81 research outputs found
Synthesis and Pruning as a Dynamic Compression Strategy for Efficient Deep Neural Networks
The brain is a highly reconfigurable machine capable of task-specific
adaptations. The brain continually rewires itself for a more optimal
configuration to solve problems. We propose a novel strategic synthesis
algorithm for feedforward networks that draws directly from the brain's
behaviours when learning. The proposed approach analyses the network and ranks
weights based on their magnitude. Unlike existing approaches that advocate
random selection, we select highly performing nodes as starting points for new
edges and exploit the Gaussian distribution over the weights to select
corresponding endpoints. The strategy aims only to produce useful connections
and result in a smaller residual network structure. The approach is
complemented with pruning to further the compression. We demonstrate the
techniques to deep feedforward networks. The residual sub-networks that are
formed from the synthesis approaches in this work form common sub-networks with
similarities up to ~90%. Using pruning as a complement to the strategic
synthesis approach, we observe improvements in compression.Comment: 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE
MANAGEMENT, 9th International Symposium DATAMOD 2020 FROM DATA TO MODELS AND
BACK, 16 Pages, 7 Figures, 3 Tables, 2 Equation
A Distributed Transaction and Accounting Model for Digital Ecosystem Composed Services
This paper addresses two known issues for dynamically
composed services in digital ecosystems. The first
issue is that of efficient distributed transaction management.
The conventional view of transactions is unsuitable as the local
autonomy of the participants is vital for the involvement of
SMEs. The second issue is that of charging for such distributed
transactions, where there will often be dynamically created
services whose composition is not known in advance and might
involve parts of different transactions. The paper provides solutions
for both of these issues, which can be combined to provide
for a unified approach to transaction management and
accounting of dynamically composed services in digital ecosystems
Long-running Transactions: Semantics, schemas, implementation
In this paper we describe a formal model for the distributed coordination of long-running transactions in a Digital Ecosystem for business, involving Small and Medium Enterprises (SMEs). The proposed non-interleaving model of interaction-based service composition allows for communication between internal activities of transactions. The formal semantics of the various modes of service composition are represented by standard xml schemas. The current implementation framework uses suitable asynchronous message passing techniques and reflects the design decisions of the proposed model for distributed transactions in digital ecosystems
The end of Eavesdropping Attacks through the Use of Advanced End to End Encryption Mechanisms
In this article we present our novel Secure Node End-2-End Encryption (SNE2EE) mechanism that is under implementation. This mechanism offers both a software and hardware solution by extending encryption technologies and techniques to the end nodes to increase privacy. The SNE2EE mechanism can address the issues concerning spyware and stalkerware at both the individual and community level
A structural characterisation of the mitogen-activated protein kinase network in cancer
Gene regulatory networks represent collections of regulators that interact with each other
and with other molecules to govern gene expression. Biological signalling networks model how
signals are transmitted and how activities are coordinated in the cell. The study of the structure
of such networks in complex diseases such as cancer can provide insights into how they function,
and consequently, suggest suitable treatment approaches. Here, we explored such topological
characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network
derived from published studies in cancer. We employed well-established techniques to conduct
network analyses, and collected information on gene function as obtained from large-scale public
databases. This allowed us to map topological and functional relationships, and build hypotheses
on this network’s functional consequences. In particular, we find that the topology of this MAPK
network is highly non-random, modular and robust. Moreover, analysis of the network’s structure
indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical
manner across communities of the network. Finally, our results indicate that the organisation of this
network renders it problematic to use treatment approaches that focus on a single target. Our analysis
suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network
functions. Overall, we propose that complex network analyses combined with pharmacological
insights will help inform on future treatment strategies, exploiting structural vulnerabilities of
signalling and regulatory networks in canc
Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study
The file attached to this record is the author's final peer reviewed version.In this paper, we present a survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods
HoneyCar: a framework to configure honeypot vulnerabilities on the internet of vehicles
The Internet of Vehicles (IoV), whereby interconnected vehicles that communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. To protect these entities and learn about adversaries, data on attackers can be realistically gathered using decoy systems like honeypots. Admittedly, honeypots introduces a trade-off between the level of honeypot-attacker interactions and incurred overheads and costs for implementing and monitoring these systems. Deception through honeypots can be achieved by strategically configuring the honeypots to represent components of the IoV to engage attackers and collect cyber threat intelligence. Here, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar benefits from the repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) database to compute optimal honeypot configuration strategies. The adversarial interaction is modelled as a repeated imperfect-information zero-sum game where the IoV network administrator strategically chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability to exploit under uncertainty. Our investigation examines two different versions of the game, with and without the re-configuration cost, to empower the network administrator to determine optimal honeypot investment strategies given a budget. We show the feasibility of this approach in a case study that consists of the vulnerabilities in autonomous and connected vehicles gathered from the CVE database and data extracted from the Common Vulnerability Scoring System (CVSS)
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