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Ann F. Sabeeh

Bio: Ann F. Sabeeh is an academic researcher from Brunel University London. The author has contributed to research in topics: Password & Login. The author has an hindex of 2, co-authored 2 publications receiving 7 citations.

Papers
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Journal ArticleDOI
TL;DR: The authors introduce a simple and effective solution to the detection of password file disclosure events and suggest an alternative approach that selects the honeywords from existing user information, a generic password list, dictionary attack, and by shuffling the characters.
Abstract: Honeywords (decoy passwords) have been proposed to detect attacks against hashed password databases. For each user account, the original password is stored with many honeywords in order to thwart any adversary. The honeywords are selected deliberately such that a cyber-attacker who steals a file of hashed passwords cannot be sure, if it is the real password or a honeyword for any account. Moreover, entering with a honeyword to login will trigger an alarm notifying the administrator about a password file breach. At the expense of increasing the storage requirement by 24 times, the authors introduce a simple and effective solution to the detection of password file disclosure events. In this study, we scrutinise the honeyword system and highlight possible weak points. Also, we suggest an alternative approach that selects the honeywords from existing user information, a generic password list, dictionary attack, and by shuffling the characters. Four sets of honeywords are added to the system that resembles the real passwords, thereby achieving an extremely flat honeywords generation method. To measure the human behaviours in relation to trying to crack the password, a testbed engaged with by 820 people was created to determine the appropriate words for the traditional and proposed methods. The results show that under the new method it is harder to obtain any indication of the real password (high flatness) when compared with traditional approaches and the probability of choosing the real password is 1/k, where k = number of honeywords plus the real password.

7 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: A new hybrid intelligent approach for optimising the performance of Software-Defined Networks (SDN), based on heuristic optimisation methods integrated with neural network paradigm, is presented.
Abstract: A new hybrid intelligent approach for optimising the performance of Software-Defined Networks (SDN), based on heuristic optimisation methods integrated with neural network paradigm, is presented. Evolutionary Optimisation techniques, such as Particle Swarm Optimisation (PSO) and Genetic Algorithms (GA), are employed to find the best set of inputs that give the maximum performance of an SDN. The Neural Network model is trained and applied as an approximator of SDN behaviour. An analytical investigation has been conducted to distinguish the optimal optimisation approach based on SDN performance as an objective function as well as the computational time. After getting the general model of the Neural Network through testing it with unseen data, this model has been implemented with PSO and GA to find the best performance of SDN. The PSO approach combined with SDN, represented by ANN, is identified as a comparatively better configuration regarding its performance index as well as its computational efficiency.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors present a survey of machine learning techniques for routing optimization in SDN based on three core categories (i.e., supervised learning, unsupervised learning, and reinforcement learning).
Abstract: In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

31 citations

Proceedings Article
26 Mar 2014
TL;DR: This paper finds that Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.
Abstract: A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.

16 citations

Proceedings ArticleDOI
06 Jul 2018
TL;DR: This paper proposes a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing and demonstrates that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks.
Abstract: The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.

8 citations

Proceedings ArticleDOI
01 Jul 2017
TL;DR: The SDNRoute system, which aims to support routing decisions in Software Defined Networks, is introduced and preliminary approaches to solve the issues including optimization problem formulation are provided.
Abstract: In this paper we introduce the SDNRoute system which aims to support routing decisions in Software Defined Networks. Such a system is especially important in the context of the SDN as this concept makes it possible to perform routing in a dynamic and elastic manner. We focus on the SDNRoute architecture and research challenges that must be investigated to develop the system. Additionally, we provide preliminary approaches to solve the issues including optimization problem formulation. Recent state of the art is presented regarding not only academia works but also industry deployments. It is especially important as SDNRoute system is being designed for the purpose of commercial application.

7 citations

Journal ArticleDOI
01 Feb 2021
TL;DR: The proposed technique allows the user to keep the ease-of-use in the mouse motion, while minimizing the risk of password guessing, in a new password generation technique on the basis of mouse motion and a special case location recognized by the number of clicks.
Abstract: This paper proposes a new password generation technique on the basis of mouse motion and a special case location recognized by the number of clicks to protect sensitive data for different companies. Two, three special locations click points for the users has been proposed to increase password complexity. Unlike other currently available random password generators, the path and number of clicks will be added by admin, and authorized users have to be training on it. This method aims to increase combinations for the graphical password generation using mouse motion for a limited number of users. A mathematical model is developed to calculate the performance of the password. The proposed technique in this paper allows the user to keep the ease-of-use in the mouse motion, while minimizing the risk of password guessing. A comparative evaluation has been conducted against a traditional password. The results show that the proposed approach improves the complexity 200% for fix position technique and two variants technique but more than 200% for three variants technique.

3 citations