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Register a new userTeaching Information Security Management Using an Incident of Intellectual Property Leakage
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Added by
Atif Ahmad
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Case-based learning is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information Systems. The real-world case is described in a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei Technologies Co. LTD, alleging theft of intellectual property and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies.
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Teaching cases based on stories about real organizations are a powerful means of storytelling. These cases closely parallel real-world situations and can deliver on pedagogical objectives as writers can use their creative license to craft a storyline that better focuses on the specific principles, concepts, and challenges they want to address in their teaching. The method instigates critical discussion, draws out relevant experiences from students, encourages questioning of accepted practices, and creates dialogue between theory and practice. We present Horizon, a case study of a firm that suffers a catastrophic incident of Intellectual Property (IP) theft. The case study was developed to teach information security management (ISM) principles in key areas such as strategy, risk, policy and training to postgraduate Information Systems and Information Technology students at the University of Melbourne, Australia.
In this short paper we argue that to combat APTs, organizations need a strategic level shift away from a traditional prevention centered approach to that of a response centered one. Drawing on the information warfare (IW) paradigm in military studies, and using Dynamic Capability Theory (DCT), this research examines the applicability of IW capabilities in the corporate domain. We propose a research framework to argue that conventional prevention centred response capabilities; such as incident response capabilities and IW centred security capabilities can be integrated into IW enabled dynamic response capabilities that improve enterprise security performance.
Training high performance Deep Neural Networks (DNNs) models require large-scale and high-quality datasets. The expensive cost of collecting and annotating large-scale datasets make the valuable datasets can be considered as the Intellectual Property (IP) of the dataset owner. To date, almost all the copyright protection schemes for deep learning focus on the copyright protection of models, while the copyright protection of the dataset is rarely studied. In this paper, we propose a novel method to actively protect the dataset from being used to train DNN models without authorization. Experimental results on on CIFAR-10 and TinyImageNet datasets demonstrate the effectiveness of the proposed method. Compared with the model trained on clean dataset, the proposed method can effectively make the test accuracy of the unauthorized model trained on protected dataset drop from 86.21% to 38.23% and from 74.00% to 16.20% on CIFAR-10 and TinyImageNet datasets, respectively.
This paper presents a high-level circuit obfuscation technique to prevent the theft of intellectual property (IP) of integrated circuits. In particular, our technique protects a class of circuits that relies on constant multiplications, such as filters and neural networks, where the constants themselves are the IP to be protected. By making use of decoy constants and a key-based scheme, a reverse engineer adversary at an untrusted foundry is rendered incapable of discerning true constants from decoy constants. The time-multiplexed constant multiplication (TMCM) block of such circuits, which realizes the multiplication of an input variable by a constant at a time, is considered as our case study for obfuscation. Furthermore, two TMCM design architectures are taken into account; an implementation using a multiplier and a multiplierless shift-adds implementation. Optimization methods are also applied to reduce the hardware complexity of these architectures. The well-known satisfiability (SAT) and automatic test pattern generation (ATPG) attacks are used to determine the vulnerability of the obfuscated designs. It is observed that the proposed technique incurs small overheads in area, power, and delay that are comparable to the hardware complexity of prominent logic locking methods. Yet, the advantage of our approach is in the insight that constants -- instead of arbitrary circuit nodes -- become key-protected.
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks.
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