Advances in synthetic biology and nanotechnology resulted in tools that can be used to control, reuse, modify, and re-engineer cells' structure, as well as enabling engineers to effectively use biological cells as programmable substrates to realize Bio-NanoThings (biological embedded computing devices). Bio-NanoThings are tiny, non-intrusive, and concealable devices that can be used for in-vivo applications such as intra-body sensing and actuation networks, where the use of artificial devices can be detrimental. Bio-NanoThings are nano-scale devices used in various healthcare settings such as continuous health monitoring, targeted drug delivery, and nanosurgeries. These services can also be grouped to form a collaborative network called nanonetwork, whose performance can potentially be improved when connected to higher bandwidth external networks such as the Internet. However, to realize the IoBNT paradigm, it is also important to seamlessly connect the biological environment with the technological landscape, for example by having a dynamic interface designing to convert biochemical signals from the human body into an equivalent electromagnetic signal (and vice versa). However, this also risks the exposure of internal biological mechanisms to cyber-based sensing and medical actuation, with potential security and privacy implications. In this paper, we comprehensively review bio-cyber interface for IoBNT architecture, focusing on bio-cyber interfacing options for IoBNT like biologically inspired bio-electronic devices, RFID enabled implantable chips, and electronic tattoos. We also identify known and potential security and privacy vulnerabilities and mitigation strategies for consideration in future IoBNT designs and implementations.INDEX TERMS Bio-cyber interface, Internet of Bio-Nano Things, Bio-electronic device security, Bio-inspired security approaches I. INTRODUCTIONWith recent pandemics and the associated lockdown regime, there has been renewed, if not accelerated, interest, in exploring electronic and remote delivery of healthcare services. One of the recent trends is in the Internet of Bio-Nano Things (IoBNT) systems, which

This article presents a four-element multiple-input multiple-output (MIMO) antenna design for next-generation millimeter-wave (mmWave) communication systems. The single antenna element of the MIMO systems consists of a T-shaped and plow-shaped patch radiator designed on an ultra-thin Rogers RT/Duroid 5880 substrate. The dimensions of the single antenna are 10 × 12 mm2. The MIMO system is designed by placing four elements in a polarization diversity configuration whose overall dimensions are 24 × 24 mm2. From the measured results, it is observed that the MIMO antenna provides 9.23 GHz impedance bandwidth ranging from 22.43 to 31.66 GHz. In addition, without the utilization of any decoupling network, a minimum isolation of 25 dB is achieved between adjacent MIMO elements. Furthermore, the proposed MIMO antenna system is fabricated, and it is noted that the simulated results are in good agreement with the measured results. Through the achieved results, it can be said that the proposed MIMO antenna system can be used in 5G mmWave radio frequency (RF) front-ends.

This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples.

Recently, advancements in energy distribution models have fulfilled the needs of microgrids in finding a suitable energy distribution model between producer and consumer without the need of central controlling authority. Most of the energy distribution model deals with energy transactions and losses without considering the security aspects such as information tampering. The transaction data could be accessible online to keep track of the energy distribution between the consumer and producer (e.g., online payment records and supplier profiles). However this data is prone to modification and misuse if a consumer moves from one producer to other. Blockchain is considered to be one solution to allow users to exchange energy related data and keep track of it without exposing it to modification. In this paper, electrical transactions embedded in blockchain are validated using the signatures of multiple producers based on their assigned attributes. These signatures are verified and endorsed by the consumers satisfying those attributes without revealing any information. The public and private keys for these consumers are generated by the producers and endorsement procedure using these keys ensures that these consumers are authorized. This approach does not need any central authority. To resist against collision attacks, producers are given a secret pseudorandom function seed. The comparative analysis shows the efficiency of proposed approach over the existing ones.