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  • Open Access


    Trust Provision in the Internet of Things Using Transversal Blockchain Networks

    Borja Bordela, Ramon Alcarriab, Diego Martína, Álvaro Sánchez-Picota

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 155-170, 2019, DOI:10.31209/2018.100000052

    Abstract The Internet-of-Things (IoT) paradigm faces new and genuine challenges and problems associated, mainly, with the ubiquitous access to the Internet, the huge number of devices involved and the heterogeneity of the components making up this new global network. In this context, protecting these systems against cyberattacks and cybercrimes has turn into a basic issue. In relation to this topic, most proposed solutions in the literature are focused on security; however other aspects have to be considered (such as privacy or trust). Therefore, in this paper we define a theoretical framework for trust in IoT scenarios, including a mathematical formalization and… More >

  • Open Access


    Protecting Android Applications with Multiple DEX Files Against Static Reverse Engineering Attacks

    Kyeonghwan Lim1, Nak Young Kim1, Younsik Jeong1, Seong-je Cho1, Sangchul Han2, Minkyu Park2

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 143-153, 2019, DOI:10.31209/2018.100000051

    Abstract The Android application package (APK) uses the DEX format as an executable file format. Since DEX files are in Java bytecode format, you can easily get Java source code using static reverse engineering tools. This feature makes it easy to steal Android applications. Tools such as ijiami, liapp, alibaba, etc. can be used to protect applications from static reverse engineering attacks. These tools typically save encrypted classes.dex in the APK file, and then decrypt and load dynamically when the application starts. However, these tools do not protect multidex Android applications. A multidex Android application is an APK that contains multiple… More >

  • Open Access


    The Design and Implementation of a Multidimensional and Hierarchical Web Anomaly Detection System

    Jianfeng Guan*, Jiawei Li, Zhongbai Jiang

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 131-141, 2019, DOI:10.31209/2018.100000050

    Abstract The traditional web anomaly detection systems face the challenges derived from the constantly evolving of the web malicious attacks, which therefore result in high false positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to these limitations, we need a new anomaly detection system to satisfy the requirements of enterprise-level anomaly detection. There are lots of anomaly detection systems designed for different application domains. However, as for web anomaly detection, it has to describe the network accessing behaviours characters from as many dimensions as possible to improve the performance. In this paper we design and implement a Multidimensional… More >

  • Open Access


    Cyber-security Risk Assessment Framework for Critical Infrastructures

    Zubair Baig1, Sherali Zeadally2

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 121-129, 2019, DOI:10.31209/2018.100000049

    Abstract A critical infrastructure provides essential services to a nation’s population. Interruptions in its smooth operations are highly undesirable because they will cause significant and devastating consequences on all stakeholders in the society. In order to provide sustained protection to a nation’s critical infrastructure, we must continually assess and evaluate the risks thereof. We propose a risk assessment framework that can evaluate the risks posed to the security of a critical infrastructure from threat agents, with a special emphasis on the smart grid communications infrastructure. The framework defines finegrained risk identification to help quantify and assess exploitable vulnerabilities within a critical… More >

  • Open Access


    Advances in Security and Privacy Technologies for Forthcoming Smart Systems, Services, Computing, and Networks

    Ilsun You, Chang Choi, Vishal Sharma, Isaac Woungang, Bharat K. Bhargava

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 117-119, 2019, DOI:10.31209/2018.100000048

    Abstract This article has no abstract. More >

  • Open Access


    Dynamic Task Assignment for Multi-AUV Cooperative Hunting

    Xiang Cao1,2,3, Haichun Yu1,3, Hongbing Sun1,3

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 25-34, 2019, DOI:10.31209/2018.100000038

    Abstract For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting in underwater environments with obstacles.… More >

  • Open Access


    Short-term Forecasting of Air Passengers Based on the Hybrid Rough Set and the Double Exponential Smoothing Model

    Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

    Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that the forecasting accuracy of the… More >

  • Open Access


    Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine Learning

    Belayat Hossaina, Takatoshi Morookab, Makiko Okunob, Manabu Niia, Shinichi Yoshiyab, Syoji Kobashia

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034

    Abstract This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with… More >

  • Open Access


    An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

    Yasir Mehmood, Waseem Shahzad

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

    Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO… More >

  • Open Access


    Simulation of Real‐Time Path Planning for Large‐Scale Transportation Network Using Parallel Computation

    Jiping Liua,b, Xiaochen Kanga,*, Chun Donga, Fuhao Zhanga

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 65-77, 2019, DOI:10.31209/2018.100000013

    Abstract To guarantee both the efficiency and accuracy of the transportation system, the real-time status should be analyzed to provide a reasonable plan for the near future. This paper proposes a model for simulating the real-world transportation networks by representing the irregular road networks with static and dynamic attributes, and the vehicles as moving agents constrained by the road networks. The all pairs shortest paths (APSP) for the networks are calculated in a real-time manner, and the ever-changing paths can be used for navigating the moving vehicles with real-time positioning devices. In addition, parallel computation is used to accelerate the shortest… More >

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