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Al-Ameer, Ali, AL-Sunni, Fouad.  2021.  A Methodology for Securities and Cryptocurrency Trading Using Exploratory Data Analysis and Artificial Intelligence. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). :54–61.
This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.
Renda, Alessandro, Ducange, Pietro, Gallo, Gionatan, Marcelloni, Francesco.  2021.  XAI Models for Quality of Experience Prediction in Wireless Networks. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.
Li, Kemeng, Zheng, Dong, Guo, Rui.  2021.  An Anonymous Editable Blockchain Scheme Based on Certificateless Aggregate Signature. 2021 3rd International Conference on Natural Language Processing (ICNLP). :57–67.
Blockchain technology has gradually replaced traditional centralized data storage methods, and provided people reliable data storage services with its decentralized and non-tamperable features. However, the current blockchain data supervision is insufficient and the data cannot be modified once it is on the blockchain, which will cause the blockchain system to face various problems such as illegal information cannot be deleted and breach of smart contract cannot be fixed in time. To address these issues, we propose an anonymous editable blockchain scheme based on the reconstruction of the blockchain structure of the SpaceMint combining with the certificateless aggregate signature algorithm. Users register with their real identities and use pseudonyms in the system to achieve their anonymity. If the number of users who agree to edit meets the threshold, the data on the blockchain can be modified or deleted, and our scheme has the function of accountability for malicious behavior. The security analysis show that the proposed certificateless aggregate signature algorithm enjoys the unforgeability under the adaptive selected message attack. Moreover, the method of setting the threshold of related users is adopted to guarantee the effectiveness and security of editing blockchain data. At last, we evaluate the performance of our certificateless aggregate signature algorithm and related schemes in theoretical analysis and experimental simulation, which demonstrates our scheme is feasible and efficient in storage, bandwidth and computational cost.
Sun, Ziwen, Zhang, Shuguo.  2021.  Modeling of Security Risk for Industrial Cyber-Physics System under Cyber-Attacks. 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). :361–368.
Due to the insufficient awareness of decision makers on the security risks of industrial cyber-physical systems(ICPS) under cyber-attacks, it is difficult to take effective defensive measures according to the characteristics of different cyber-attacks in advance. To solve the above problem, this paper gives a qualitative analysis method of ICPS security risk from the perspective of defenders. The ICPS being attacked is modeled as a dynamic closed-loop fusion model where the mathematical models of the physical plant and the feedback controller are established. Based on the fusion model, the disruption resources generated by attacks are mathematically described. Based on the designed Kalman filter, the detection of attacks is judged according to the residual value of the system. According to the disruption resources and detectability, a general security risk level model is further established to evaluate the security risk level of the system under attacks. The simulation experiments are conducted by using Matlab to analyze the destructiveness and detectability of attacks, where the results show that the proposed qualitative analysis method can effectively describe the security risk under the cyber-attacks.
Buccafurri, Francesco, Angelis, Vincenzo De, Francesca Idone, Maria, Labrini, Cecilia.  2021.  WIP: An Onion-Based Routing Protocol Strengthening Anonymity. 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). :231–235.
Anonymous Communication Networks (ACNs) are networks in which, beyond data confidentiality, also traffic flow confidentiality is provided. The most popular routing approach for ACNs also used in practice is Onion. Onion is based on multiple encryption wrapping combined with the proxy mechanism (relay nodes). However, it offers neither sender anonymity nor recipient anonymity in a global passive adversary model, simply because the adversary can observe (at the first relay node) the traffic coming from the sender, and (at the last relay node) the traffic delivered to the recipient. This may also cause a loss of relationship anonymity if timing attacks are performed. This paper presents Onion-Ring, a routing protocol that improves anonymity of Onion in the global adversary model, by achieving sender anonymity and recipient anonymity, and thus relationship anonymity.
Künnemann, Robert, Garg, Deepak, Backes, Michael.  2021.  Accountability in the Decentralised-Adversary Setting. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1–16.
A promising paradigm in protocol design is to hold parties accountable for misbehavior, instead of postulating that they are trustworthy. Recent approaches in defining this property, called accountability, characterized malicious behavior as a deviation from the protocol that causes a violation of the desired security property, but did so under the assumption that all deviating parties are controlled by a single, centralized adversary. In this work, we investigate the setting where multiple parties can deviate with or without coordination in a variant of the applied-π calculus.We first demonstrate that, under realistic assumptions, it is impossible to determine all misbehaving parties; however, we show that accountability can be relaxed to exclude causal dependencies that arise from the behavior of deviating parties, and not from the protocol as specified. We map out the design space for the relaxation, point out protocol classes separating these notions and define conditions under which we can guarantee fairness and completeness. Most importantly, we discover under which circumstances it is correct to consider accountability in the single-adversary setting, where this property can be verified with off-the-shelf protocol verification tools.
Yang, SU.  2021.  An Approach on Attack Path Prediction Modeling Based on Game Theory. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:2604–2608.
Considering the lack of theoretical analysis for distributed network under APT (advanced persistent threat) attacks, a game model was proposed to solve the problem based on APT attack path. Firstly, this paper analyzed the attack paths of attackers and proposed the defensive framework of network security by analyzing the characteristics of the APT attack and the distributed network structure. Secondly, OAPG(an attack path prediction model oriented to APT) was established from the value both the attacker and the defender based on game theory, besides, this paper calculated the game equilibrium and generated the maximum revenue path of the attacker, and then put forward the best defensive strategy for defender. Finally, this paper validated the model by an instance of APT attack, the calculated results showed that the model can analyze the attacker and defender from the attack path, and can provide a reasonable defense scheme for organizations that use distributed networks.
Yixuan, Zhang, Qiwei, Xu, Sheng, Long, Zhihao, Cheng, Chao, Zhi.  2021.  Design of a New Micro Linear Actuator Owning Two-phase No-cross Planar Coils. 2021 IEEE 4th International Electrical and Energy Conference (CIEEC). :1–11.
This paper presents a new micro linear actuator design. The North-South (NS) permanent magnet array configuration is assembled as the mobile part. The fixed part is designed to two-phase planar coils with no crossings avoiding interferences between overlapped conductors. The analytical calculation of the permanent magnet array verifies the feasibility of the finite element simulation. And then electromagnetic optimizations based on simulation to maximize the average thrust and minimize thrust ripple. In order to deal with millimeter level structure design, a microfabrication approach is adopted to process the new micro linear actuator in silicon material. The new micro linear actuator is able to perform millimeter level displacement strokes along a single axis in the horizontal plane. The experimental results demonstrate that the new micro linear actuator is capable of delivering variable strokes up to 5 mm with a precision error of 30 μm in position closed loop control and realizes the maximum velocity of 26.62mm/s with maximum error of 4.92%.
Xu, Zhiwu, Hu, Xiongya, Tao, Yida, Qin, Shengchao.  2020.  Analyzing Cryptographic API Usages for Android Applications Using HMM and N-Gram. 2020 International Symposium on Theoretical Aspects of Software Engineering (TASE). :153–160.
A recent research shows that 88 % of Android applications that use cryptographic APIs make at least one mistake. For this reason, several tools have been proposed to detect crypto API misuses, such as CryptoLint, CMA, and CogniCryptSAsT. However, these tools depend heavily on manually designed rules, which require much cryptographic knowledge and could be error-prone. In this paper, we propose an approach based on probabilistic models, namely, hidden Markov model and n-gram model, to analyzing crypto API usages in Android applications. The difficulty lies in that crypto APIs are sensitive to not only API orders, but also their arguments. To address this, we have created a dataset consisting of crypto API sequences with arguments, wherein symbolic execution is performed. Finally, we have also conducted some experiments on our models, which shows that ( i) our models are effective in capturing the usages, detecting and locating the misuses; (ii) our models perform better than the ones without symbolic execution, especially in misuse detection; and (iii) compared with CogniCryptSAsT, our models can detect several new misuses.
Zhang, Lin, Chen, Xin, Kong, Fanxin, Cardenas, Alvaro A..  2020.  Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations. 2020 IEEE Real-Time Systems Symposium (RTSS). :205–217.
Attack detection and recovery are fundamental elements for the operation of safe and resilient cyber-physical systems. Most of the literature focuses on attack-detection, while leaving attack-recovery as an open problem. In this paper, we propose novel attack-recovery control for securing cyber-physical systems. Our recovery control consists of new concepts required for a safe response to attacks, which includes the removal of poisoned data, the estimation of the current state, a prediction of the reachable states, and the online design of a new controller to recover the system. The synthesis of such recovery controllers for cyber-physical systems has barely investigated so far. To fill this void, we present a formal method-based approach to online compute a recovery control sequence that steers a system under an ongoing sensor attack from the current state to a target state such that no unsafe state is reachable on the way. The method solves a reach-avoid problem on a Linear Time-Invariant (LTI) model with the consideration of an error bound $ε$ $\geq$ 0. The obtained recovery control is guaranteed to work on the original system if the behavioral difference between the LTI model and the system's plant dynamics is not larger than $ε$. Since a recovery control should be obtained and applied at the runtime of the system, in order to keep its computational time cost as low as possible, our approach firstly builds a linear programming restriction with the accordingly constrained safety and target specifications for the given reach-avoid problem, and then uses a linear programming solver to find a solution. To demonstrate the effectiveness of our method, we provide (a) the comparison to the previous work over 5 system models under 3 sensor attack scenarios: modification, delay, and reply; (b) a scalability analysis based on a scalable model to evaluate the performance of our method on large-scale systems.
Ma, Chuang, You, Haisheng, Wang, Li, Zhang, Jiajun.  2020.  Intelligent Cybersecurity Situational Awareness Model Based on Deep Neural Network. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :76–83.
In recent years, we have faced a series of online threats. The continuous malicious attacks on the network have directly caused a huge threat to the user's spirit and property. In order to deal with the complex security situation in today's network environment, an intelligent network situational awareness model based on deep neural networks is proposed. Use the nonlinear characteristics of the deep neural network to solve the nonlinear fitting problem, establish a network security situation assessment system, take the situation indicators output by the situation assessment system as a guide, and collect on the main data features according to the characteristics of the network attack method, the main data features are collected and the data is preprocessed. This model designs and trains a 4-layer neural network model, and then use the trained deep neural network model to understand and analyze the network situation data, so as to build the network situation perception model based on deep neural network. The deep neural network situational awareness model designed in this paper is used as a network situational awareness simulation attack prediction experiment. At the same time, it is compared with the perception model using gray theory and Support Vector Machine(SVM). The experiments show that this model can make perception according to the changes of state characteristics of network situation data, establish understanding through learning, and finally achieve accurate prediction of network attacks. Through comparison experiments, datatypized neural network deep neural network situation perception model is proved to be effective, accurate and superior.
Raich, Philipp, Kastner, Wolfgang.  2021.  A Computational Model for 6LoWPAN Multicast Routing. 2021 17th IEEE International Conference on Factory Communication Systems (WFCS). :143–146.
Reliable group communication is an important cornerstone for various applications in the domain of Industrial Internet of Things (IIoT). Yet, despite various proposals, state-of- the-art (open) protocol stacks for IPv6-enabled Low Power and Lossy Networks (LLNs) have little to offer, regarding standardized or agreed-upon protocols for correct multicast routing, not to mention reliable multicast. We present an informal computational model, which allows us to analyze the respective candidates for multicast routing. Further, we focus on the IEEE 802.15.4/6LoWPAN stack and discuss prominent multicast routing protocols and how they fit into this model.
Zhu, Huifeng, Guo, Xiaolong, Jin, Yier, Zhang, Xuan.  2020.  PowerScout: A Security-Oriented Power Delivery Network Modeling Framework for Cross-Domain Side-Channel Analysis. 2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1–6.
The growing complexity of modern electronic systems often leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains, despite the fact that PDNs are not part of the functional design. Recent work have demonstrated that exploitation of the side channel can compromise the system security (i.e. information leakage and fault injection). In this work, we systematically investigate the PDN-based side channel as well as the countermeasures. To facilitate our goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in literature through simulation results. Further, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23dB at mid-frequency and inter-chip leakage by an average of 31.68dB at mid- and high-frequencies. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control.
Chang, Sang-Yoon, Park, Younghee, Kengalahalli, Nikhil Vijayakumar, Zhou, Xiaobo.  2020.  Query-Crafting DoS Threats Against Internet DNS. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.
Domain name system (DNS) resolves the IP addresses of domain names and is critical for IP networking. Recent denial-of-service (DoS) attacks on Internet targeted the DNS system (e.g., Dyn), which has the cascading effect of denying the availability of the services and applications relying on the targeted DNS. In view of these attacks, we investigate the DoS on DNS system and introduce the query-crafting threats where the attacker controls the DNS query payload (the domain name) to maximize the threat impact per query (increasing the communications between the DNS servers and the threat time duration), which is orthogonal to other DoS approaches to increase the attack impact such as flooding and DNS amplification. We model the DNS system using a state diagram and comprehensively analyze the threat space, identifying the threat vectors which include not only the random/invalid domains but also those using the domain name structure to combine valid strings and random strings. Query-crafting DoS threats generate new domain-name payloads for each query and force increased complexity in the DNS query resolution. We test the query-crafting DoS threats by taking empirical measurements on the Internet and show that they amplify the DoS impact on the DNS system (recursive resolver) by involving more communications and taking greater time duration. To defend against such DoS or DDoS threats, we identify the relevant detection features specific to query-crafting threats and evaluate the defense using our prototype in CloudLab.
Damasevicius, Robertas, Toldinas, Jevgenijus, Venckauskas, Algimantas, Grigaliunas, Sarunas, Morkevicius, Nerijus.  2020.  Technical Threat Intelligence Analytics: What and How to Visualize for Analytic Process. 2020 24th International Conference Electronics. :1–4.
Visual Analytics uses data visualization techniques for enabling compelling data analysis by engaging graphical and visual portrayal. In the domain of cybersecurity, convincing visual representation of data enables to ascertain valuable observations that allow the domain experts to construct efficient cyberattack mitigation strategies and provide useful decision support. We present a survey of visual analytics tools and methods in the domain of cybersecurity. We explore and discuss Technical Threat Intelligence visualization tools using the Five Question Method. We conclude the analysis of the works using Moody's Physics of Notations, and VIS4ML ontology as a methodological background of visual analytics process. We summarize our analysis as a high-level model of visual analytics for cybersecurity threat analysis.
Abbas, Syed Ghazanfar, Zahid, Shahzaib, Hussain, Faisal, Shah, Ghalib A., Husnain, Muhammad.  2020.  A Threat Modelling Approach to Analyze and Mitigate Botnet Attacks in Smart Home Use Case. 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE). :122–129.
Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.
Franchina, L., Socal, A..  2020.  Innovative Predictive Model for Smart City Security Risk Assessment. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1831–1836.
In a Smart City, new technologies such as big data analytics, data fusion and artificial intelligence will increase awareness by measuring many phenomena and storing a huge amount of data. 5G will allow communication of these data among different infrastructures instantaneously. In a Smart City, security aspects are going to be a major concern. Some drawbacks, such as vulnerabilities of a highly integrated system and information overload, must be considered. To overcome these downsides, an innovative predictive model for Smart City security risk assessment has been developed. Risk metrics and indicators are defined by considering data coming from a wide range of sensors. An innovative ``what if'' algorithm is introduced to identify critical infrastructures functional relationship. Therefore, it is possible to evaluate the effects of an incident that involves one infrastructure over the others.
Luo, Bo, Beuran, Razvan, Tan, Yasuo.  2020.  Smart Grid Security: Attack Modeling from a CPS Perspective. 2020 IEEE Computing, Communications and IoT Applications (ComComAp). :1–6.
With the development of smart grid technologies and the fast adoption of household IoT devices in recent years, new threats, attacks, and security challenges arise. While a large number of vulnerabilities, threats, attacks and controls have been discussed in the literature, there lacks an abstract and generalizable framework that can be used to model the cyber-physical interactions of attacks and guide the design of defense mechanisms. In this paper, we propose a new modeling approach for security attacks in smart grids and IoT devices using a Cyber-Physical Systems (CPS) perspective. The model considers both the cyber and physical aspects of the core components of the smart grid system and the household IoT devices, as well as the interactions between the components. In particular, our model recognizes the two parallel attack channels via the cyber world and the physical world, and identifies the potential crossing routes between these two attack channels. We further discuss all possible attack surfaces, attack objectives, and attack paths in this newly proposed model. As case studies, we examine from the perspective of this new model three representative attacks proposed in the literature. The analysis demonstrates the applicability of the model, for instance, to assist the design of detection and defense mechanisms against smart grid cyber-attacks.
Yadav, Mohini, Shankar, Deepak, Jose, Tom.  2020.  Functional Safety for Braking System through ISO 26262, Operating System Security and DO 254. 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). :1–8.
This paper presents an introduction to functional safety through ISO 26262 focusing on system, software and hardware possible failures that bring security threats and discussion on DO 254. It discusses the approach to bridge the gap between different other hazard level and system ability to identify the particular fault and resolve it minimum time span possible. Results are analyzed by designing models to check and avoid all the failures, loophole prior development.
Qu, Dapeng, Zhang, Jiankun, Hou, Zhenhuan, Wang, Min, Dong, Bo.  2020.  A Trust Routing Scheme Based on Identification of Non-complete Cooperative Nodes in Mobile Peer-to-Peer Networks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :22–29.
Mobile peer-to-peer network (MP2P) attracts increasing attentions due to the ubiquitous use of mobile communication and huge success of peer-to-peer (P2P) mode. However, open p2p mode makes nodes tend to be selfish, and the scarcity of resources in mobile nodes aggravates this problem, thus the nodes easily express a non-complete cooperative (NCC) attitude. Therefore, an identification of non-complete cooperative nodes and a corresponding trust routing scheme are proposed for MP2P in this paper. The concept of octant is firstly introduced to build a trust model which analyzes nodes from three dimensions, namely direct trust, internal state and recommendation reliability, and then the individual non-complete cooperative (INCC) nodes can be identified by the division of different octants. The direct trust monitors nodes' external behaviors, and the consideration of internal state and recommendation reliability contributes to differentiate the subjective and objective non-cooperation, and mitigate the attacks about direct trust values respectively. Thus, the trust model can identify various INCC nodes accurately. On the basis of identification of INCC nodes, cosine similarity method is applied to identify collusive non-complete cooperate (CNCC) nodes. Moreover, a trust routing scheme based on the identification of NCC nodes is presented to reasonably deal with different kinds of NCC nodes. Results from extensive simulation experiments demonstrate that this proposed identification and routing scheme have better performances, in terms of identification precision and packet delivery fraction than current schemes respectively.
Alsoghyer, Samah, Almomani, Iman.  2020.  On the Effectiveness of Application Permissions for Android Ransomware Detection. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA). :94–99.
Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.
Pan, Zhicheng, Deng, Jun, Chu, Jinwei, Zhang, Zhanlong, Dong, Zijian.  2020.  Research on Correlation Analysis of Vibration Signals at Multiple Measuring Points and Black Box Model of Flexible-DC Transformer. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). :3238–3242.
The internal structure of the flexible-DC transformer is complicated and the lack of a reliable vibration calculation model limits the application of the vibration analysis method in the fault diagnosis of the flexible-DC transformer. In response to this problem, this paper analyzes the correlation between the vibration signals of multiple measuring points and establishes a ``black box'' model of transformer vibration detection. Using the correlation analysis of multiple measuring points and BP neural network, a ``black box'' model that simulates the internal vibration transmission relationship of the transformer is established. The vibration signal of the multiple measuring points can be used to calculate the vibration signal of the target measuring point under specific working conditions. This can provide effective information for fault diagnosis and judgment of the running status of the flexible-DC transformer.
Petrenko, Sergei A., Petrenko, Alexey S., Makoveichuk, Krystina A., Olifirov, Alexander V..  2020.  "Digital Bombs" Neutralization Method. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :446–451.
The article discusses new models and methods for timely identification and blocking of malicious code of critically important information infrastructure based on static and dynamic analysis of executable program codes. A two-stage method for detecting malicious code in the executable program codes (the so-called "digital bombs") is described. The first step of the method is to build the initial program model in the form of a control graph, the construction is carried out at the stage of static analysis of the program. The article discusses the purpose, features and construction criteria of an ordered control graph. The second step of the method is to embed control points in the program's executable code for organizing control of the possible behavior of the program using a specially designed recognition automaton - an automaton of dynamic control. Structural criteria for the completeness of the functional control of the subprogram are given. The practical implementation of the proposed models and methods was completed and presented in a special instrumental complex IRIDA.
Chamotra, Saurabh, Barbhuiya, Ferdous Ahmed.  2020.  Analysis and Modelling of Multi-Stage Attacks. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1268–1275.
Honeypots are the information system resources used for capturing and analysis of cyber attacks. Highinteraction Honeypots are capable of capturing attacks in their totality and hence are an ideal choice for capturing multi-stage cyber attacks. The term multi-stage attack is an abstraction that refers to a class of cyber attacks consisting of multiple attack stages. These attack stages are executed either by malicious codes, scripts or sometimes even inbuilt system tools. In the work presented in this paper we have proposed a framework for capturing, analysis and modelling of multi-stage cyber attacks. The objective of our work is to devise an effective mechanism for the classification of multi-stage cyber attacks. The proposed framework comprise of a network of high interaction honeypots augmented with an attack analysis engine. The analysis engine performs rule based labeling of captured honeypot data. The labeling engine labels the attack data as generic events. These events are further fused to generate attack graphs. The hence generated attack graphs are used to characterize and later classify the multi-stage cyber attacks.
Cheng, Xiuzhen, Chellappan, Sriram, Cheng, Wei, Sahin, Gokhan.  2020.  Guest Editorial Introduction to the Special Section on Network Science for High-Confidence Cyber-Physical Systems. IEEE Transactions on Network Science and Engineering. 7:764–765.
The papers in this special section focus on network science for high confidence cyber-physical systems (CPS) Here CPS refers to the engineered systems that can seamlessly integrate the physical world with the cyber world via advanced computation and communication capabilities. To enable high-confidence CPS for achieving better benefits as well as supporting emerging applications, network science-based theories and methodologies are needed to cope with the ever-growing complexity of smart CPS, to predict the system behaviors, and to model the deep inter-dependencies among CPS and the natural world. The major objective of this special section is to exploit various network science techniques such as modeling, analysis, mining, visualization, and optimization to advance the science of supporting high-confidence CPS for greater assurances of security, safety, scalability, efficiency, and reliability. These papers bring a timely and important research topic. The challenges and opportunities of applying network science approaches to high-confidence CPS are profound and far-reaching.
Conference Name: IEEE Transactions on Network Science and Engineering