach to develop Modbus-specific IDS rule sets, which can enhance the detection accuracy of the anomalies either by data-integrity attacks or maloperation on cyber-physical DER Modbus devices. Further, the IDS algorithm autogenerates the Modbus-specific IDS rulesets in compliance with various open- source IDS rule syntax formats, such as Snort and Suricata, for seamless integration and mitigation of semantic/syntax errors in the development and production environment. We considered the IEEE 13-bus distribution grid, including DERs, as a case study. We conducted various DoS type attacks and data-integrity attacks on the hardware-in-the-loop (HIL) CPS DER testbed at ISU to evaluate the proposed D-IDS. Consequently, we computed the performance metrics such as IDS detection accuracy, IDS detection rate, and end-to-end latency. The results demonstrated that 100% detection accuracy, 100% detection rate for 60k DoS packets, 99.96% detection rate for 80k DoS packets, and 0.25 ms end-to-end latency between DERs to Control Center.
2021-10-04
Zhang, Chong, Liu, Xiao, Zheng, Xi, Li, Rui, Liu, Huai.  2020.  FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1–4.
Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.
2021-04-27
Uthayashangar, S., Abinaya, J., Harshini, V., Jayavardhani, R..  2020.  Image And Text Encrypted Data With Authorized Deduplication In Cloud. 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). :1—5.
In this paper, the role re-encryption is used to avoid the privacy data lekage and also to avoid the deduplication in a secure role re-encryption system(SRRS). And also it checks for the proof of ownership for to identify whether the user is authorized user or not. This is for the efficiency. Role re-encrytion method is to share the access key for the corresponding authorized user for accessing the particular file without the leakage of privacy data. In our project we are using both the avoidance of text and digital images. For example we have the personal images in our mobile, handheld devices, and in the desktop etc., So, as these images have to keep secure and so we are using the encryption for to increase the high security. The text file also important for the users now-a-days. It has to keep secure in a cloud server. Digital images have to be protected over the communication, however generally personal identification details like copies of pan card, Passport, ATM, etc., to store on one's own pc. So, we are protecting the text file and image data for avoiding the duplication in our proposed system.
2021-03-01
Shi, W., Liu, S., Zhang, J., Zhang, R..  2020.  A Location-aware Computation Offloading Policy for MEC-assisted Wireless Mesh Network. 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). :53–58.
Mobile edge computing (MEC), an emerging technology, has the characteristics of low latency, mobile energy savings, and context-awareness. As a type of access network, wireless mesh network (WMN) has gained wide attention due to its flexible network architecture, low deployment cost, and self-organization. The combination of MEC and WMN can solve the shortcomings of traditional wireless communication such as storage capacity, privacy, and security. In this paper, we propose a location-aware (LA) algorithm to cognize the location and a location-aware offloading policy (LAOP) algorithm considering the energy consumption and time delay. Simulation results show that the proposed LAOP algorithm can obtain a higher completion rate and lower average processing delay compared with the other two methods.
2021-09-21
Zhao, Quanling, Sun, Jiawei, Ren, Hongjia, Sun, Guodong.  2020.  Machine-Learning Based TCP Security Action Prediction. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :1329–1333.
With the continuous growth of Internet technology and the increasingly broadening applications of The Internet, network security incidents as well as cyber-attacks are also showing a growing trend. Consequently, computer network security is becoming increasingly important. TCP firewall is a computer network security system, and it allows or denies the transmission of data according to specific rules for providing security for the computer network. Traditional firewalls rely on network administrators to set security rules for them, and network administrators sometimes need to choose to allow and deny packets to keep computer networks secure. However, due to the huge amount of data on the Internet, network administrators have a huge task. Therefore, it is particularly important to solve this problem by using the machine learning method of computer technology. This study aims to predict TCP security action based on the TCP transmission characteristics dataset provided by UCI machine learning repository by implementing machine learning models such as neural network, support vector machine (SVM), AdaBoost, and Logistic regression. Processes including evaluating various models and interpretability analysis. By utilizing the idea of ensemble-learning, the final result has an accuracy score of over 98%.
2021-03-01
Davis, B., Glenski, M., Sealy, W., Arendt, D..  2020.  Measure Utility, Gain Trust: Practical Advice for XAI Researchers. 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX). :1–8.
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.
2021-02-08
Pelissero, N., Laso, P. M., Puentes, J..  2020.  Naval cyber-physical anomaly propagation analysis based on a quality assessed graph. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
As any other infrastructure relying on cyber-physical systems (CPS), naval CPS are highly interconnected and collect considerable data streams, on which depend multiple command and navigation decisions. Being a data-driven decision system requiring optimized supervisory control on a permanent basis, it is critical to examine the CPS vulnerability to anomalies and their propagation. This paper presents an approach to detect CPS anomalies and estimate their propagation applying a quality assessed graph, which represents the CPS physical and digital subsystems, combined with system variables dependencies and a set of data and information quality measures vectors. Following the identification of variables dependencies and high-risk nodes in the CPS, data and information quality measures reveal how system variables are modified when an anomaly is detected, also indicating its propagation path. Taking as reference the normal state of a naval propulsion management system, four anomalies in the form of cyber-attacks - port scan, programmable logical controller stop, and man in the middle to change the motor speed and operation of a tank valve - were produced. Three anomalies were properly detected and their propagation path identified. These results suggest the feasibility of anomaly detection and estimation of propagation estimation in CPS, applying data and information quality analysis to a system graph.
2021-07-27
Xiao, Wenli, Jiang, Hao, Xia, Song.  2020.  A New Black Box Attack Generating Adversarial Examples Based on Reinforcement Learning. 2020 Information Communication Technologies Conference (ICTC). :141–146.
Machine learning can be misled by adversarial examples, which is formed by making small changes to the original data. Nowadays, there are kinds of methods to produce adversarial examples. However, they can not apply non-differentiable models, reduce the amount of calculations, and shorten the sample generation time at the same time. In this paper, we propose a new black box attack generating adversarial examples based on reinforcement learning. By using deep Q-learning network, we can train the substitute model and generate adversarial examples at the same time. Experimental results show that this method only needs 7.7ms to produce an adversarial example, which solves the problems of low efficiency, large amount of calculation and inapplicable to non-differentiable model.
2021-09-30
Titouna, Chafiq, Na\"ıt-Abdesselam, Farid, Moungla, Hassine.  2020.  An Online Anomaly Detection Approach For Unmanned Aerial Vehicles. 2020 International Wireless Communications and Mobile Computing (IWCMC). :469–474.
A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.
2021-10-12
Zhong, Zhenyu, Hu, Zhisheng, Chen, Xiaowei.  2020.  Quantifying DNN Model Robustness to the Real-World Threats. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :150–157.
DNN models have suffered from adversarial example attacks, which lead to inconsistent prediction results. As opposed to the gradient-based attack, which assumes white-box access to the model by the attacker, we focus on more realistic input perturbations from the real-world and their actual impact on the model robustness without any presence of the attackers. In this work, we promote a standardized framework to quantify the robustness against real-world threats. It is composed of a set of safety properties associated with common violations, a group of metrics to measure the minimal perturbation that causes the offense, and various criteria that reflect different aspects of the model robustness. By revealing comparison results through this framework among 13 pre-trained ImageNet classifiers, three state-of-the-art object detectors, and three cloud-based content moderators, we deliver the status quo of the real-world model robustness. Beyond that, we provide robustness benchmarking datasets for the community.
2021-01-22
Sahabandu, D., Allen, J., Moothedath, S., Bushnell, L., Lee, W., Poovendran, R..  2020.  Quickest Detection of Advanced Persistent Threats: A Semi-Markov Game Approach. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :9—19.
Advanced Persistent Threats (APTs) are stealthy, sophisticated, long-term, multi-stage attacks that threaten the security of sensitive information. Dynamic Information Flow Tracking (DIFT) has been proposed as a promising mechanism to detect and prevent various cyber attacks in computer systems. DIFT tracks suspicious information flows in the system and generates security analysis when anomalous behavior is detected. The number of information flows in a system is typically large and the amount of resources (such as memory, processing power and storage) required for analyzing different flows at different system locations varies. Hence, efficient use of resources is essential to maintain an acceptable level of system performance when using DIFT. On the other hand, the quickest detection of APTs is crucial as APTs are persistent and the damage caused to the system is more when the attacker spends more time in the system. We address the problem of detecting APTs and model the trade-off between resource efficiency and quickest detection of APTs. We propose a game model that captures the interaction of APT and a DIFT-based defender as a two-player, multi-stage, zero-sum, Stackelberg semi-Markov game. Our game considers the performance parameters such as false-negatives generated by DIFT and the time required for executing various operations in the system. We propose a two-time scale Q-learning algorithm that converges to a Stackelberg equilibrium under infinite horizon, limiting average payoff criteria. We validate our model and algorithm on a real-word attack dataset obtained using Refinable Attack INvestigation (RAIN) framework.
2021-02-22
Alzakari, N., Dris, A. B., Alahmadi, S..  2020.  Randomized Least Frequently Used Cache Replacement Strategy for Named Data Networking. 2020 3rd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
To accommodate the rapidly changing Internet requirements, Information-Centric Networking (ICN) was recently introduced as a promising architecture for the future Internet. One of the ICN primary features is `in-network caching'; due to its ability to minimize network traffic and respond faster to users' requests. Therefore, various caching algorithms have been presented that aim to enhance the network performance using different measures, such as cache hit ratio and cache hit distance. Choosing a caching strategy is critical, and an adequate replacement strategy is also required to decide which content should be dropped. Thus, in this paper, we propose a content replacement scheme for ICN, called Randomized LFU that is implemented with respect to content popularity taking the time complexity into account. We use Abilene and Tree network topologies in our simulation models. The proposed replacement achieves encouraging results in terms of the cache hit ratio, inner hit, and hit distance and it outperforms FIFO, LRU, and Random replacement strategies.
2021-03-01
Taylor, E., Shekhar, S., Taylor, G. W..  2020.  Response Time Analysis for Explainability of Visual Processing in CNNs. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :1555–1558.
Explainable artificial intelligence (XAI) methods rely on access to model architecture and parameters that is not always feasible for most users, practitioners, and regulators. Inspired by cognitive psychology, we present a case for response times (RTs) as a technique for XAI. RTs are observable without access to the model. Moreover, dynamic inference models performing conditional computation generate variable RTs for visual learning tasks depending on hierarchical representations. We show that MSDNet, a conditional computation model with early-exit architecture, exhibits slower RT for images with more complex features in the ObjectNet test set, as well as the human phenomenon of scene grammar, where object recognition depends on intrascene object-object relationships. These results cast light on MSDNet's feature space without opening the black box and illustrate the promise of RT methods for XAI.
2021-06-01
Englund, Håkan, Lindskog, Niklas.  2020.  Secure acceleration on cloud-based FPGAs – FPGA enclaves. 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). :119—122.
FPGAs are becoming a common sight in cloud environments and new usage paradigms, such as FPGA-as-a-Service, have emerged. This development poses a challenge to traditional FPGA security models, as these are assuming trust between the user and the hardware owner. Currently, the user cannot keep bitstream nor data protected from the hardware owner in an FPGA-as-a-service setting. This paper proposes a security model where the chip manufacturer takes the role of root-of-trust to remedy these security problems. We suggest that the chip manufacturer creates a Public Key Infrastructure (PKI), used for user bitstream protection and data encryption, on each device. The chip manufacturer, rather than the hardware owner, also controls certain security-related peripherals. This allows the user to take control over a predefined part of the programmable logic and set up a protected enclave area. Hence, all user data can be provided in encrypted form and only be revealed inside the enclave area. In addition, our model enables secure and concurrent multi-tenant usage of remote FPGAs. To also consider the needs of the hardware owner, our solution includes bitstream certification and affirming that uploaded bitstreams have been vetted against maliciousness.
2021-09-16
Yang, Xiaodong, Liu, Rui, Chen, Guilan, Wang, Meiding, Wang, Caifen.  2020.  Security Analysis of a Certificateless Signcryption Mechanism without Bilinear Mapping. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2431–2434.
Certificateless signcryption mechanism can not only provide security services, such as message integrity, non-repudiation and confidentiality, but also solve the problems of public key certificate management and key escrow. Zhou et al. proposed a certificateless signcryption mechanism without bilinear mapping and gave its security proof under the discrete logarithm problem and the computational Diffie Hellman problem in the random oracle model. However, the analysis show that this scheme has security flaws. That is, attackers can forge legitimate signatures of any messages. Finally, we give the specific attack process.
2021-03-22
Larasati, H. T., Kim, H..  2020.  Simulation of Modular Exponentiation Circuit for Shor's Algorithm in Qiskit. 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA. :1–7.
This paper discusses and demonstrates the construction of a quantum modular exponentiation circuit in the Qiskit simulator for use in Shor's Algorithm for integer factorization problem (IFP), which is deemed to be able to crack RSA cryptosystems when a large-qubit quantum computer exists. We base our implementation on Vedral, Barenco, and Ekert (VBE) proposal of quantum modular exponentiation, one of the firsts to explicitly provide the aforementioned circuit. Furthermore, we present an example simulation of how to construct a 7xmod 15 circuit in a step-by-step manner, giving clear and detailed information and consideration that currently not provided in the existing literature, and present the whole circuit for use in Shor's Algorithm. Our present simulation shows that the 4-bit VBE quantum modular exponentiation circuit can be constructed, simulated, and measured in Qiskit, while the Shor's Algorithm incorporating this VBE approach itself can be constructed but not yet simulated due to an overly large number of QASM instructions.
2021-10-12
Musleh, Ahmed S., Chen, Guo, Dong, Zhao Yang, Wang, Chen, Chen, Shiping.  2020.  Statistical Techniques-Based Characterization of FDIA in Smart Grids Considering Grid Contingencies. 2020 International Conference on Smart Grids and Energy Systems (SGES). :83–88.
False data injection attack (FDIA) is a real threat to smart grids due to its wide range of vulnerabilities and impacts. Designing a proper detection scheme for FDIA is the 1stcritical step in defending the attack in smart grids. In this paper, we investigate two main statistical techniques-based approaches in this regard. The first is based on the principal component analysis (PCA), and the second is based on the canonical correlation analysis (CCA). The test cases illustrate a better characterization performance of FDIA using CCA compared to the PCA. Further, CCA provides a better differentiation of FDIA from normal grid contingencies. On the other hand, PCA provides a significantly reduced false alarm rate.