tazar hussain

An Analysis of the Impact of Uncertainty on the Internet of Things: A Smart Home Case Study

The Internet of Things (IoT) is the enabling technology for a range of smart domains and applicat... more The Internet of Things (IoT) is the enabling technology for a range of smart domains and application areas. IoT consists of sensors, actuators, gateways and cloud infrastructures which together support the processing of information to enable decision makers to take appropriate actions in a given scenario. IoT infrastructures may, however, be associated with unreliable sensors and network infrastructures (e.g., due to incompleteness, inconsistency, inaccuracy and inaccessibility) which will lead to uncertainty in the decision-making process and as a result take inappropriate actions which may pose a threat to the safety and security of its users. The work presented in this paper focuses on analysing and evaluating the impact of different types of data imperfections and unreliable sensor data on the process of classification within an IoT instance applied in the domain of smart homes (SH). Four classifiers, namely Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (kNN) and Naïve Bayes (NB) were chosen for evaluation purpose. Classification results based on an openly available SH dataset were analysed and compared to evaluate and illustrate the performance and robustness of classifiers. The results indicated that it is difficult to declare a single classifier with high performance and robustness. Nevertheless, the RF classifier achieved the highest accuracy of 95.51%, 83.27% and 85.08% for attribute noise, missing and failure cases, respectively while NB achieved a highest accuracy of 79.5% in the case of class noise.

An Attack Impact and Host Importance based Approach to Intrusion Response Action Selection

2022 4th International Conference on Information Technology and Computer Communications (ITCC)

Selecting appropriate actions is crucial for building effective Intrusion Response Systems (IRS) ... more Selecting appropriate actions is crucial for building effective Intrusion Response Systems (IRS) that can counter intrusions according to their priority level. Currently, the priority level of intrusions is determined manually, in a static manner, which is time consuming, ineffective and cannot scale with the growing number of attacks. In * Corresponding author This work is licensed under a Creative Commons Attribution International 4.0 License.

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From Machine Learning Based Intrusion Detection to Cost Sensitive Intrusion Response

2022 6th International Conference on Cryptography, Security and Privacy (CSP)

An Analysis of the Impact of Uncertainty on the Internet of Things: A Smart Home Case Study

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019

The Internet of Things (IoT) is the enabling technology for a range of smart domains and applicat... more The Internet of Things (IoT) is the enabling technology for a range of smart domains and application areas. IoT consists of sensors, actuators, gateways and cloud infrastructures which together support the processing of information to enable decision makers to take appropriate actions in a given scenario. IoT infrastructures may, however, be associated with unreliable sensors and network infrastructures (e.g., due to incompleteness, inconsistency, inaccuracy and inaccessibility) which will lead to uncertainty in the decision-making process and as a result take inappropriate actions which may pose a threat to the safety and security of its users. The work presented in this paper focuses on analysing and evaluating the impact of different types of data imperfections and unreliable sensor data on the process of classification within an IoT instance applied in the domain of smart homes (SH). Four classifiers, namely Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (kNN) and Naïve Bayes (NB) were chosen for evaluation purpose. Classification results based on an openly available SH dataset were analysed and compared to evaluate and illustrate the performance and robustness of classifiers. The results indicated that it is difficult to declare a single classifier with high performance and robustness. Nevertheless, the RF classifier achieved the highest accuracy of 95.51%, 83.27% and 85.08% for attribute noise, missing and failure cases, respectively while NB achieved a highest accuracy of 79.5% in the case of class noise.

A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies

Sensors, 2021

The Internet of Things (IoT) is a key and growing technology for many critical real-life applicat... more The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based...

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Enhancing C4I Security Using Threat Modeling

C4I (command, control, communications, computer and intelligence) system is a complex system of s... more C4I (command, control, communications, computer and intelligence) system is a complex system of systems that enables the military commander to achieve decision superiority by affecting adversary's information and information based processes while protecting one`s own information systems. Owing to the sensitivity of military information the security threats to C4I systems are real and growing therefore security is a major challenge in order to maintain integrity, confidentiality, availability and accountability. Different Models and architectures frameworks e.g. DODAF, CDSA have been proposed which decompose the detail structure of C4I systems however these models and frameworks do not provide mechanism to capture associated security risks and issues. Threat modeling is the process to understand different ways system can be attacked which helps towards secure system design and implement countermeasures. This paper suggests that using threat modeling could help to eradicate or reduce the effect of advancing threats to C4I systems. In this paper we also identify how security can be modeled in top down manner e.g. from architecture level to lower level of modeling so that countermeasures can be incorporated on system level rather than on bit by bit basis.

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Towards software engineering process for C4I systems

Command Control, Communication Computer and Intelligence (C4I) systems enables modern military fo... more Command Control, Communication Computer and Intelligence (C4I) systems enables modern military forces to achieve information superiority in the battlefield. C4I are complex System of systems (SOS) where individual systems interact locally to achieve global SOS behaviors. To build software for C4I systems conventional software engineering SwE process and practices have shortcomings and are not capable to support certain aspect of these systems. If C4I systems fail to operate as required due to the fact that SwE process was unable to fulfill its requirements, the consequences may not be tolerated because of the criticality of the mission of these systems in information warfare (IW). This paper highlights the distinguished characteristics and operational requirements of C4I systems which poses challenges to SwE process and practices. This paper also discuss the possible future research areas in order to enhance SwE process so that better software could drive these complex systems as required.

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Enhancing C4I Security using Intrusion Detection and Prevention System

Command, control, communication, computer and intelligence (C4I) systems are complex distributed ... more

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