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Wright, Marc, Chizari, Hassan, Viana, Thiago.  2020.  Analytical Framework for National Cyber-Security and Corresponding Critical Infrastructure: A Pragmatistic Approach. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). :127–130.
Countries are putting cyber-security at the forefront of their national issues. With the increase in cyber capabilities and infrastructure systems becoming cyber-enabled, threats now have a physical impact from the cyber dimension. This paper proposes an analytical framework for national cyber-security profiling by taking national governmental and technical threat modeling simulations. Applying thematic analysis towards national cybersecurity strategy helps further develop understanding, in conjunction with threat modeling methodology simulation, to gain insight into critical infrastructure threat impact.
Almohri, Hussain M. J., Watson, Layne T., Evans, David.  2020.  An Attack-Resilient Architecture for the Internet of Things. IEEE Transactions on Information Forensics and Security. 15:3940–3954.
With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.
Conference Name: IEEE Transactions on Information Forensics and Security
Samoshina, Anna, Promyslov, Vitaly, Kamesheva, Saniya, Galin, Rinat.  2020.  Application of Cloud Modeling Technologies in Ensuring Cyber Security of APCS. 2020 13th International Conference "Management of Large-Scale System Development" (MLSD). :1–5.
This paper describes the development of a module for calculating security zones in the cloud service of APCS modeling. A mathematical model based on graph theory is used. This allows you to describe access relationships between objects and security policy subjects. A comparative analysis of algorithms for traversing graph vertices is performed in order to select a suitable method for allocating security zones. The implemented algorithm for calculating security zones was added to the cloud service
Nweke, Livinus Obiora, Wolthusen, Stephen D..  2020.  Modelling Adversarial Flow in Software-Defined Industrial Control Networks Using a Queueing Network Model. 2020 IEEE Conference on Communications and Network Security (CNS). :1–6.
In recent years, software defined networking (SDN) has been proposed for enhancing the security of industrial control networks. However, its ability to guarantee the quality of service (QoS) requirements of such networks in the presence of adversarial flow still needs to be investigated. Queueing theory and particularly queueing network models have long been employed to study the performance and QoS characteristics of networks. The latter appears to be particularly suitable to capture the behaviour of SDN owing to the dependencies between layers, planes and components in an SDN architecture. Also, several authors have used queueing network models to study the behaviour of different application of SDN architectures, but none of the existing works have considered the strong periodic network traffic in software-defined industrial control networks. In this paper, we propose a queueing network model for softwaredefined industrial control networks, taking into account the strong periodic patterns of the network traffic in the data plane. We derive the performance measures for the analytical model and apply the queueing network model to study the effect of adversarial flow in software-defined industrial control networks.
Thie, Nicolas, Franken, Marco, Schwaeppe, Henrik, Böttcher, Luis, Müller, Christoph, Moser, Albert, Schumann, Klemens, Vigo, Daniele, Monaci, Michele, Paronuzzi, Paolo et al..  2020.  Requirements for Integrated Planning of Multi-Energy Systems. 2020 6th IEEE International Energy Conference (ENERGYCon). :696–701.
The successful realization of the climate goals agreed upon in the European Union's COP21 commitments makes a fundamental change of the European energy system necessary. In particular, for a reduction of greenhouse gas emissions over 80%, the use of renewable energies must be increased not only in the electricity sector but also across all energy sectors, such as heat and mobility. Furthermore, a progressive integration of renewable energies increases the risk of congestions in the transmission grid and makes network expansion necessary. An efficient planning for future energy systems must comprise the coupling of energy sectors as well as interdependencies of generation and transmission grid infrastructure. However, in traditional energy system planning, these aspects are considered as decoupled. Therefore, the project PlaMES develops an approach for integrated planning of multi-energy systems on a European scale. This paper aims at analyzing the model requirements and describing the modeling approach.
Choi, Ho-Jin, Lee, Young-Jun.  2020.  Deep Learning Based Response Generation using Emotion Feature Extraction. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :255–262.
Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. However, the previous emotional response generation model cannot understand the subtle part of emotions, because this model use the desired emotion of response as a token form. Moreover, this model is difficult to generate natural responses related to input utterance at the content level, since the information of input utterance can be biased to the emotion token. To overcome these limitations, we propose an emotional response generation model which generates emotional and natural responses by using the emotion feature extraction. Our model consists of two parts: Extraction part and Generation part. The extraction part is to extract the emotion of input utterance as a vector form by using the pre-trained LSTM based classification model. The generation part is to generate an emotional and natural response to the input utterance by reflecting the emotion vector from the extraction part and the thought vector from the encoder. We evaluate our model on the emotion-labeled dialogue dataset: DailyDialog. We evaluate our model on quantitative analysis and qualitative analysis: emotion classification; response generation modeling; comparative study. In general, experiments show that the proposed model can generate emotional and natural responses.
Yang, Jian, Liu, Shoubao, Fang, Yuan, Xiong, Zhonghao, Li, Xin.  2020.  A simulation calculation method for suppressing the magnetizing inrush current in the setting of the overcurrent protection of the connecting transformer in the hydropower station. 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :197–202.
In order to improve the reliability of power supply in adjacent hydropower stations, the auxiliary power systems of the two stations are connected through a contact transformer. The magnetizing inrush current generated by the connecting transformer of a hydropower station has the characteristics of high frequency, strong energy, and multi-coupling. The harm caused by the connecting transformer is huge. In order to prevent misoperation during the closing process of the connecting transformer, this article aims at the problem of setting the switching current of the connecting transformer of the two hydropower stations, and establishes the analysis model of the excitation inrush current with SimPowerSystem software, and carries out the quantitative simulation calculation of the excitation inrush current of the connecting transformer. A setting strategy for overcurrent protection of tie transformers to suppress the excitation inrush current is proposed. Under the conditions of changing switch closing time, generator load, auxiliary transformer load, tie transformer core remanence, the maximum amplitude of the excitation inrush current is comprehensively judged Value, and then achieve the suppression of the excitation inrush current, and accurately determine the protection setting of the switch.
Hu, Dongfang, Xu, Bin, Wang, Jun, Han, Linfeng, Liu, Jiayi.  2020.  A Shilling Attack Model Based on TextCNN. 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). :282–289.
With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index.
Chen, Congwei, Elsayed, Marwa A., Zulkernine, Mohammad.  2020.  HBD-Authority: Streaming Access Control Model for Hadoop. 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys). :16–25.
Big data analytics, in essence, is becoming the revolution of business intelligence around the world. This momentum has given rise to the hype around analytic technologies, including Apache Hadoop. Hadoop was not originally developed with security in mind. Despite the evolving efforts to integrate security in Hadoop through developing new tools (e.g., Apache Sentry and Ranger) and employing traditional mechanisms (e.g., Kerberos and LDAP), they mainly focus on providing encryption and authentication features, albeit with limited authorization support. Existing solutions in the literature extended these evolving efforts. However, they suffer from limitations, hindering them from providing robust authorization that effectively meets the unique requirements of big data environments. Towards covering this gap, this paper proposes a hybrid authority (HBD-Authority) as a formal attribute-based access control model with context support. This model is established on a novel hybrid approach of authorization transparency that pertains to three fundamental properties of accuracy: correctness, security, and completeness. The model leverages streaming data analytics to foster distributed parallel processing capabilities that achieve multifold benefits: a) efficiently managing the security policies and promptly updating the privileges assigned to a high number of users interacting with the analytic services; b) swiftly deciding and enforcing authorization of requests over data characterized by the 5Vs; and c) providing dynamic protection for data which is frequently updated. The implementation details and experimental evaluation of the proposed model are presented, demonstrating its performance efficiency.
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.
Abdul Basit Ur Rahim, Muhammad, Duan, Qi, Al-Shaer, Ehab.  2020.  A Formal Analysis of Moving Target Defense. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1802—1807.
Static system configuration provides a significant advantage for the adversaries to discover the assets and launch attacks. Configuration-based moving target defense (MTD) reverses the cyber warfare asymmetry by mutating certain configuration parameters to disrupt the attack planning or increase the attack cost significantly. In this research, we present a methodology for the formal verification of MTD techniques. We formally modeled MTD techniques and verified them against constraints. We use Random Host Mutation (RHM) as a case study for MTD formal verification. The RHM transparently mutates the IP addresses of end-hosts and turns into untraceable moving targets. We apply the formal methodology to verify the correctness, safety, mutation, mutation quality, and deadlock-freeness of RHM using the model checking tool. An adversary is also modeled to validate the effectiveness of the MTD technique. Our experimentation validates the scalability and feasibility of the formal verification methodology.
S, Kanthimathi, Prathuri, Jhansi Rani.  2020.  Classification of Misbehaving nodes in MANETS using Machine Learning Techniques. 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–2.
Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.
Ruiz-Martin, Cristina, Wainer, Gabriel, Lopez-Paredes, Adolfo.  2020.  Studying Communications Resiliency in Emergency Plans. 2020 Spring Simulation Conference (SpringSim). :1–12.
Recent disasters have shown that hazards can be unpredictable and can have catastrophic consequences. Emergency plans are key to dealing with these situations and communications play a key role in emergency management. In this paper, we provide a formalism to design resilient emergency plans in terms of communications. We exemplify how to use the formalism using a case study of a Nuclear Emergency Plan.
Basu, Prithwish, Salonidis, Theodoros, Kraczek, Brent, Saghaian, Sayed M., Sydney, Ali, Ko, Bongjun, La Porta, Tom, Chan, Kevin.  2020.  Decentralized placement of data and analytics in wireless networks for energy-efficient execution. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :486—495.
We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints. We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(·) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration. Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.
Fan, Xiaosong.  2020.  Analysis of the Design of Digital Video Security Monitoring System Based on Bee Population Optimization Algorithm. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :339–342.
With the concept of “wireless city”, 3G, WIFI and other wireless network coverages have become more extensive. Data transmission rate has achieved a qualitative leap, providing feasibility for the implementation of mobile video surveillance solutions. The mobile video monitoring system based on the bee population optimization algorithm proposed in this paper makes up for the defects of traditional network video surveillance, and according to the video surveillance system monitoring command, the optimal visual effect of the current state of the observed object can be rendered quickly and steadily through the optimization of the camera linkage model and simulation analysis.
Yao, Xiaoyong, Pei, Yuwen, Wu, Pingdong, Huang, Man-ling.  2020.  Study on Integrative Control between the Stereoscopic Image and the Tactile Feedback in Augmented Reality. 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE). :177—180.
The precise integrative control between the stereoscopic image and the tactile feedback is very essential in augmented reality[1]-[4]. In order to study this question, this paper will introduce a stereoscopic-imaging and tactile integrative augmented-reality system, and a stereoscopic-imaging and tactile integrative algorithm. The system includes a stereoscopic-imaging part and a string-based tactile part. The integrative algorithm is used to precisely control the interaction between the two parts. The results for testing the system and the algorithm demonstrate the system to be perfect through 5 testers' operation and will be presented in the last part of the paper.
Gonçalves, Charles F., Menasche, Daniel S., Avritzer, Alberto, Antunes, Nuno, Vieira, Marco.  2020.  A Model-Based Approach to Anomaly Detection Trading Detection Time and False Alarm Rate. 2020 Mediterranean Communication and Computer Networking Conference (MedComNet). :1—8.
The complexity and ubiquity of modern computing systems is a fertile ground for anomalies, including security and privacy breaches. In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. Specifically, it is challenging to define normal behavior comprehensively and to acquire data on anomalies in diverse cloud environments. To tackle those challenges, we focus on anomaly detection approaches based on system performance signatures. In particular, performance signatures have the potential of detecting zero-day attacks, as those approaches are based on detecting performance deviations and do not require detailed knowledge of attack history. The proposed methodology leverages an analytical performance model and experimentation, and allows to control the rate of false positives in a principled manner. The methodology is evaluated using the TPCx-V workload, which was profiled during a set of executions using resource exhaustion anomalies that emulate the effects of anomalies affecting system performance. The proposed approach was able to successfully detect the anomalies, with a low number of false positives (precision 90%-98%).
Reijsbergen, Daniël, Anh Dinh, Tien Tuan.  2020.  On Exploiting Transaction Concurrency To Speed Up Blockchains. 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS). :1044—1054.
Consensus protocols are currently the bottlenecks that prevent blockchain systems from scaling. However, we argue that transaction execution is also important to the performance and security of blockchains. In other words, there are ample opportunities to speed up and further secure blockchains by reducing the cost of transaction execution. Our goal is to understand how much we can speed up blockchains by exploiting transaction concurrency available in blockchain workloads. To this end, we first analyze historical data of seven major public blockchains, namely Bitcoin, Bitcoin Cash, Litecoin, Dogecoin, Ethereum, Ethereum Classic, and Zilliqa. We consider two metrics for concurrency, namely the single-transaction conflict rate per block, and the group conflict rate per block. We find that there is more concurrency in UTXO-based blockchains than in account-based ones, although the amount of concurrency in the former is lower than expected. Another interesting finding is that some blockchains with larger blocks have more concurrency than blockchains with smaller blocks. Next, we propose an analytical model for estimating the transaction execution speed-up given an amount of concurrency. Using results from our empirical analysis, the model estimates that 6× speed-ups in Ethereum can be achieved if all available concurrency is exploited.
Gu, Yanyang, Zhang, Ping, Chen, Zhifeng, Cao, Fei.  2020.  UEFI Trusted Computing Vulnerability Analysis Based on State Transition Graph. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :1043–1052.
In the face of increasingly serious firmware attacks, it is of great significance to analyze the vulnerability security of UEFI. This paper first introduces the commonly used trusted authentication mechanisms of UEFI. Then, aiming at the loopholes in the process of UEFI trust verification in the startup phase, combined with the state transition diagram, PageRank algorithm and Bayesian network theory, the analysis model of UEFI trust verification startup vulnerability is constructed. And according to the example to verify the analysis. Through the verification and analysis of the data obtained, the vulnerable attack paths and key vulnerable nodes are found. Finally, according to the analysis results, security enhancement measures for UEFI are proposed.
Ouchani, Samir, Khebbeb, Khaled, Hafsi, Meriem.  2020.  Towards Enhancing Security and Resilience in CPS: A Coq-Maude based Approach. 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). :1—6.
Cyber-Physical Systems (CPS) have gained considerable interest in the last decade from both industry and academia. Such systems have proven particularly complex and provide considerable challenges to master their design and ensure their functionalities. In this paper, we intend to tackle some of these challenges related to the security and the resilience of CPS at the design level. We initiate a CPS modeling approach to specify such systems structure and behaviors, analyze their inherent properties and to overcome threats in terms of security and correctness. In this initiative, we consider a CPS as a network of entities that communicate through physical and logical channels, and which purpose is to achieve a set of tasks expressed as an ordered tree. Our modeling approach proposes a combination of the Coq theorem prover and the Maude rewriting system to ensure the soundness and correctness of CPS design. The introduced solution is illustrated through an automobile manufacturing case study.
Zhu, Hong, Xia, Bing, Zhou, Dongxu, Zhang, Ming, Ma, Zhoujun.  2020.  Research on Integrated Model and Interactive Influence of Energy Internet Cyber Physical System. 2020 IEEE Sustainable Power and Energy Conference (iSPEC). :1667–1671.

Energy Internet is a typical cyber-physical system (CPS), in which the disturbance on cyber part may result in the operation risks on the physical part. In order to perform CPS assessment and research the interactive influence between cyber part and physical part, an integrated energy internet CPS model which adopts information flow matrix, energy control flow matrix and information energy hybrid flow matrix is proposed in this paper. The proposed model has a higher computational efficacy compared with simulation based approaches. Then, based on the proposed model, the influence of cyber disturbances such as data dislocation, data delay and data error on the physical part are studied. Finally, a 3 MW PET based energy internet CPS is built using PSCAD/EMTDC software. The simulation results prove the validity of the proposed model and the correctness of the interactive influence analysis.

Neema, Himanshu, Sztipanovits, Janos, Hess, David J., Lee, Dasom.  2020.  TE-SAT: Transactive Energy Simulation and Analysis Toolsuite. 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION). :19—20.

Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.

Hong, Tang, Ju, Tailiang, Li, Yao.  2020.  Address Collision Attacks on ECSM Protected by ADPA. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :235—239.

Using the physical characteristics of the encryption device, an attacker can more easily obtain the key, which is called side-channel attack. Common side-channel attacks, such as simple power analysis (SPA) and differential power analysis (DPA), mainly focus on the statistical analysis of the data involved in the encryption algorithm, while there are relatively few studies on the Hamming weight of the addresses. Therefore, a new method of address-based Hamming weight analysis, address collision attack, is proposed in this research. The collision attack method (CA) and support vector machines algorithm (SVM) are used for analysis, meanwhile, the scalar multiplication implemented by protected address-bit DPA (ADPA) can be attack on the ChipWhisperer-Pro CW1200.

Kishore, Pushkar, Barisal, Swadhin Kumar, Prasad Mohapatra, Durga.  2020.  JavaScript malware behaviour analysis and detection using sandbox assisted ensemble model. 2020 IEEE REGION 10 CONFERENCE (TENCON). :864—869.

Whenever any internet user visits a website, a scripting language runs in the background known as JavaScript. The embedding of malicious activities within the script poses a great threat to the cyberworld. Attackers take advantage of the dynamic nature of the JavaScript and embed malicious code within the website to download malware and damage the host. JavaScript developers obfuscate the script to keep it shielded from getting detected by the malware detectors. In this paper, we propose a novel technique for analysing and detecting JavaScript using sandbox assisted ensemble model. We extract the payload using malware-jail sandbox to get the real script. Upon getting the extracted script, we analyse it to define the features that are needed for creating the dataset. We compute Pearson's r between every feature for feature extraction. An ensemble model consisting of Sequential Minimal Optimization (SMO), Voted Perceptron and AdaBoost algorithm is used with voting technique to detect malicious JavaScript. Experimental results show that our proposed model can detect obfuscated and de-obfuscated malicious JavaScript with an accuracy of 99.6% and 0.03s detection time. Our model performs better than other state-of-the-art models in terms of accuracy and least training and detection time.

Lu, Xinjin, Lei, Jing, Li, Wei.  2020.  A Physical Layer Encryption Algorithm Based on Length-Compatible Polar Codes. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1—7.
The code length and rate of length-compatible polar codes can be adaptively adjusted and changed because of the special coding structure. In this paper, we propose a method to construct length-compatible polar codes by employing physical layer encryption technology. The deletion way of frozen bits and generator matrix are random, which makes polar codes more flexible and safe. Simulation analysis shows that the proposed algorithm can not only effectively improve the performance of length-compatible polar codes but also realize the physical layer security encryption of the system.