Threat Modeling Approaches for Securing Cloud Computin

Amini, A.; Jamil, Norziana; Ahmad, Abd Rahman; Z’aba, Muhammad Reza

doi:10.3923/jas.2015.953.967

Cited by 21 publications

(36 citation statements)

References 29 publications

(39 reference statements)

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“…There are different types of networks, and the research aspect for each of which encompasses improving the performance that is associated with the efficiency, flexibility, security and elasticity qualities [23], [24]. The routing optimization is a general problem in all network topologies.…”

Section: Resultsmentioning

confidence: 99%

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

Jubair¹,

Mostafa²,

Gunasekaran³

2018

International Journal on Advanced Science, Engineering and Information Technology

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Mobile Ad-hoc Networks (MANETs) are self-sufficient networks that can work without the need for centralized controls, pre-configuration to the routes or advance infrastructures. The nodes of a MANET are autonomously controlled, which allow them to act freely in a random manner within the MANET. The nodes can leave their MANET and join other MANETs at any time. These characteristics, however, might negatively affect the performance of the routing protocols and the overall topology of the networks. Subsequently, MANETs comprise specially designed routing protocols that reactively and proactively perform the routing. This paper evaluates and compares the performance of two routing protocols which are Ad-Hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) in MANET environment. The study includes implementing a simulation to examine the performance of the routing protocols based on the variables of the nodes' number and network size. The evaluation results show that the AODV outperforms the OLSR in most of the simulated cases. The results further show that the number of nodes and network size has a great impact on the Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E) of the network.

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Section: Resultsmentioning

confidence: 99%

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

Jubair¹,

Mostafa²,

Gunasekaran³

2018

International Journal on Advanced Science, Engineering and Information Technology

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“…1. Although DREAD is an asset-centric threat modelling approach, several of its application in the literature is in combination with STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege) model [8], [9], [10], [11], [12]. In this type of approach, the DREAD scoring scheme is used to identify the likelihood that an attack is able to exploit a particular threat.…”

Section: A Dreadmentioning

confidence: 99%

A Review of Asset-Centric Threat Modelling Approaches

Nweke¹,

Wolthusen²

2020

IJACSA

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The threat landscape is constantly evolving. As attackers continue to evolve and seek better methods of compromising a system; in the same way, defenders continue to evolve and seek better methods of protecting a system. Threats are events that could cause harm to the confidentiality, integrity, or availability of information systems, through unauthorized disclosure, misuse, alteration, or destruction of information or information system. The process of developing and applying a representation of those threats, to understand the possibility of the threats being realized is referred to as threat modelling. Threat modelling approaches provide defenders with a tool to characterize potential threats systematically. They include the prioritization of threats and mitigation based on probabilities of the threats being realized, the business impacts and the cost of countermeasures. In this paper, we provide a review of assetcentric threat modelling approaches. These are threat modelling techniques that focus on the assets of the system being threat modelled. First, we discuss the most widely used asset-centric threat modelling approaches. Then, we present a gap analysis of these methods. Finally, we examine the features of asset-centric threat modelling approaches with a discussion on their similarities and differences.

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“…Security Target describes TOE by threats, security objectives, SFRs, SARs, and TOE security functions that occur in TOE [22][23][24][25].…”

Section: Eal2 Analysis and Requirementsmentioning

confidence: 99%

How to Obtain Common Criteria Certification of Smart TV for Home IoT Security and Reliability

Kang

Kim

2017

Symmetry

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Abstract:In the new era of IoT (Internet of Things), numerous gadgets and services include innovative IoT technologies that provide customers with convenience and improve their quality of life. Smart TVs are typical IoT devices that offer broadcasting services. However, they are susceptible to security intrusions via e-mail, media players, cameras, and internet connectivity. The frequency of hacking through malicious applications installed in Smart TV has rapidly increased. Therefore, appropriate countermeasures should be developed immediately. In April 2017, we (with LG electronics) received the 'world-first' Common Criteria EAL2 (Evaluation Assurance Level 2) certification for Smart TVs. As far as we know, no Smart TV has received a Common Criteria EAL2 security certification until now. This article describes our experience with the certification process and examines several security and reliability aspects of Smart TVs.

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Threat Modeling Approaches for Securing Cloud Computin

Cited by 21 publications

References 29 publications

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

A Review of Asset-Centric Threat Modelling Approaches

How to Obtain Common Criteria Certification of Smart TV for Home IoT Security and Reliability

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