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2020-06-04
Asiri, Somayah, Alzahrani, Ahmad A..  2019.  The Effectiveness of Mixed Reality Environment-Based Hand Gestures in Distributed Collaboration. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1—6.

Mixed reality (MR) technologies are widely used in distributed collaborative learning scenarios and have made learning and training more flexible and intuitive. However, there are many challenges in the use of MR due to the difficulty in creating a physical presence, particularly when a physical task is being performed collaboratively. We therefore developed a novel MR system to overcomes these limitations and enhance the distributed collaboration user experience. The primary objective of this paper is to explore the potential of a MR-based hand gestures system to enhance the conceptual architecture of MR in terms of both visualization and interaction in distributed collaboration. We propose a synchronous prototype named MRCollab as an immersive collaborative approach that allows two or more users to communicate with a peer based on the integration of several technologies such as video, audio, and hand gestures.

Briggs, Shannon, Perrone, Michael, Peveler, Matthew, Drozdal, Jaimie, Balagyozyan, Lilit, Su, Hui.  2019.  Multimodal, Multiuser Immersive Brainstorming and Scenario Planning for Intelligence Analysis. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). :1—4.

This paper discusses two pieces of software designed for intelligence analysis, the brainstorming tool and the Scenario Planning Advisor. These tools were developed in the Cognitive Immersive Systems Lab (CISL) in conjunction with IBM. We discuss the immersive environment the tools are situated in, and the proposed benefit for intelligence analysis.

2020-05-26
Satav, Pravin R, Jawandhiya, Pradeep M., Thakare, Vilas M..  2018.  Secure Route Selection Mechanism in the Presence of Black Hole Attack with AOMDV Routing Algorithm. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). :1–6.
The research in MANET has been carried out for the development of various techniques which will increase the competency of the network only. A plenty number of proposed routing protocols are magnificent in terms of efficiency. However, proposed protocols were generally fulfilling the set of trusted network and not considered for adversarial network setting, hence there is no security mechanism has been considered. MANET is widely used in sensitive fields like battlefield, police rescue operation and many more in such type of sensitive field an attacker may try to gather information about the conversation starting from the origin node to the terminal node. Secure route selection approach for route selection in adverse environment is discussed in this article. The results shows that proposed algorithm, will resolve the single & collaborative attack by increasing the computational & storage overhead and by improving the significant PDR, achieves a noticeable enhancement in the end to end delay.
V S, Deepthi, S, Vagdevi.  2018.  Behaviour Analysis and Detection of Blackhole Attacker Node under Reactive Routing Protocol in MANETs. 2018 International Conference on Networking, Embedded and Wireless Systems (ICNEWS). :1–5.
Mobile Adhoc networks are wireless adhoc networks that have property of self organizing, less infrastructure, multi hoping, which are designed to work under low power vulnerable environment. Due to its very unique characteristics, there is much chances of threat of malicious nodes within the network. Blackhole attack is a menace in MANETs which redirects all traffic to itself and drops it. This paper’s objective is to analyze the effects of blackhole attack under reactive routing protocol such as Adhoc on Demand Distance Vector routing (AODV). The performance of this protocol is assessed to find the vulnerability of attack and also compared the impact of attack on both AODV, AODV with blackhole and proposed AODV protocols. The analysis is done by simulated using NS- 2.35 and QoS parameters such as Throughput, PDR, and Average Energy Consumed are measured further.
2020-05-08
Vigneswaran, Rahul K., Vinayakumar, R., Soman, K.P., Poornachandran, Prabaharan.  2018.  Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—6.
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-`99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Huang, Yifan, Chung, Wingyan, Tang, Xinlin.  2018.  A Temporal Recurrent Neural Network Approach to Detecting Market Anomaly Attacks. 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). :160—162.
In recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. Market Anomaly Attacks is an illegal practice in the stock or commodities markets that induces investors to make purchase or sale decisions based on false information. Identifying these threats from noisy social media datasets remains challenging because of the long time sequence in these social media postings, ambiguous textual context and the difficulties for traditional deep learning approaches to handle both temporal and text dependent data such as financial social media messages. This research developed a temporal recurrent neural network (TRNN) approach to capturing both time and text sequence dependencies for intelligent detection of market anomalies. We tested the approach by using financial social media of U.S. technology companies and their stock returns. Compared with traditional neural network approaches, TRNN was found to more efficiently and effectively classify abnormal returns.
Shen, Weiguo, Wang, Wei.  2018.  Node Identification in Wireless Network Based on Convolutional Neural Network. 2018 14th International Conference on Computational Intelligence and Security (CIS). :238—241.
Aiming at the problem of node identification in wireless networks, a method of node identification based on deep learning is proposed, which starts with the tiny features of nodes in radiofrequency layer. Firstly, in order to cut down the computational complexity, Principal Component Analysis is used to reduce the dimension of node sample data. Secondly, a convolution neural network containing two hidden layers is designed to extract local features of the preprocessed data. Stochastic gradient descent method is used to optimize the parameters, and the Softmax Model is used to determine the output label. Finally, the effectiveness of the method is verified by experiments on practical wireless ad-hoc network.
Niemiec, Marcin, Mehic, Miralem, Voznak, Miroslav.  2018.  Security Verification of Artificial Neural Networks Used to Error Correction in Quantum Cryptography. 2018 26th Telecommunications Forum (℡FOR). :1—4.
Error correction in quantum cryptography based on artificial neural networks is a new and promising solution. In this paper the security verification of this method is discussed and results of many simulations with different parameters are presented. The test scenarios assumed partially synchronized neural networks, typical for error rates in quantum cryptography. The results were also compared with scenarios based on the neural networks with random chosen weights to show the difficulty of passive attacks.
Katasev, Alexey S., Emaletdinova, Lilia Yu., Kataseva, Dina V..  2018.  Neural Network Spam Filtering Technology. 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.
In this paper we solve the problem of neural network technology development for e-mail messages classification. We analyze basic methods of spam filtering such as a sender IP-address analysis, spam messages repeats detection and the Bayesian filtering according to words. We offer the neural network technology for solving this problem because the neural networks are universal approximators and effective in addressing the problems of classification. Also, we offer the scheme of this technology for e-mail messages “spam”/“not spam” classification. The creation of effective neural network model of spam filtering is performed within the databases knowledge discovery technology. For this training set is formed, the neural network model is trained, its value and classifying ability are estimated. The experimental studies have shown that a developed artificial neural network model is adequate and it can be effectively used for the e-mail messages classification. Thus, in this paper we have shown the possibility of the effective neural network model use for the e-mail messages filtration and have shown a scheme of artificial neural network model use as a part of the e-mail spam filtering intellectual system.
Su, Chunmei, Li, Yonggang, Mao, Wen, Hu, Shangcheng.  2018.  Information Network Risk Assessment Based on AHP and Neural Network. 2018 10th International Conference on Communication Software and Networks (ICCSN). :227—231.
This paper analyzes information network security risk assessment methods and models. Firstly an improved AHP method is proposed to assign the value of assets for solving the problem of risk judgment matrix consistency effectively. And then the neural network technology is proposed to construct the neural network model corresponding to the risk judgment matrix for evaluating the individual risk of assets objectively, the methods for calculating the asset risk value and system risk value are given. Finally some application results are given. Practice proves that the methods are correct and effective, which has been used in information network security risk assessment application and offers a good foundation for the implementation of the automatic assessment.
Zhang, Xu, Ye, Zhiwei, Yan, Lingyu, Wang, Chunzhi, Wang, Ruoxi.  2018.  Security Situation Prediction based on Hybrid Rice Optimization Algorithm and Back Propagation Neural Network. 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). :73—77.
Research on network security situation awareness is currently a research hotspot in the field of network security. It is one of the easiest and most effective methods to use the BP neural network for security situation prediction. However, there are still some problems in BP neural network, such as slow convergence rate, easy to fall into local extremum, etc. On the other hand, some common used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), easily fall into local optimum. Hybrid rice optimization algorithm is a newly proposed algorithm with strong search ability, so the method of this paper is proposed. This article describes in detail the use of BP network security posture prediction method. In the proposed method, HRO is used to train the connection weights of the BP network. Through the advantages of HRO global search and fast convergence, the future security situation of the network is predicted, and the accuracy of the situation prediction is effectively improved.
Katasev, Alexey S., Emaletdinova, Lilia Yu., Kataseva, Dina V..  2018.  Neural Network Model for Information Security Incident Forecasting. 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.
This paper describes the technology of neural network application to solve the problem of information security incidents forecasting. We describe the general problem of analyzing and predicting time series in a graphical and mathematical setting. To solve this problem, it is proposed to use a neural network model. To solve the task of forecasting a time series of information security incidents, data are generated and described on the basis of which the neural network is trained. We offer a neural network structure, train the neural network, estimate it's adequacy and forecasting ability. We show the possibility of effective use of a neural network model as a part of an intelligent forecasting system.
Zhang, Shaobo, Shen, Yongjun, Zhang, Guidong.  2018.  Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :426—429.
Network situation value is an important index to measure network security. Establishing an effective network situation prediction model can prevent the occurrence of network security incidents, and plays an important role in network security protection. Through the understanding and analysis of the network security situation, we can see that there are many factors affecting the network security situation, and the relationship between these factors is complex., it is difficult to establish more accurate mathematical expressions to describe the network situation. Therefore, this paper uses the grey neural network as the prediction model, but because the convergence speed of the grey neural network is very fast, the network is easy to fall into local optimum, and the parameters can not be further modified, so the Multi-Swarm Chaotic Particle Optimization (MSCPO)is used to optimize the key parameters of the grey neural network. By establishing the nonlinear mapping relationship between the influencing factors and the network security situation, the network situation can be predicted and protected.
CUI, A-jun, Li, Chen, WANG, Xiao-ming.  2019.  Real-Time Early Warning of Network Security Threats Based on Improved Ant Colony Algorithm. 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). :309—316.
In order to better ensure the operation safety of the network, the real-time early warning of network security threats is studied based on the improved ant colony algorithm. Firstly, the network security threat perception algorithm is optimized based on the principle of neural network, and the network security threat detection process is standardized according to the optimized algorithm. Finally, the real-time early warning of network security threats is realized. Finally, the experiment proves that the network security threat real-time warning based on the improved ant colony algorithm has better security and stability than the traditional warning methods, and fully meets the research requirements.
Hafeez, Azeem, Topolovec, Kenneth, Awad, Selim.  2019.  ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks. 2019 15th International Computer Engineering Conference (ICENCO). :29—38.
Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection.
Lavrova, Daria, Zegzhda, Dmitry, Yarmak, Anastasiia.  2019.  Using GRU neural network for cyber-attack detection in automated process control systems. 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1—3.
This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.
Dionísio, Nuno, Alves, Fernando, Ferreira, Pedro M., Bessani, Alysson.  2019.  Cyberthreat Detection from Twitter using Deep Neural Networks. 2019 International Joint Conference on Neural Networks (IJCNN). :1—8.
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.
Wu, Peilun, Guo, Hui.  2019.  LuNet: A Deep Neural Network for Network Intrusion Detection. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). :617—624.
Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
Saraswat, Pavi, Garg, Kanika, Tripathi, Rajan, Agarwal, Ayush.  2019.  Encryption Algorithm Based on Neural Network. 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). :1—5.
Security is one of the most important needs in network communication. Cryptography is a science which involves two techniques encryption and decryption and it basically enables to send sensitive and confidential data over the unsecure network. The basic idea of cryptography is concealing of the data from unauthenticated users as they can misuse the data. In this paper we use auto associative neural network concept of soft computing in combination with encryption technique to send data securely on communication network.
Chaudhary, Anshika, Mittal, Himangi, Arora, Anuja.  2019.  Anomaly Detection using Graph Neural Networks. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :346—350.
Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.
Fu, Tian, Lu, Yiqin, Zhen, Wang.  2019.  APT Attack Situation Assessment Model Based on optimized BP Neural Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :2108—2111.
In this paper, it first analyzed the characteristics of Advanced Persistent Threat (APT). according to APT attack, this paper established an BP neural network optimized by improved adaptive genetic algorithm to predict the security risk of nodes in the network. and calculated the path of APT attacks with the maximum possible attack. Finally, experiments verify the effectiveness and correctness of the algorithm by simulating attacks. Experiments show that this model can effectively evaluate the security situation in the network, For the defenders to adopt effective measures defend against APT attacks, thus improving the security of the network.
Wang, Dongqi, Shuai, Xuanyue, Hu, Xueqiong, Zhu, Li.  2019.  Research on Computer Network Security Evaluation Method Based on Levenberg-Marquardt Algorithms. 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). :399—402.
As we all know, computer network security evaluation is an important link in the field of network security. Traditional computer network security evaluation methods use BP neural network combined with network security standards to train and simulate. However, because BP neural network is easy to fall into local minimum point in the training process, the evalu-ation results are often inaccurate. In this paper, the LM (Levenberg-Marquard) algorithm is used to optimize the BP neural network. The LM-BP algorithm is constructed and applied to the computer network security evaluation. The results show that compared with the traditional evaluation algorithm, the optimized neural network has the advantages of fast running speed and accurate evaluation results.
Guan, Chengli, Yang, Yue.  2019.  Research of Computer Network Security Evaluation Based on Backpropagation Neural Network. 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :181—184.
In recent years, due to the invasion of virus and loopholes, computer networks in colleges and universities have caused great adverse effects on schools, teachers and students. In order to improve the accuracy of computer network security evaluation, Back Propagation (BP) neural network was trained and built. The evaluation index and target expectations have been determined based on the expert system, with 15 secondary evaluation index values taken as input layer parameters, and the computer network security evaluation level values taken as output layer parameter. All data were divided into learning sample sets and forecasting sample sets. The results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.000999654. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.98723. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the computer network security evaluation.
2020-04-03
Zhao, Hui, Li, Zhihui, Wei, Hansheng, Shi, Jianqi, Huang, Yanhong.  2019.  SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective. 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST). :59—67.

Industrial networks are the cornerstone of modern industrial control systems. Performing security checks of industrial communication processes helps detect unknown risks and vulnerabilities. Fuzz testing is a widely used method for performing security checks that takes advantage of automation. However, there is a big challenge to carry out security checks on industrial network due to the increasing variety and complexity of industrial communication protocols. In this case, existing approaches usually take a long time to model the protocol for generating test cases, which is labor-intensive and time-consuming. This becomes even worse when the target protocol is stateful. To help in addressing this problem, we employed a deep learning model to learn the structures of protocol frames and deal with the temporal features of stateful protocols. We propose a fuzzing framework named SeqFuzzer which automatically learns the protocol frame structures from communication traffic and generates fake but plausible messages as test cases. For proving the usability of our approach, we applied SeqFuzzer to widely-used Ethernet for Control Automation Technology (EtherCAT) devices and successfully detected several security vulnerabilities.

Lipp, Benjamin, Blanchet, Bruno, Bhargavan, Karthikeyan.  2019.  A Mechanised Cryptographic Proof of the WireGuard Virtual Private Network Protocol. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :231—246.

WireGuard is a free and open source Virtual Private Network (VPN) that aims to replace IPsec and OpenVPN. It is based on a new cryptographic protocol derived from the Noise Protocol Framework. This paper presents the first mechanised cryptographic proof of the protocol underlying WireGuard, using the CryptoVerif proof assistant. We analyse the entire WireGuard protocol as it is, including transport data messages, in an ACCE-style model. We contribute proofs for correctness, message secrecy, forward secrecy, mutual authentication, session uniqueness, and resistance against key compromise impersonation, identity mis-binding, and replay attacks. We also discuss the strength of the identity hiding provided by WireGuard. Our work also provides novel theoretical contributions that are reusable beyond WireGuard. First, we extend CryptoVerif to account for the absence of public key validation in popular Diffie-Hellman groups like Curve25519, which is used in many modern protocols including WireGuard. To our knowledge, this is the first mechanised cryptographic proof for any protocol employing such a precise model. Second, we prove several indifferentiability lemmas that are useful to simplify the proofs for sequences of key derivations.