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Zhou, Yimin, Zhang, Kai.  2020.  DoS Vulnerability Verification of IPSec VPN. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :698–702.
This paper analyzes the vulnerability in the process of key negotiation between the main mode and aggressive mode of IKEv1 protocol in IPSec VPN, and proposes a DOS attack method based on OSPF protocol adjacent route spoofing. The experiment verifies the insecurity of IPSec VPN using IKEv1 protocol. This attack method has the advantages of lower cost and easier operation compared with using botnet.
Vinarskii, Evgenii, Demakov, Alexey, Kamkin, Alexander, Yevtushenko, Nina.  2020.  Verifying cryptographic protocols by Tamarin Prover. 2020 Ivannikov Memorial Workshop (IVMEM). :69–75.
Cryptographic protocols are utilized for establishing a secure session between “honest” agents which communicate strictly according to the protocol rules as well as for ensuring the authenticated and confidential transmission of messages. The specification of a cryptographic protocol is usually presented as a set of requirements for the sequences of transmitted messages including the format of such messages. Note that protocol can describe several execution scenarios. All these requirements lead to a huge formal specification for a real cryptographic protocol and therefore, it is difficult to verify the security of the whole cryptographic protocol at once. In this paper, to overcome this problem, we suggest verifying the protocol security for its fragments. Namely, we verify the security properties for a special set of so-called traces of the cryptographic protocol. Intuitively, a trace of the cryptographic protocol is a sequence of computations, value checks, and transmissions on the sides of “honest” agents permitted by the protocol. In order to choose such set of traces, we introduce an Adversary model and the notion of a similarity relation for traces. We then verify the security properties of selected traces with Tamarin Prover. Experimental results for the EAP and Noise protocols clearly show that this approach can be promising for automatic verification of large protocols.
Hassan, Mehmood, Sultan, Aiman, Awan, Ali Afzal, Tahir, Shahzaib, Ihsan, Imran.  2020.  An Enhanced and Secure Multiserver-based User Authentication Protocol. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1–6.
The extensive use of the internet and web-based applications spot the multiserver authentication as a significant component. The users can get their services after authenticating with the service provider by using similar registration records. Various protocol schemes are developed for multiserver authentication, but the existing schemes are not secure and often lead towards various vulnerabilities and different security issues. Recently, Zhao et al. put forward a proposal for smart card and user's password-based authentication protocol for the multiserver environment and showed that their proposed protocol is efficient and secure against various security attacks. This paper points out that Zhao et al.'s authentication scheme is susceptive to traceability as well as anonymity attacks. Thus, it is not feasible for the multiserver environment. Furthermore, in their scheme, it is observed that a user while authenticating does not send any information with any mention of specific server identity. Therefore, this paper proposes an enhanced, efficient and secure user authentication scheme for use in any multiserver environment. The formal security analysis and verification of the protocol is performed using state-of-the-art tool “ProVerif” yielding that the proposed scheme provides higher levels of security.
Kai, Wang, Wei, Li, Tao, Chen, Longmei, Nan.  2020.  Research on Secure JTAG Debugging Model Based on Schnorr Identity Authentication Protocol. 2020 IEEE 15th International Conference on Solid-State Integrated Circuit Technology (ICSICT). :1–3.
As a general interface for chip system testing and on-chip debugging, JTAG is facing serious security threats. By analyzing the typical JTAG attack model and security protection measures, this paper designs a secure JTAG debugging model based on Schnorr identity authentication protocol, and takes RISCV as an example to build a set of SoC prototype system to complete functional verification. Experiments show that this secure JTAG debugging model has high security, flexible implementation, and good portability. It can meet the JTAG security protection requirements in various application scenarios. The maximum clock frequency can reach 833MHZ, while the hardware overhead is only 47.93KGate.
Li, Yongjian, Cao, Taifeng, Jansen, David N., Pang, Jun, Wei, Xiaotao.  2020.  Accelerated Verification of Parametric Protocols with Decision Trees. 2020 IEEE 38th International Conference on Computer Design (ICCD). :397–404.
Within a framework for verifying parametric network protocols through induction, one needs to find invariants based on a protocol instance of a small number of nodes. In this paper, we propose a new approach to accelerate parameterized verification by adopting decision trees to represent the state space of a protocol instance. Such trees can be considered as a knowledge base that summarizes all behaviors of the protocol instance. With this knowledge base, we are able to efficiently construct an oracle to effectively assess candidates of invariants of the protocol, which are suggested by an invariant finder. With the discovered invariants, a formal proof for the correctness of the protocol can be derived in the framework after proper generalization. The effectiveness of our method is demonstrated by experiments with typical benchmarks.
He, Leifeng, Liu, Guanjun.  2020.  Petri Nets Based Verification of Epistemic Logic and Its Application on Protocols of Privacy and Security. 2020 IEEE World Congress on Services (SERVICES). :25–28.
Epistemic logic can specify many design requirements of privacy and security of multi-agent systems (MAS). The existing model checkers of epistemic logic use some programming languages to describe MAS, induce Kripke models as the behavioral representation of MAS, apply Ordered Binary Decision Diagrams (OBDD) to encode Kripke models to solve their state explosion problem and verify epistemic logic based on the encoded Kripke models. However, these programming languages are usually non-intuitive. More seriously, their OBDD-based model checking processes are often time-consuming due to their dynamic variable ordering for OBDD. Therefore, we define Knowledge-oriented Petri Nets (KPN) to intuitively describe MAS, induce similar reachability graphs as the behavioral representation of KPN, apply OBDD to encode all reachable states, and finally verify epistemic logic. Although we also use OBDD, we adopt a heuristic method for the computation of a static variable order instead of dynamic variable ordering. More importantly, while verifying an epistemic formula, we dynamically generate its needed similar relations, which makes our model checking process much more efficient. In this paper, we introduce our work.
Remlein, Piotr, Rogacki, Mikołaj, Stachowiak, Urszula.  2020.  Tamarin software – the tool for protocols verification security. 2020 Baltic URSI Symposium (URSI). :118–123.
In order to develop safety-reliable standards for IoT (Internet of Things) networks, appropriate tools for their verification are needed. Among them there is a group of tools based on automated symbolic analysis. Such a tool is Tamarin software. Its usage for creating formal proofs of security protocols correctness has been presented in this paper using the simple example of an exchange of messages with asynchronous encryption between two agents. This model can be used in sensor networks or IoT e.g. in TLS protocol to provide a mechanism for secure cryptographic key exchange.
Naveed, Sarah, Sultan, Aiman, Mansoor, Khwaja.  2020.  An Enhanced SIP Authentication Protocol for Preserving User Privacy. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1–6.
Owing to the advancements in communication media and devices all over the globe, there has arisen a dire need for to limit the alarming number of attacks targeting these and to enhance their security. Multiple techniques have been incorporated in different researches and various protocols and schemes have been put forward to cater security issues of session initiation protocol (SIP). In 2008, Qiu et al. presented a proposal for SIP authentication which while effective than many existing schemes, was still found vulnerable to many security attacks. To overcome those issues, Zhang et al. proposed an authentication protocol. This paper presents the analysis of Zhang et al. authentication scheme and concludes that their proposed scheme is susceptible to user traceablity. It also presents an improved SIP authentication scheme that eliminates the possibility of traceability of user's activities. The proposed scheme is also verified by contemporary verification tool, ProVerif and it is found to be more secure, efficient and practical than many similar SIP authetication scheme.
Latif, Shahid, Idrees, Zeba, Zou, Zhuo, Ahmad, Jawad.  2020.  DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT. 2020 International Conference on UK-China Emerging Technologies (UCET). :1–4.
Industrial Internet of Things (IIoT) has arisen as an emerging trend in the industrial sector. Millions of sensors present in IIoT networks generate a massive amount of data that can open the doors for several cyber-attacks. An intrusion detection system (IDS) monitors real-time internet traffic and identify the behavior and type of network attacks. In this paper, we presented a deep random neural (DRaNN) based scheme for intrusion detection in IIoT. The proposed scheme is evaluated by using a new generation IIoT security dataset UNSW-NB15. Experimental results prove that the proposed model successfully classified nine different types of attacks with a low false-positive rate and great accuracy of 99.54%. To validate the feasibility of the proposed scheme, experimental results are also compared with state-of-the-art deep learning-based intrusion detection schemes. The proposed model achieved a higher attack detection rate of 99.41%.
Desnitsky, Vasily A., Kotenko, Igor V., Parashchuk, Igor B..  2020.  Neural Network Based Classification of Attacks on Wireless Sensor Networks. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :284–287.
The paper proposes a method for solving problems of classifying multi-step attacks on wireless sensor networks in the conditions of uncertainty (incompleteness and inconsistency) of the observed signs of attacks. The method aims to eliminate the uncertainty of classification of attacks on networks of this class one the base of the use of neural network approaches to the processing of incomplete and contradictory knowledge on possible attack characteristics. It allows increasing objectivity (accuracy and reliability) of information security monitoring in modern software and hardware systems and Internet of Things networks that actively exploit advantages of wireless sensor networks.
Wang, Wei, Liu, Tieyuan, Chang, Liang, Gu, Tianlong, Zhao, Xuemei.  2020.  Convolutional Recurrent Neural Networks for Knowledge Tracing. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :287–290.
Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian Knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent Knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets.
Ren, Xun-yi, Luo, Qi-qi, Shi, Chen, Huang, Jia-ming.  2020.  Network Security Posture Prediction Based on SAPSO-Elman Neural Networks. 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). :533–537.
With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.
Pamukov, Marin, Poulkov, Vladimir, Shterev, Vasil.  2020.  NSNN Algorithm Performance with Different Neural Network Architectures. 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). :280–284.
Internet of Things (IoT) development and the addition of billions of computationally limited devices prohibit the use of classical security measures such as Intrusion Detection Systems (IDS). In this paper, we study the influence of the implementation of different feed-forward type of Neural Networks (NNs) on the detection Rate of the Negative Selection Neural Network (NSNN) algorithm. Feed-forward and cascade forward NN structures with different number of neurons and different number of hidden layers are tested. For training and testing the NSNN algorithm the labeled KDD NSL dataset is applied. The detection rates provided by the algorithm with several NN structures to determine the optimal solution are calculated and compared. The results show how these different feed-forward based NN architectures impact the performance of the NSNN algorithm.
Peng, Cheng, Yongli, Wang, Boyi, Yao, Yuanyuan, Huang, Jiazhong, Lu, Qiao, Peng.  2020.  Cyber Security Situational Awareness Jointly Utilizing Ball K-Means and RBF Neural Networks. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :261–265.
Low accuracy and slow speed of predictions for cyber security situational awareness. This paper proposes a network security situational awareness model based on accelerated accurate k-means radial basis function (RBF) neural network, the model uses the ball k-means clustering algorithm to cluster the input samples, to get the nodes of the hidden layer of the RBF neural network, speeding up the selection of the initial center point of the RBF neural network, and optimize the parameters of the RBF neural network structure. Finally, use the training data set to train the neural network, using the test data set to test the accuracy of this neural network structure, the results show that this method has a greater improvement in training speed and accuracy than other neural networks.
Mahmoud, Loreen, Praveen, Raja.  2020.  Network Security Evaluation Using Deep Neural Network. 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST). :1–4.
One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.
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
Christie V, Samuel H., Smirnova, Daria, Chopra, Amit K., Singh, Munindar P..  2020.  Protocols Over Things: A Decentralized Programming Model for the Internet of Things. 53:60–68.
Current programming models for developing Internet of Things (IoT) applications are logically centralized and ill-suited for most IoT applications. We contribute Protocols over Things, a decentralized programming model that represents an IoT application via a protocol between the parties involved and provides improved performance over network-level delivery guarantees.
Shehada, Dina, Gawanmeh, Amjad, Fachkha, Claude, Damis, Haitham Abu.  2020.  Performance Evaluation of a Lightweight IoT Authentication Protocol. 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). :1–4.
Ensuring security to IoT devices is important in order to provide privacy and quality of services. Proposing a security solution is considered an important step towards achieving protection, however, proving the soundness of the solution is also crucial. In this paper, we propose a methodology for the performance evaluation of lightweight IoT-based authentication protocols based on execution time. Then, a formal verification test is conducted on a lightweight protocol proposed in the literature. The formal verification test conducted with Scyther tool proofs that the model provides mutual authentication, authorization, integrity, confidentiality, non-repudiation, and accountability. The protocol also was proven to provide protection from various attacks.
Wang, Zicheng, Cui, Bo.  2020.  An Enhanced System for Smart Home in IPv6-Based Wireless Home Network. 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). :119–122.
The development of IPv6-based wireless local area networks is becoming increasingly mature, and it has defined no less than different standards to meet the needs of different applications. Wireless home networks are widely used because they can be seamlessly connected to daily life, especially smart home devices linked to it. There are certain security issues with smart home devices deployed in wireless home networks, such as data tampering and leakage of sensitive information. This paper proposes a smart home management system based on IPv6 wireless home network, and develops a prototype system deployed on mobile portable devices. Through this system, different roles in the wireless home network can be dynamically authorized and smart home resources can be allocated to achieve the purpose of access control and management.
Daru, April Firman, Dwi Hartomo, Kristoko, Purnomo, Hindriyanto Dwi.  2020.  Internet of Things Wireless Attack Detection Conceptual Model Over IPv6 Network. 2020 International Seminar on Application for Technology of Information and Communication (iSemantic). :431–435.
Wireless network is an alternative communication to cable, where radio wave is used as transmission media instead of copper medium. However, wireless network more vulnerable to risk in security compared to cable network. Wireless network mostly used by Internet of Things node as communication media between nodes. Hence, these nodes exposed to risk of flooding attack from third party person. Hence, a system which capability to detect flooding attack at IoT node is required. Many researches have been done before, but most of the research only focus to IPv4 and signature-based detection. IPv6-based attacks undetectable by the current research, due to different datagram structure. This paper proposed a conceptual detection method with reinforcement learning algorithm to detect IPv6-based attack targeting IoT nodes. This reward will decide whether the detection system is good or not. The assessment calculation equation is used to turn reward-based score into detection accuracy.
Ul Rehman, Shafiq, Singh, Parminder, Manickam, Selvakumar, Praptodiyono, Supriyanto.  2020.  Towards Sustainable IoT Ecosystem. 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE). :135–138.
As the world is moving towards industry 4.0, it is estimated that in the near future billions of IoT devices will be interconnected over the Internet. The open and heterogeneous nature of IoT environment makes it vulnerable to adversarial attacks. To maintain sustainability in IoT ecosystem, this paper evaluates some of the recent IoT schemes based on key security features i.e. authentication, confidentiality, trust etc. These schemes are classified according to three-layer IoT architecture. Based on our findings, some of these solutions are applicable at physical layer while others are at network, and application layers. However, none of these schemes can provide end-to-end solution for IoT environment. Therefore, our work provides a roadmap for future research directions in IoT domain to design robust security schemes for IoT environment, thus can achieve sustainability in IoT ecosystem.
Praptodiyono, Supriyanto, Jauhari, Moh., Fahrizal, Rian, Hasbullah, Iznan H., Osman, Azlan, Ul Rehman, Shafiq.  2020.  Integration of Firewall and IDS on Securing Mobile IPv6. 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE). :163–168.
The number of Mobile device users in the word has evolved rapidly. Many internet users currently want to connect the internet for all utilities automatically. One of the technologies in the IPv6 network, which supports data access from moving users, is IPv6 Mobile protocol. In its mobility, the users on a range of networks can move the range to another network. High demand for this technology will interest to a hacker or a cracker to carry out an attack. One of them is a DoS attack that compromises a target to denial its services. A firewall is usually used to protect networks from external attacks. However, since the firewall based on the attacker database, the unknown may not be detected. In order to address the obstacle, a detection tool could be used. In this research, IDS as an intrusion detection tool was integrated with a firewall to be implemented in IPv6 Mobile to stop the DoS attack. The results of some experiments showed that the integration system could block the attack at 0.9 s in Correspondent Node and 1.2 s in Home Agent. The blocked attack can decrease the network throughput up to 27.44% when a Mobile Node in Home Agent, 28,87% when the Mobile Node in a Foreign Network. The final result of the blocked attack is reducing the average CPU utilization up to 30.99%.
Song, Guanglei, He, Lin, Wang, Zhiliang, Yang, Jiahai, Jin, Tao, Liu, Jieling, Li, Guo.  2020.  Towards the Construction of Global IPv6 Hitlist and Efficient Probing of IPv6 Address Space. 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS). :1–10.
Fast IPv4 scanning has made sufficient progress in network measurement and security research. However, it is infeasible to perform brute-force scanning of the IPv6 address space. We can find active IPv6 addresses through scanning candidate addresses generated by the state-of-the-art algorithms, whose probing efficiency of active IPv6 addresses, however, is still very low. In this paper, we aim to improve the probing efficiency of IPv6 addresses in two ways. Firstly, we perform a longitudinal active measurement study over four months, building a high-quality dataset called hitlist with more than 1.3 billion IPv6 addresses distributed in 45.2k BGP prefixes. Different from previous work, we probe the announced BGP prefixes using a pattern-based algorithm, which makes our dataset overcome the problems of uneven address distribution and low active rate. Secondly, we propose an efficient address generation algorithm DET, which builds a density space tree to learn high-density address regions of the seed addresses in linear time and improves the probing efficiency of active addresses. On the public hitlist and our hitlist, we compare our algorithm DET against state-of-the-art algorithms and find that DET increases the de-aliased active address ratio by 10%, and active address (including aliased addresses) ratio by 14%, by scanning 50 million addresses.
Tseng, Chia-Wei, Wu, Li-Fan, Hsu, Shih-Chun, Yu, Sheng-Wang.  2020.  IPv6 DoS Attacks Detection Using Machine Learning Enhanced IDS in SDN/NFV Environment. 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS). :263–266.
The rapid growth of IPv6 traffic makes security issues become more important. This paper proposes an IPv6 network security system that integrates signature-based Intrusion Detection Systems (IDS) and machine learning classification technologies to improve the accuracy of IPv6 denial-of-service (DoS) attacks detection. In addition, this paper has also enhanced IPv6 network security defense capabilities through software-defined networking (SDN) and network function virtualization (NFV) technologies. The experimental results prove that the detection and defense mechanisms proposed in this paper can effectively strengthen IPv6 network security.
Zhang, Yu-Yan, Chen, Xing-Xing, Zhang, Xu.  2020.  PCHA: A Fast Packet Classification Algorithm For IPv6 Based On Hash And AVL Tree. 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). :397–404.
As the core infrastructure of cloud data operation, exchange and storage, data centerneeds to ensure its security and reliability, which are the important prerequisites for the development of cloud computing. Due to various illegal accesses, attacks, viruses and other security threats, it is necessary to protect the boundary of cloud data center through security gateway. Since the traffic growing up to gigabyte level, the secure gateway must ensure high transmission efficiency and different network services to support the cloud services. In addition, data center is gradually evolving from IPv4 to IPv6 due to excessive consumption of IP addresses. Packet classification algorithm, which can divide packets into different specific streams, is very important for QoS, real-time data stream application and firewall. Therefore, it is necessary to design a high performance IPv6 packet classification algorithm suitable for security gateway.AsIPv6 has a128-bitIP address and a different packet structure compared with IPv4, the traditional IPv4 packet classification algorithm is not suitable properly for IPv6 situations. This paper proposes a fast packet classification algorithm for IPv6 - PCHA (packet classification based on hash andAdelson-Velsky-Landis Tree). It adopts the three flow classification fields of source IPaddress(SA), destination IPaddress(DA) and flow label(FL) in the IPv6 packet defined by RFC3697 to implement fast three-tuple matching of IPv6 packet. It is through hash matching of variable length IPv6 address and tree matching of shorter flow label. Analysis and testing show that the algorithm has a time complexity close to O(1) in the acceptable range of space complexity, which meets the requirements of fast classification of IPv6 packetsand can adapt well to the changes in the size of rule sets, supporting fast preprocessing of rule sets. Our algorithm supports the storage of 500,000 3-tuple rules on the gateway device and can maintain 75% of the performance of throughput for small packets of 78 bytes.