Abstract

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing and denial of service (DoS) attacks, and malware attacks. Among these attacks, the attacks which are performed through the malware botnet (i.e., Mirai) are the malignant attacks. The existence of malware botnets leads to attacks on confidentiality, integrity, authenticity and availability of the data and other resources of the system. In presence of such attacks, the sensitive data of IoT communication may be disclosed, altered or even may not be available to the authorized users. Therefore, it becomes essential to protect the IoT/IoMT environment from malware attacks. In this review paper, we first perform the study of various types of malware attacks, and their symptoms. We also discuss some architectures of IoT environment along with their applications. Next, a taxonomy of security protocols in IoT environment is provided. Moreover, we conduct a comparative study on various existing schemes for malware detection and prevention in IoT environment. Finally, some future research challenges and directions of malware detection in IoT/IoMT environment are highlighted.

Abstract

As we all know that the technology is projected to be next to humans very soon because of its holistic growth. Now-a-days, we see a lot of applications that are making our lives comfortable such as smart cars, smart homes, smart traffic management, smart offices, smart medical consultation, smart cities, etc. All such facilities are in the reach of a common man because of the advancement in Information and Communications Technology (ICT). Because of this advancement, new computing and communication environment such as Internet of Things (IoT) came into picture. Lot of research work is in progress in IoT domain which helps for the overall development of the society and makes the lives easy and comfortable. But in the resource constrained environment of Wireless Sensor Network (WSN) and IoT, it is almost inconceivable to establish a fully secure system. As we are moving forward very fast, technology is becoming more and more vulnerable to the security threats. In future, the number of Internet connected people will be less than the smart objects so we need to prepare a robust system for keeping the above mentioned environments safe and standardized it for the smooth conduction of communication among IoT objects. In this survey paper, we provide the details of threat model applicable for the security of WSN and IoT based communications. We also discuss the security requirements and various attacks possible in WSN and IoT based communication environments. The emerging projects of WSNs integrated to IoT are also briefed. We then provide the details of different architectures of WSN and IoT based communication environments. Next, we discuss the current issues and challenges related to WSN and IoT. We also provide a critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis. A taxonomy of security and privacy-preservation protocols in WSN and IoT is also highlighted. Finally, we discuss some research challenges which need to be addressed in the coming future.

Abstract

Mobile banking refers to the use of a mobile device (e.g., a smartphone) to perform online banking activities, while away from the home computer, for transferring funds, monitoring account balance and bill payments, and so on. It provides a great convenience to bank customers, but it suffers from various types of attacks that are possible on the mobile banking infrastructure. We present the evolution of mobile banking and discuss the various threats associated with it as well as some of the most recent malware attacks. Finally, we include a review of recent security solutions for enabling secure mobile banking.

Abstract

A user (external party) is interested in accessing the real-time data from some designated drones of a particular fly zone in the Internet of Drones (IoD) deployment. However, to provide this facility, the user needs to be authenticated by an accessed remote drone and vice-versa. After successful authentication both parties can establish a secret session key for the secure communication. To handle this important problem in IoD environment, we design a novel temporal credential based anonymous lightweight user authentication mechanism for IoD environment, called TCALAS. A detailed security analysis using formal security under the broadly applied real-or-random (ROR) model, formal security verification under the broadly used software verification tool, known as automated validation of internet security protocols and applications, and also informal security analysis reveal that TCALAS has the capability to resist various known attacks against passive/active adversary. In addition, a detailed comparative study has been conducted for TCALAS and other related schemes, and the study also reveals that TCALAS provides better security and functionality features, and lower costs in both computation and communication as compared to existing schemes.

Abstract

This paper proposes the use of Shewharttest to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggy-backing and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime

Abstract

In this paper, sum link capacity expressions for successive interference cancellation (SIC) and regarding interference as noise (IAN) in reverse time-division duplexing (RTDD) heterogeneous cellular network are derived. The considered RTDD network always operates in a synchronized fashion such that if the macro tier is in the uplink (UL), then the small tier will be in the downlink (DL) and vice-versa. Rate and power throttling are used in the uplink (UL) for both IAN and SIC to consider an asymmetric traffic network (DL≫ UL). Systemlevel simulations are performed to compare the overall system throughput of IAN and SIC for different DL/UL ratios. It is observed that rate or power-throttled SIC performs better than rate-throttled IAN and worse than power-throttled IAN.

Abstract

Sparse arrays such as co-prime and nested arrays can identify more sources than the number of sensors. This is because their difference co-arrays contain a uniformly spaced virtual array with more elements than the number of sensors in the array. In this paper we demonstrate this using two dimensional co-prime and nested sparse arrays combined with sparse Bayesian learning (SBL) for 2D beamforming in azimuth and elevation. SBL can directly process the sparse array data and significantly outperform conventional beam-forming and MUSIC as seen from simulations.

Abstract

Sparse Bayesian learning (SBL) has emerged as a fast and competitive method to perform sparse processing. The SBL algorithm, which is developed using a Bayesian framework, iteratively solves a non-convex optimization problem using fixed point updates. It provides comparable performance and is significantly faster than convex optimization techniques used in sparse processing. We propose a multi-dictionary SBL algorithm that simultaneously can process observations generated by different underlying dictionaries sharing the same sparsity profile. Two algorithms are proposed and corresponding fixed point update equations are derived. Noise variances are estimated using stochastic maximum likelihood. The multi-dictionary SBL has many practical applications. We demonstrate this using direction-of-arrival (DOA) estimation. The first example uses the proposed multi-dictionary SBL to process multi …

Abstract

The paper considers direction of arrival (DOA) estimation from long-term observations in a very noisy environment. The concern is to derive methods obtaining reasonable DOAs at very low SNR. The noise is assumed zero-mean Gaussian and its variance varies in time and space, causing stationary data models to fit poorly over long observation times. Therefore a heteroscedastic Gaussian noise model is introduced where the variance varies across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. This SBL approach is more flexible and performs better …

Abstract

Page 1. Multi-frequency sparse Bayesian learning with noise models Kay L. Gemba1, Santosh Nannuru, and Peter Gerstoft ASA SD FALL 2019 Marine Physical Laboratory of the Scripps Institution of Oceanography University of California at San Diego 1gemba@ucsd.edu 1 Page 2. Presentation objectives We investigate SBL performance and present results for the MFP and beamforming applications using 3 noise models: 1. SBL behaves similarly to an adaptive processor and displays robustness to modest model-data mismatch. SBL performance is compared to the white noise gain constraint (WNGC), MUSIC, and Bartlett processors. 2. Compare SBL performance, implemented with 3 noise models: 1. SBL1: stationary noise, 2. SBL2: non-stationary noise, which is useful when the noise variance evolves across snapshots, 3. SBL3: non-stationary noise in a spatially, non-homogeneous field (example: surface noise) …