14 research outputs found
A distributed deep learning approach with mobile edge computing for next generation IoT networks security
Along with recent development in Next Generation IoT, the Deep Learning (DL) has become a promising paradigm to perform various tasks such as computation and analysis. Many security researchers have proposed distributed DL supporting DL task at the IoT device level to deliver low latency and high accuracy. However, due to limited computing capabilities of IoT devices, distributed DL is failed to maintain Quality-of-service demand in practical IoT applications. To this end, BlockDeepEdge, a Blockchain-based Distributed DL with Mobile Edge Computing (MEC) is proposed where MEC supports the lightweight IoT devices by delivering computing operations to them at the edge of the network. The blockchain provide a secure, decentralized and P2P interaction among IoT devices and MEC server to carryout distributed DL operation
Advancing Perception in Artificial Intelligence through Principles of Cognitive Science
Although artificial intelligence (AI) has achieved many feats at a rapid
pace, there still exist open problems and fundamental shortcomings related to
performance and resource efficiency. Since AI researchers benchmark a
significant proportion of performance standards through human intelligence,
cognitive sciences-inspired AI is a promising domain of research. Studying
cognitive science can provide a fresh perspective to building fundamental
blocks in AI research, which can lead to improved performance and efficiency.
In this review paper, we focus on the cognitive functions of perception, which
is the process of taking signals from one's surroundings as input, and
processing them to understand the environment. Particularly, we study and
compare its various processes through the lens of both cognitive sciences and
AI. Through this study, we review all current major theories from various
sub-disciplines of cognitive science (specifically neuroscience, psychology and
linguistics), and draw parallels with theories and techniques from current
practices in AI. We, hence, present a detailed collection of methods in AI for
researchers to build AI systems inspired by cognitive science. Further, through
the process of reviewing the state of cognitive-inspired AI, we point out many
gaps in the current state of AI (with respect to the performance of the human
brain), and hence present potential directions for researchers to develop
better perception systems in AI.Comment: Summary: a detailed review of the current state of perception models
through the lens of cognitive A
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
A New Index based on Power Splitting Indices for Predicting Proper Time of Controlled Islanding
In the event of large disturbances, the practice of controlled islanding is
used as a last resort to prevent cascading outages. The application of the
strategy at the right time is crucial to maintaining system security. A
controlled islanding strategy may be deployed efficiently at the right time by
predicting the time of uncontrolled system splitting. The purpose of this study
is to predict the appropriate islanding time to prevent catastrophic blackout
and uncontrolled islanding based on existing relationships between coherent
generator groups. A new instability index is derived from the proximity of
inter-area oscillations to power splitting indices. Power splitting indices are
derived using synchronization coefficients, which recognize the conditions in
the system that warrant controlled islanding. The critical values of indices
are calculated in offline mode using simulation data from IEEE 39-Buses, and
their online performance is evaluated following a controlled islanding
strategy. Through the introduction of these indices, system degradation can be
effectively evaluated, and blackouts can be predicted early and prevented by
controlled islanding at the right time.Comment: N
A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants
Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. - 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 8-408-2-172.Scopu
Mapping biological systems to network systems
The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully applied in various domains. Nevertheless, it also presents a case study discussing the security aspects of wireless sensor networks and how biological solution stand out in comparison to optimized solutions. Furthermore, it also discusses novel biological solutions for solving problems in diverse engineering domains such as mechanical, electrical, civil, aerospace, energy and agriculture. The readers will not only get proper understanding of the bio inspired systems but also better insight for developing novel bio inspired solutions. Shows how bio-inspired systems – which are inherently robust, flexible and have high resilience towards critical errors -- hold immense potential for next generation network systems Outlines computing and problem solving techniques inspired by biological systems that can provide flexible, adaptable ways of solving networking problems Provides insights into how the study of biological systems can make network systems more flexible, adaptable, self-organized, self-aware, and self-sufficient
Mathematical Evaluation of Human Immune Systems for Securing Software Defined Networks
The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.VI. ACKNOWLEDGEMENTS This publication was made possible by NPRP grant #8-408-2-172 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu
A bio-inspired framework to mitigate dos attacks in software defined networking
Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer and adaptive layer, is initiated at the data layer and the control layer of SDN, respectively. The proposed model is reactive and lightweight for DoS mitigation in SDNs. - 2019 IEEE.Scopu
A review of security challenges, attacks and resolutions for wireless medical devices
Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.This publication was made possible by NPRP grant #8-408-2-172 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu