Lattice-Based Certificateless Collaborative Signature Scheme for Post-Quantum Intelligent Computing and Network Systems

It is a great privilege to welcome you to the 2019 The 2nd International Conference on Computational Intelligence and Intelligent Systems (CIIS 2019) in Bangkok, Thailand during November 23-25, 2019. A major goal and feature of it is to bring academic scientists, engineers, industry researchers together to exchange and share their experiences and research results about most aspects of science and social research, and discuss the practical challenges encountered and the solutions adopted. We hope you will have a technically rewarding experience, and use this occasion to meet old friends and make many new ones.

The public debate and discussion about trust in Computational Intelligence (CI) systems is not new, but a topic that has seen a recent rise. This is mainly due to the explosion of technological innovations that have been brought to the attention of the public, from lab to reality usually through media reporting. This growth in the public attention was further compounded by the 2018 GDPR legislation and new laws regarding the right to explainable systems, such as the of accurate data, logic and the use of appropriate mathematical and statistical procedures for profiling. Therefore, trust is not just a topic for debate – it must be addressed from the onset, through the selection of fundamental machine learning processes that are used to create models embedded within autonomous decision-making systems, to the selection of training, validation and testing data. This paper presents current work on trust in the field of Computational Intelligence systems and discusses the legal framework we should ascribe to trust in CI systems. A case study examining current public perceptions of recent CI inspired technologies which took part at a national science festival is presented with some surprising results. Finally, we look at current research underway that is aiming to increase trust in Computational Intelligent systems and we identify a clear educational gap.

Currently, much awareness is paid to the effects of smart grids on efficiency of power systems and energy districts, and the selection of suitable operating modes for equipment for transmitting electric energy. Modern intelligent networks have an impact on ensuring a stable state of power system operation modes, increasing their reliability in operation, and increasing the quality of electric energy. The article analyzes the use of intelligent electrical networks and intelligent network systems in both Western countries and the Russian Federation. Statistical data on electricity consumption and forecast values for this rate of consumption are given. In the future, until 2020-2025, there will be a gradual transition to a new type of electric grid organization - smart grids. The use of electric power systems in conjunction with innovative digital information management systems will significantly change the technical and economic characteristics of future power systems in a positive way. The comparison of technical parameters of traditional and intelligent electric networks, as well as the classification of the main groups of devices controlled (flexible) by AC power transmission systems is carried out. Systems are divided according to the principle of power regulation. Implemented projects of using flexible alternative current transmission system devices in the world are presented. As a result of the analysis, it was found that China is actively developing managed (flexible) AC power transmission systems, with a large amount of investment from the state, the volume of investment is much larger and amounts to about $ 70 billion per year.

We have developed and demonstrated an intelligent auto-organizing aerial robotic sensor network system composed of cameras installed on UAVs and ground fixtures for urban surveillance using the decentralised control paradigm. The system can auto-organize to meet user requirements, changes to user requirements and when new UAVs or ground cameras are added or are lost due to failure. User designated targets can be automatically tracked and handed over from one camera to another, to ensure continuous tracking. The cameras can also self adjust their positions and pointing angles to cooperatively cover areas of interest completely. We have achieved this by designing a software architecture for managing multiple interacting algorithms online and in parallel, facilitating incremental software development and debugging. We have adapted algorithms to become computationally efficient and effective for real world use online and on-board the UAVs, and ensured that the integration of video analytics and tactical behaviours work in real world settings. We have also achieved near optimal, online close coordination of the UAVs and ground cameras. To the best of our knowledge, this is the first proof-of-concept of a fully integrated intelligent auto-organizing aerial robotic sensor network system of cameras installed on UAVs and ground fixtures for urban surveillance.

Most of the existing trackers have high performance and good effect, but it is difficult to have fast speed and can not meet the real-time requirements. Therefore, the development of target tracking system with high accuracy and good robustness has ...

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information. In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: ”Do Smart Adaptive Systems Exist?”. One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems. However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today. Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world’s leading think tank advanced technology organisations and very suggestively titled: ”Get smart: How intelligent technology will enhance our world”.

Computational Intelligence Systems (CIS) is one of advanced softwares. CIS has been important position for solving single-objective / reverse / inverse and multi-objective design problems in engineering. The paper hybridise a CIS for optimisation with the concept of Nash-Equilibrium as an optimisation pre-conditioner to accelerate the optimisation process. The hybridised CIS (Hybrid Intelligence System) coupled to the Finite Element Analysis (FEA) tool and one type of Computer Aided Design (CAD) system, GiD is applied to solve an inverse engineering design problem, reconstruction of High Lift Systems (HLS). Numerical results obtained by the hybridised CIS are compared to the results obtained by the original CIS. The benefits of using the concept of Nash-Equilibrium are clearly demonstrated in terms of solution accuracy and optimisation efficiency.

Vehicular ad hoc networks (VANETs), an application of the Internet of Things (IoT) providing a better intelligent transportation system (ITS), has received substantial attention from both industry and academia. Heterogeneous vehicular communications in VANETs occur when both vehicles and the infrastructure use different cryptographic technologies to exchange safety messages. However, the safety messages between vehicles and the infrastructure are communicated wirelessly; therefore, security issues are a serious concern in this domain. The existing schemes secure the transmission of safety messages with respect to confidentiality, authentication, and nonrepudiation. However, they are inappropriate with respect to efficiency. They cause computational overhead on the receiver and bandwidth overhead in the communications. To cope with this, we propose an elliptic curve cryptosystem-based hybrid signcryption (ECCHSC) protocol that satisfies the security requirements (i.e., message confidentiality, message’s source authentication, message integrity, nonrepudiation, and identity–anonymity) for heterogeneous vehicle-to-infrastructure (V2I) communications in a single logical step. This protocol allows secure transmission of a safety message from a vehicle using identity-based cryptography (IDC) to a roadside unit (RSU) using public-key infrastructure (PKI). In addition, the ECCHSC protocol enables the RSU to receive multiple ciphertexts, aggregate them, and de-signcrypt them simultaneously through the batch de-signcryption method, which further improves the performance. The ECCHSC protocol has indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability against adaptive chosen message attacks (EUF-CMAs) in the random oracle model (ROM). The performance analysis of our protocol demonstrates a significant reduction in computational overhead and in communication/storage overhead as compared to the state-of-the-art schemes.

Purpose of research. The aim of this work is to develop an approach to constructing intelligent systems based on a replenished semantic network and a multi-agent environment with agents of various types: cognitive and deductive reactive. The architecture of a multi-level intelligent system is proposed and substantiated, which uses cognitive and reactive intelligent agents that differ in composition and number of implemented cognitive and deductive presumptions. Methods. Knowledge about the subject area is formalized both using the modal version of the first-order predicate calculus for describing cognitive agents and in terms of classical non-modal versions of predicate calculus and deductive inference mechanisms for reactive agents. The operation of an intelligent system is described by an incompletely defined semantic network represented by a conceptual graph and a system of production rules. Results. A functional architecture of an intelligent agent-based system is proposed. At the conceptual level, the architecture of an intelligent agent-based system is proposed to be represented by three sublevels. Cognitive agentsuse knowledge and beliefs that follow from epistemic logic systems. Cognitive presumptions of these agents include beliefs, goals, intentions, and desires of agents and are modeled within the framework of BDI logic. Conclusion. The conducted study shows the importance of BDI logic for cognitive agents, although it was used insignificantly in solving the task at hand, at the level of the content-conceptual description of the intelligent system. The extended functions of cognitive agents include the execution of input, registration, transmission and comparison of lists of objects and relations between them. The goals for the subsequent interpretation of cognitive agents are defined.

Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.

This article analyzes technologies and modern means of military use with intelligent computer systems (platforms). Such means include "smart" bullets, rifles with augmented reality glasses, as well as ballistic computers, "smart" weapons (mortars), high-precision ammunition, robotic combat vehicles and other equipment, laser air defense systems for ground, air, as well as naval forces, unmanned aerial vehicles, etc. Examples of intellectual means of military use, their tactical and technical characteristics and fields of application are given. The advantages and disadvantages of using technologies and means with intelligent systems (platforms) are highlighted, as well as the justified expediency of their use for military purposes. The examples of tools with intelligent systems (platforms) listed above are effectively used in the leading countries of the world, namely: the USA, Great Britain, Japan, France, Germany, Israel, the Netherlands, etc. The experience of use by the military of these countries has shown that intelligent systems make it possible to simplify the performance of relevant tasks, measures to protect against accidents or excessive aggression of criminals (enemies), and also increase the safety of the civilian population. It was determined that the introduction of world achievements, as well as the development of domestic systems and means with intelligent systems, is one of the priority directions of the development of the military-industrial complex of Ukraine. This, in turn, requires close cooperation between scientists and the military, which prompts the study of the possibilities of using intelligent systems in various fields, in particular in the field of national security development of Ukraine. Further prospective studies in this direction are highlighted.

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

Most of the methods and tools of Computational Intelligence (CI), such as Artificial Neural Networks, Evolutionary Computation, Fuzzy logic, Computational Swarm Intelligence and Artificial Immune Systems, have been developed for decades. However, their applications are not disseminated as exhaustively as they could be, for many reasons. In this work, a survey on some applications of Computational Intelligence to Mechanical Engineering will be presented. This review has been based on statistical analysis of a large amount of data extracted from major bibliographic databases. This analysis provides a quantitative map of the variety of CI methods and tools and their applications to some fields of Mechanical Engineering. The results will suggest to researchers some important topics of Mechanical Engineering where CI has great potential and may improve its impact.

SummaryFor the problems that ring signature needs key escrow, has some security risks, and cannot avoid malicious key generation center (KGC) in the post‐quantum era, we design a certificateless ring signature scheme based on lattice (L‐CRSS). In our scheme, the bimodal Gaussian distribution is used to improve the security and efficiency. Compared with the previous ring signature scheme based on lattice, our design does not need key escrow, does not disclose the signer's identity, can avoid malicious KGC, achieves the constant signature size, and has higher security and efficiency in the post‐quantum era. Finally, under random oracle model (ROM), we prove that our scheme is anonymous against the full‐key exposure, and existentially unforgeable against adaptive chosen message attacks (EUF‐CMA).

CIFEr, Computational Intelligence for Financial Engineering and Economics is the major collaboration between the professional engineering and financial communities, and is one of the leading forums for new technologies and applications in the intersection of computational intelligence and financial engineering and economics. Intelligent computational systems have become indispensable in virtually all financial applications, from portfolio selection to proprietary trading to risk management. But nowhere in this definition does it explain the significant role that these three mediums, Computational Intelligence and Financial Engineering and Economics, play in the various financial communities and in our professional lives. CIFEr has been a means of exploration for us and many other professionals who work in a financial arena. We need leading edge Financial Engineers now more then ever to work with "a new" set of "basics" which have evolved recently. Our subprime/liquidity crisis and flash crashes has affected us globally. Program and Algorithmic Trading will continue to grow while changing the way Traders trade and Trade Support supports. The bar has been raised with Regulatory and Compliance and Risk Management. The new rules will not work with the old processes. We need to find new ways to working with the new tools we have developed with Computational Intelligence. We believe CIFEr is one of those forums that will help Financial Engineers get through some perplexing problems at this very difficult time.