19,399 research outputs found
An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection
Sophisticated automatic incident detection (AID) technology plays a key role
in contemporary transportation systems. Though many papers were devoted to
study incident classification algorithms, few study investigated how to enhance
feature representation of incidents to improve AID performance. In this paper,
we propose to use an unsupervised feature learning algorithm to generate higher
level features to represent incidents. We used real incident data in the
experiments and found that effective feature mapping function can be learnt
from the data crosses the test sites. With the enhanced features, detection
rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are
significantly improved in all of the three representative cases. This approach
also provides an alternative way to reduce the amount of labeled data, which is
expensive to obtain, required in training better incident classifiers since the
feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation
Systems (ITSC 2012
Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery
Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together
EviPlant: An efficient digital forensic challenge creation, manipulation and distribution solution
Education and training in digital forensics requires a variety of suitable
challenge corpora containing realistic features including regular
wear-and-tear, background noise, and the actual digital traces to be discovered
during investigation. Typically, the creation of these challenges requires
overly arduous effort on the part of the educator to ensure their viability.
Once created, the challenge image needs to be stored and distributed to a class
for practical training. This storage and distribution step requires significant
time and resources and may not even be possible in an online/distance learning
scenario due to the data sizes involved. As part of this paper, we introduce a
more capable methodology and system as an alternative to current approaches.
EviPlant is a system designed for the efficient creation, manipulation, storage
and distribution of challenges for digital forensics education and training.
The system relies on the initial distribution of base disk images, i.e., images
containing solely base operating systems. In order to create challenges for
students, educators can boot the base system, emulate the desired activity and
perform a "diffing" of resultant image and the base image. This diffing process
extracts the modified artefacts and associated metadata and stores them in an
"evidence package". Evidence packages can be created for different personae,
different wear-and-tear, different emulated crimes, etc., and multiple evidence
packages can be distributed to students and integrated into the base images. A
number of additional applications in digital forensic challenge creation for
tool testing and validation, proficiency testing, and malware analysis are also
discussed as a result of using EviPlant.Comment: Digital Forensic Research Workshop Europe 201
Design Challenges for GDPR RegTech
The Accountability Principle of the GDPR requires that an organisation can
demonstrate compliance with the regulations. A survey of GDPR compliance
software solutions shows significant gaps in their ability to demonstrate
compliance. In contrast, RegTech has recently brought great success to
financial compliance, resulting in reduced risk, cost saving and enhanced
financial regulatory compliance. It is shown that many GDPR solutions lack
interoperability features such as standard APIs, meta-data or reports and they
are not supported by published methodologies or evidence to support their
validity or even utility. A proof of concept prototype was explored using a
regulator based self-assessment checklist to establish if RegTech best practice
could improve the demonstration of GDPR compliance. The application of a
RegTech approach provides opportunities for demonstrable and validated GDPR
compliance, notwithstanding the risk reductions and cost savings that RegTech
can deliver. This paper demonstrates a RegTech approach to GDPR compliance can
facilitate an organisation meeting its accountability obligations
Alternative sweetener from curculigo fruits
This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes
Malware in the Future? Forecasting of Analyst Detection of Cyber Events
There have been extensive efforts in government, academia, and industry to
anticipate, forecast, and mitigate cyber attacks. A common approach is
time-series forecasting of cyber attacks based on data from network telescopes,
honeypots, and automated intrusion detection/prevention systems. This research
has uncovered key insights such as systematicity in cyber attacks. Here, we
propose an alternate perspective of this problem by performing forecasting of
attacks that are analyst-detected and -verified occurrences of malware. We call
these instances of malware cyber event data. Specifically, our dataset was
analyst-detected incidents from a large operational Computer Security Service
Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on
automated systems. Our data set consists of weekly counts of cyber events over
approximately seven years. Since all cyber events were validated by analysts,
our dataset is unlikely to have false positives which are often endemic in
other sources of data. Further, the higher-quality data could be used for a
number for resource allocation, estimation of security resources, and the
development of effective risk-management strategies. We used a Bayesian State
Space Model for forecasting and found that events one week ahead could be
predicted. To quantify bursts, we used a Markov model. Our findings of
systematicity in analyst-detected cyber attacks are consistent with previous
work using other sources. The advanced information provided by a forecast may
help with threat awareness by providing a probable value and range for future
cyber events one week ahead. Other potential applications for cyber event
forecasting include proactive allocation of resources and capabilities for
cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs.
Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa
Learning from accidents : machine learning for safety at railway stations
In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety
in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry
An investigation into the perspectives of providers and learners on MOOC accessibility
An effective open eLearning environment should consider the target learner’s abilities, learning goals, where learning takes place, and which specific device(s) the learner uses. MOOC platforms struggle to take these factors into account and typically are not accessible, inhibiting access to environments that are intended to be open to all. A series of research initiatives are described that are intended to benefit MOOC providers in achieving greater accessibility and disabled learners to improve their lifelong learning and re-skilling. In this paper, we first outline the rationale, the research questions, and the methodology. The research approach includes interviews, online surveys and a MOOC accessibility audit; we also include factors such the risk management of the research programme and ethical considerations when conducting research with vulnerable learners. Preliminary results are presented from interviews with providers and experts and from analysis of surveys of learners. Finally, we outline the future research opportunities. This paper is framed within the context of the Doctoral Consortium organised at the TEEM'17 conference
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