Biblio

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2021-08-11
Xi, Bowei, Kamhoua, Charles A..  2020.  A Hypergame‐Based Defense Strategy Toward Cyber Deception in Internet of Battlefield Things (IoBT). Modeling and Design of Secure Internet of Things. :59–77.
In this chapter, we develop a defense strategy to secure Internet of Battlefield Things (IoBT) based on a hypergame employing deceptive techniques. The hypergame is played multiple rounds. At each round, the adversary updates its perception of the attack graph and chooses the next node to compromise. The defender updates its perceived list of compromised nodes and actively feeds false signals to the adversary to create deception. The hypergame developed in this chapter provides an important theoretical framework for us to model how a cyberattack spreads on a network and the interaction between the adversary and the defender. It also provides quantitative metrics such as the time it takes the adversary to explore the network and compromise the target nodes. Based on these metrics, the defender can reboot the network devices and reset the network topology in time to clean up all potentially compromised devices and to protect the critical nodes. The hypergame provides useful guidance on how to create cyber deceptions so that the adversary cannot obtain information about the correct network topology and can be deterred from reaching the target critical nodes on a military network while it is in service.
2021-08-31
Fadolalkarim, Daren, Bertino, Elisa, Sallam, Asmaa.  2020.  An Anomaly Detection System for the Protection of Relational Database Systems against Data Leakage by Application Programs. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :265—276.
Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.
2021-04-08
Bouzar-Benlabiod, L., Rubin, S. H., Belaidi, K., Haddar, N. E..  2020.  RNN-VED for Reducing False Positive Alerts in Host-based Anomaly Detection Systems. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :17–24.
Host-based Intrusion Detection Systems HIDS are often based on anomaly detection. Several studies deal with anomaly detection by analyzing the system-call traces and get good detection rates but also a high rate off alse positives. In this paper, we propose a new anomaly detection approach applied on the system-call traces. The normal behavior learning is done using a Sequence to sequence model based on a Variational Encoder-Decoder (VED) architecture that integrates Recurrent Neural Networks (RNN) cells. We exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to structure and optimize the model input-data representation. After the learning step, a one-class classification is run to categorize the sequences as normal or abnormal. The architecture may be used for predicting abnormal behaviors. The tests are achieved on the ADFA-LD dataset.
2021-02-03
Xu, J., Howard, A..  2020.  Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :814—820.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

2021-04-08
Xingjie, F., Guogenp, W., ShiBIN, Z., ChenHAO.  2020.  Industrial Control System Intrusion Detection Model based on LSTM Attack Tree. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :255–260.
With the rapid development of the Industrial Internet, the network security risks faced by industrial control systems (ICSs) are becoming more and more intense. How to do a good job in the security protection of industrial control systems is extremely urgent. For traditional network security, industrial control systems have some unique characteristics, which results in traditional intrusion detection systems that cannot be directly reused on it. Aiming at the industrial control system, this paper constructs all attack paths from the hacker's perspective through the attack tree model, and uses the LSTM algorithm to identify and classify the attack behavior, and then further classify the attack event by extracting atomic actions. Finally, through the constructed attack tree model, the results are reversed and predicted. The results show that the model has a good effect on attack recognition, and can effectively analyze the hacker attack path and predict the next attack target.
Cheng, J., He, R., Yuepeng, E., Wu, Y., You, J., Li, T..  2020.  Real-Time Encrypted Traffic Classification via Lightweight Neural Networks. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–6.
The fast growth of encrypted traffic puts forward burning requirements on the efficiency of traffic classification. Although deep learning models perform well in the classification, they sacrifice the efficiency to obtain high-precision results. To reduce the resource and time consumption, a novel and lightweight model is proposed in this paper. Our design principle is to “maximize the reuse of thin modules”. A thin module adopts the multi-head attention and the 1D convolutional network. Attributed to the one-step interaction of all packets and the parallelized computation of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of 1D convolutional networks are proved in traffic classification. Besides, the proposed model can work well in a real time manner, since only three consecutive packets of a flow are needed. To improve the stability of the model, the designed network is trained with the aid of ResNet, layer normalization and learning rate warmup. The proposed model outperforms the state-of-the-art works based on deep learning on two public datasets. The results show that our model has higher accuracy and running efficiency, while the number of parameters used is 1.8% of the 1D convolutional network and the training time halves.
2021-09-07
Hossain, Md Delwar, Inoue, Hiroyuki, Ochiai, Hideya, FALL, Doudou, Kadobayashi, Youki.  2020.  Long Short-Term Memory-Based Intrusion Detection System for In-Vehicle Controller Area Network Bus. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :10–17.
The Controller Area Network (CAN) bus system works inside connected cars as a central system for communication between electronic control units (ECUs). Despite its central importance, the CAN does not support an authentication mechanism, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways: denial of service, fuzzing, spoofing, etc. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We first inject attacks at the CAN bus system in a car that we have at our disposal to generate the attack dataset, which we use to test and train our model. Our results demonstrate that our classifier is efficient in detecting the CAN attacks. We achieved a detection accuracy of 99.9949%.
2021-03-29
Bodhe, A., Sangale, A..  2020.  Network Parameter Analysis; ad hoc WSN for Security Protocol with Fuzzy Logic. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). :960—963.

The wireless communication has become very vast, important and easy to access nowadays because of less cost associated and easily available mobile devices. It creates a potential threat for the community while accessing some secure information like banking passwords on the unsecured network. This proposed research work expose such a potential threat such as Rogue Access Point (RAP) detection using soft computing prediction tool. Fuzzy logic is used to implement the proposed model to identify the presence of RAP existence in the network.

2021-02-23
Ratti, R., Singh, S. R., Nandi, S..  2020.  Towards implementing fast and scalable Network Intrusion Detection System using Entropy based Discretization Technique. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.

2021-03-04
Tang, R., Yang, Z., Li, Z., Meng, W., Wang, H., Li, Q., Sun, Y., Pei, D., Wei, T., Xu, Y. et al..  2020.  ZeroWall: Detecting Zero-Day Web Attacks through Encoder-Decoder Recurrent Neural Networks. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2479—2488.

Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical Web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.

2021-05-26
Zhengbo, Chen, Xiu, Liu, Yafei, Xing, Miao, Hu, Xiaoming, Ju.  2020.  Markov Encrypted Data Prefetching Model Based On Attribute Classification. 2020 5th International Conference on Computer and Communication Systems (ICCCS). :54—59.

In order to improve the buffering performance of the data encrypted by CP-ABE (ciphertext policy attribute based encryption), this paper proposed a Markov prefetching model based on attribute classification. The prefetching model combines the access strategy of CP-ABE encrypted file, establishes the user relationship network according to the attribute value of the user, classifies the user by the modularity-based community partitioning algorithm, and establishes a Markov prefetching model based on attribute classification. In comparison with the traditional Markov prefetching model and the classification-based Markov prefetching model, the attribute-based Markov prefetching model is proposed in this paper has higher prefetch accuracy and coverage.

2021-08-17
Byrnes, Jeffrey, Hoang, Thomas, Mehta, Nihal Nitin, Cheng, Yuan.  2020.  A Modern Implementation of System Call Sequence Based Host-based Intrusion Detection Systems. 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :218—225.
Much research is concentrated on improving models for host-based intrusion detection systems (HIDS). Typically, such research aims at improving a model's results (e.g., reducing the false positive rate) in the familiar static training/testing environment using the standard data sources. Matching advancements in the machine learning community, researchers in the syscall HIDS domain have developed many complex and powerful syscall-based models to serve as anomaly detectors. These models typically show an impressive level of accuracy while emphasizing on minimizing the false positive rate. However, with each proposed model iteration, we get further from the setting in which these models are intended to operate. As kernels become more ornate and hardened, the implementation space for anomaly detection models is narrowing. Furthermore, the rapid advancement of operating systems and the underlying complexity introduced dictate that the sometimes decades-old datasets have long been obsolete. In this paper, we attempt to bridge the gap between theoretical models and their intended application environments by examining the recent Linux kernel 5.7.0-rc1. In this setting, we examine the feasibility of syscall-based HIDS in modern operating systems and the constraints imposed on the HIDS developer. We discuss how recent advancements to the kernel have eliminated the previous syscall trace collect method of writing syscall table wrappers, and propose a new approach to generate data and place our detection model. Furthermore, we present the specific execution time and memory constraints that models must meet in order to be operable within their intended settings. Finally, we conclude with preliminary results from our model, which primarily show that in-kernel machine learning models are feasible, depending on their complexity.
2020-11-23
Ramapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K..  2019.  Anomaly Detection Models for Smart Home Security. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.
2019-12-30
Kim, Sunbin, Kim, Hyeoncheol.  2019.  Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit. 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). :1–4.
Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.
2020-10-26
Chen, Cheng-Yu, Hsiao, Shun-Wen.  2019.  IoT Malware Dynamic Analysis Profiling System and Family Behavior Analysis. 2019 IEEE International Conference on Big Data (Big Data). :6013–6015.
Not only the number of deployed IoT devices increases but also that of IoT malware increases. We eager to understand the threat made by IoT malware but we lack tools to observe, analyze and detect them. We design and implement an automatic, virtual machine-based profiling system to collect valuable IoT malware behavior, such as API call invocation, system call execution, etc. In addition to conventional profiling methods (e.g., strace and packet capture), the proposed profiling system adapts virtual machine introspection based API hooking technique to intercept API call invocation by malware, so that our introspection would not be detected by IoT malware. We then propose a method to convert the multiple sequential data (API calls) to a family behavior graph for further analysis.
2020-05-18
Nambiar, Sindhya K, Leons, Antony, Jose, Soniya, Arunsree.  2019.  Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :782–785.
In various natural language processing applications, PART-OF-SPEECH (POS) tagging is performed as a preprocessing step. For making POS tagging accurate, various techniques have been explored. But in Indian languages, not much work has been done. This paper describes the methods to build a Part of speech tagger by using hidden markov model. Supervised learning approach is implemented in which, already tagged sentences in malayalam is used to build hidden markov model.
2020-07-06
Chai, Yadeng, Liu, Yong.  2019.  Natural Spoken Instructions Understanding for Robot with Dependency Parsing. 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). :866–871.
This paper presents a method based on syntactic information, which can be used for intent determination and slot filling tasks in a spoken language understanding system including the spoken instructions understanding module for robot. Some studies in recent years attempt to solve the problem of spoken language understanding via syntactic information. This research is a further extension of these approaches which is based on dependency parsing. In this model, the input for neural network are vectors generated by a dependency parsing tree, which we called window vector. This vector contains dependency features that improves performance of the syntactic-based model. The model has been evaluated on the benchmark ATIS task, and the results show that it outperforms many other syntactic-based approaches, especially in terms of slot filling, it has a performance level on par with some state of the art deep learning algorithms in recent years. Also, the model has been evaluated on FBM3, a dataset of the RoCKIn@Home competition. The overall rate of correctly understanding the instructions for robot is quite good but still not acceptable in practical use, which is caused by the small scale of FBM3.
2020-09-18
Taggu, Amar, Marchang, Ningrinla.  2019.  Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1—5.
Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.
2020-08-24
Sophakan, Natnaree, Sathitwiriyawong, Chanboon.  2019.  A Secured OpenFlow-Based Software Defined Networking Using Dynamic Bayesian Network. 2019 19th International Conference on Control, Automation and Systems (ICCAS). :1517–1522.
OpenFlow has been the main standard protocol of software defined networking (SDN) since the launch of this new networking paradigm. It is a programmable network protocol that controls traffic flows among switches and routers regardless of their platforms. Its security relies on the optional implementation of Transport Layer Security (TLS) which has been proven vulnerable. The aim of this research was to develop a secured OpenFlow, so-called Secured-OF. A stateful firewall was used to store state information for further analysis. Dynamic Bayesian Network (DBN) was used to learn denial-of-service attack and distributed denial-of-service attack. It analyzes packet states to determine the nature of an attack and adds that piece of information to the flow table entry. The proposed Secured-OF model in Ryu controller was evaluated with several performance metrics. The analytical evaluation of the proposed Secured-OF scheme was performed on an emulated network. The results showed that the proposed Secured-OF scheme offers a high attack detection accuracy at 99.5%. In conclusion, it was able to improve the security of the OpenFlow controller dramatically with trivial performance degradation compared to an SDN with no security implementation.
2020-03-18
Li, Tao, Guo, Yuanbo, Ju, Ankang.  2019.  A Self-Attention-Based Approach for Named Entity Recognition in Cybersecurity. 2019 15th International Conference on Computational Intelligence and Security (CIS). :147–150.
With cybersecurity situation more and more complex, data-driven security has become indispensable. Numerous cybersecurity data exists in textual sources and data analysis is difficult for both security analyst and the machine. To convert the textual information into structured data for further automatic analysis, we extract cybersecurity-related entities and propose a self-attention-based neural network model for the named entity recognition in cybersecurity. Considering the single word feature not enough for identifying the entity, we introduce CNN to extract character feature which is then concatenated into the word feature. Then we add the self-attention mechanism based on the existing BiLSTM-CRF model. Finally, we evaluate the proposed model on the labelled dataset and obtain a better performance than the previous entity extraction model.
2020-09-04
Wu, Yi, Liu, Jian, Chen, Yingying, Cheng, Jerry.  2019.  Semi-black-box Attacks Against Speech Recognition Systems Using Adversarial Samples. 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). :1—5.
As automatic speech recognition (ASR) systems have been integrated into a diverse set of devices around us in recent years, security vulnerabilities of them have become an increasing concern for the public. Existing studies have demonstrated that deep neural networks (DNNs), acting as the computation core of ASR systems, is vulnerable to deliberately designed adversarial attacks. Based on the gradient descent algorithm, existing studies have successfully generated adversarial samples which can disturb ASR systems and produce adversary-expected transcript texts designed by adversaries. Most of these research simulated white-box attacks which require knowledge of all the components in the targeted ASR systems. In this work, we propose the first semi-black-box attack against the ASR system - Kaldi. Requiring only partial information from Kaldi and none from DNN, we can embed malicious commands into a single audio chip based on the gradient-independent genetic algorithm. The crafted audio clip could be recognized as the embedded malicious commands by Kaldi and unnoticeable to humans in the meanwhile. Experiments show that our attack can achieve high attack success rate with unnoticeable perturbations to three types of audio clips (pop music, pure music, and human command) without the need of the underlying DNN model parameters and architecture.
2020-04-06
Li, Jiabin, Xue, Zhi.  2019.  Distributed Threat Intelligence Sharing System: A New Sight of P2P Botnet Detection. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.

Botnet has been evolving over time since its birth. Nowadays, P2P (Peer-to-Peer) botnet has become a main threat to cyberspace security, owing to its strong concealment and easy expansibility. In order to effectively detect P2P botnet, researchers often focus on the analysis of network traffic. For the sake of enriching P2P botnet detection methods, the author puts forward a new sight of applying distributed threat intelligence sharing system to P2P botnet detection. This system aims to fight against distributed botnet by using distributed methods itself, and then to detect botnet in real time. To fulfill the goal of botnet detection, there are 3 important parts: the threat intelligence sharing and evaluating system, the BAV quantitative TI model, and the AHP and HMM based analysis algorithm. Theoretically, this method should work on different types of distributed cyber threat besides P2P botnet.

2020-02-10
Ishtiaq, Asra, Islam, Muhammad Arshad, Azhar Iqbal, Muhammad, Aleem, Muhammad, Ahmed, Usman.  2019.  Graph Centrality Based Spam SMS Detection. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :629–633.

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

2020-03-09
Li, Zhixin, Liu, Lei, Kong, Degang.  2019.  Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :700–703.

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

2020-09-28
Mitani, Tatsuo, OTSUKA, Akira.  2019.  Traceability in Permissioned Blockchain. 2019 IEEE International Conference on Blockchain (Blockchain). :286–293.
In this paper, we propose the traceability of assets in a permissioned blockchain connected with a permissionless blockchain. We make traceability of assets in the permissioned blockchain be defined and be expressed as a hidden Markov model. There exists no dishonest increase and decrease of assets in this model. The condition is called balance. As we encrypt this model with fully homomorphic encryption and apply the zero knowledge proof of plaintext knowledge, we show that the trace-ability and balance of the permissioned blockchain are able to be proved in zero knowledge to the permissionless blockchain with concealing the asset allocation of the permissioned blockchain.