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Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security
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Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 18975

Special Issue Editor

Dr. Konstantinos Demertzis

E-Mail Website
Guest Editor
School of Science, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: model-agnostic meta-learning; multi-task learning; real-time analytics; scalable and compassable privacy-preserving data mining; automated assessment and response systems; AI anomaly detection; AI malware analysis; AI IDS-IPS; AI forensics; AI in blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent malware developments have the ability to remain hidden during infection and operation, using various techniques, such as obscure sessions, the modification of file attributes, or operation under the pretense of legitimate services and registry keys. In more advanced cases, the malware might attempt to subvert modern detection software, by hiding or masquerading running processes, obfuscating encrypted connections, and executing scripts for persistence with strings with malicious URLs or registry keys. Sometimes the malware goes a step further and obfuscates the entire file, thus making all the original code and data unreadable.

As cyberattacks grow in volume and complexity, artificial intelligence is helping under-resourced security operations analysts stay ahead of threats. Bio-inspired machine learning and bio-inspired optimization algorithms are recognized in artificial intelligence to address optimal solutions of complex problems in information science and engineering. However, cybersecurity problems are usually nonlinear and restricted to multiple nonlinear constraints that propose many problems such as time requirements and high dimensionality to find an optimal solution. To tackle these problems, recent trends have tended to apply bio-inspired machine learning and bio-inspired optimization algorithms in hybrid frameworks that represent a promising approach for solving complex cybersecurity problems.

The present Special Issue is devised as a collection of articles reporting both concise reviews of recently obtained results and new findings produced in this broad research area. Topics of interest include but are not limited to bio−inspired computing and applications in cybersecurity, such as bio−inspired computing in cloud computing and big data; neural computation and deep learning; spiking neural networks; bio−inspired complex networks and hybrid systems; evolutionary computation; swarm intelligence and bio-inspired optimization; artificial immune systems; bio−inspired intelligent systems; cellular automata; and DNA and membrane computing.

Dr. Konstantinos Demertzis
Guest Editor

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Keywords

  • cybersecurity
  • neural computation
  • bio-inspired machine learning
  • bio-inspired optimization
  • bio-inspired computing
  • evolutionary optimization

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Published Papers (5 papers)

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Research

14 pages, 826 KiB  
Article
Mitigating Insider Threats Using Bio-Inspired Models
by Andreas Nicolaou, Stavros Shiaeles and Nick Savage
Appl. Sci. 2020, 10(15), 5046; https://doi.org/10.3390/app10155046 - 22 Jul 2020
Cited by 15 | Viewed by 3557
Abstract
Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from [...] Read more.
Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to mitigate it by developing a machine learning model based on Bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explore swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results show that swarm intelligence algorithms perform well on feature selection optimization and the generated, near-optimal, subset of features has a similar performance to the original one. Full article
(This article belongs to the Special Issue Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security)
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15 pages, 1846 KiB  
Article
The μ-Calculus Model-Checking Algorithm for Generalized Possibilistic Decision Process
by Jiulei Jiang, Panqing Zhang and Zhanyou Ma
Appl. Sci. 2020, 10(7), 2594; https://doi.org/10.3390/app10072594 - 9 Apr 2020
Cited by 3 | Viewed by 2385
Abstract
Model checking is a formal automatic verification technology for complex concurrent systems. It is used widely in the verification and analysis of computer software and hardware systems, communication protocols, security protocols, etc. The generalized possibilistic μ-calculus model-checking algorithm for decision processes is studied [...] Read more.
Model checking is a formal automatic verification technology for complex concurrent systems. It is used widely in the verification and analysis of computer software and hardware systems, communication protocols, security protocols, etc. The generalized possibilistic μ-calculus model-checking algorithm for decision processes is studied to solve the formal verification problem of concurrent systems with nondeterministic information and incomplete information on the basis of possibility theory. Firstly, the generalized possibilistic decision process is introduced as the system model. Then, the classical proposition μ-calculus is improved and extended, and the concept of generalized possibilistic μ-calculus (GPoμ) is given to describe the attribute characteristics of nondeterministic systems. Then, the GPoμ model-checking algorithm is proposed, and the model-checking problem is simplified to fuzzy matrix operations. Finally, a specific example and a case study are analyzed and verified. Compared with the classical μ-calculus, the generalized possibilistic μ-calculus has a stronger expressive power and can better characterize the attributes of nondeterministic systems. The model-checking algorithm can give the possibility that the system satisfies the attributes. The research work provides a new idea and method for model checking nondeterministic systems. Full article
(This article belongs to the Special Issue Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security)
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19 pages, 1092 KiB  
Article
Detection of Sensitive Data to Counter Global Terrorism
by Binod Kumar Adhikari, Wanli Zuo, Ramesh Maharjan, Xuming Han and Shining Liang
Appl. Sci. 2020, 10(1), 182; https://doi.org/10.3390/app10010182 - 25 Dec 2019
Cited by 5 | Viewed by 2729
Abstract
Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for [...] Read more.
Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale. Full article
(This article belongs to the Special Issue Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security)
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13 pages, 2825 KiB  
Article
A Brain-Inspired Goal-Oriented Robot Navigation System
by Qiuying Chen and Hongwei Mo
Appl. Sci. 2019, 9(22), 4869; https://doi.org/10.3390/app9224869 - 14 Nov 2019
Cited by 10 | Viewed by 3599
Abstract
Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture [...] Read more.
Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability. Full article
(This article belongs to the Special Issue Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security)
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17 pages, 2357 KiB  
Article
iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †
by Md. Ataur Rahman Bhuiyan, Md. Rifat Ullah and Amit Kumar Das
Appl. Sci. 2019, 9(16), 3365; https://doi.org/10.3390/app9163365 - 15 Aug 2019
Cited by 11 | Viewed by 5465
Abstract
Recently, the healthcare industry has caught the attention of researchers due to a need to develop a smart and interactive system for effective and efficient treatment facilities. The healthcare system consists of massive biological data (unstructured or semi-structured) which needs to be analyzed [...] Read more.
Recently, the healthcare industry has caught the attention of researchers due to a need to develop a smart and interactive system for effective and efficient treatment facilities. The healthcare system consists of massive biological data (unstructured or semi-structured) which needs to be analyzed and processed for early disease detection. In this paper, we have designed a piece of healthcare technology which can deal with a patient’s past and present medical data including symptoms of a disease, emotional data, and genetic data. We have designed a probabilistic data acquisition scheme to analyze the medical data. This model contains a data warehouse with a two-way interaction between high-performance computing and cloud synchronization. Finally, we present a prediction scheme that is performed in the cloud server to predict disease in a patient. To complete this task, we used Random Forest, Support Vector Machine (SVM), C5.0, Naive Bayes, and Artificial Neural Networks for prediction analysis, and made a comparison between these algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security)
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