•Detection of stealthy false data injection attacks in the smart grid.•Supervised and unsupervised machine learning methods.•Ensemble-based machine learning methods.
Stealthy false data injection ...attacks target state estimation in energy management systems in smart power grids to adversely affect operations of the power transmission systems. This paper presents a data-driven machine learning based scheme to detect stealthy false data injection attacks on state estimation. The scheme employs ensemble learning, where multiple classifiers are used and decisions by individual classifiers are further classified. Two ensembles are used in this scheme, one uses supervised classifiers while the other uses unsupervised classifiers. The scheme is validated using simulated data on the standard IEEE 14-bus system. Experimental results show that the performance of both supervised individual and ensemble models are comparable. However, for unsupervised models, the ensembles performed better than the individual classifiers.
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been ...developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.
Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to security and privacy policies to ensure that patient data ...remains confidential and secure. However, there is confusion among IoMT stakeholders as to what security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter's security and compliance requirements. In this paper, we present a recommendation tool that models IoMT concepts and security issues in addition to successively recommending security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a recommendation system that empowers users to make well-educated decisions. The recommendation tool classifies IoMT security threats faced by IoMT stakeholders and automatically recommends security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., security issues and recommended security measures). The results show that the tool was effectively able to recommend necessary security measures.
Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary ...uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.
Proper security solutions in the cyber world are crucial for enforcing network security by providing real-time network protection against network vulnerabilities and data exploitation. An effective ...intrusion detection strategy is capable of taking a holistic approach for protecting critical systems against unauthorized access or attack. In this paper, we describe a machine learning (ML) based comprehensive security solution for network intrusion detection using ensemble supervised ML framework and ensemble feature selection methods. In addition, we provide a comparative analysis of several ML models and feature selection methods. The goal of this research is to design a generic detection mechanism and achieve higher accuracy with minimal false positive rates (FPR). NSL-KDD, UNSW-NB15, and CICIDS2017 datasets are used in the experiment, and results show that our detection model can identify 99.3% of intrusions successfully with the lowest 0.5% of false alarms, which depicts better performance metrics compared to existing solutions.
Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to ...increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.
Effective load balancing is tougher in grid computing compared to other conventional distributed computing platforms due to its heterogeneity, autonomy, scalability, and adaptability characteristics, ...resource selection and distribution mechanisms, and data separation. Hence, it is necessary to identify and handle the uncertainty of the tasks and grid resources before making load balancing decisions. Using two potential forms of Hidden Markov Models (HMM), i.e., Profile Hidden Markov Model (PF_HMM) and Pair Hidden Markov Model (PR_HMM), the uncertainties in the task and system parameters are identified. Load balancing is then carried out using our novel Fuzzy Neutrosophic Soft Set theory (FNSS) based transfer Q-learning with pre-trained knowledge. The transfer Q-learning enabled with FNSS solves large scale load balancing problems efficiently as the models are already trained and do not need pre-training. Our expected value analysis and simulation results confirm that the proposed scheme is 90 percent better than three of the recent load balancing schemes.
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly ...disruption of services across all sectors and critical infrastructure. Machine-learning-based approaches have shown promise in developing intrusion detection systems (IDSs) for detecting cyber-attacks, such as DDoS. Herein, we present a solution to detect DDoS attacks through an ensemble-based machine learning approach that combines supervised and unsupervised machine learning ensemble frameworks. This combination produces higher performance in detecting known DDoS attacks using supervised ensemble and for zero-day DDoS attacks using an unsupervised ensemble. The unsupervised ensemble, which employs novelty and outlier detection, is effective in identifying prior unseen attacks. The ensemble framework is tested using three well-known benchmark datasets, NSL-KDD, UNSW-NB15, and CICIDS2017. The results show that ensemble classifiers significantly outperform single-classifier-based approaches. Our model with combined supervised and unsupervised ensemble models correctly detects up to 99.1% of the DDoS attacks, with a negligible rate of false alarms.
Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the ...network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.
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