DESIGNING OF METASURFACE FOR GAIN ENHANCEMENT IN 5G
APPLICATIONS
Introduction
Metasurfaces change optical characteristics like amplitude,
polarization, refractive index.
Used without phase gradient, limiting applications.
Planar structure allows low loss and fabrication with techniques.
Lower cost than metamaterials.
Design Methodology
Metasurface and unit cell designed and analyzed based on
transmission and reflection coefficients.
Unit Cell and Metasurface Design
Designed on CST Microwave Studio.
RT6010LM substrate with copper patch and slots.
Unit cell replicated to form 8x5 metasurface array.
Results and Discussion
Unit cell shows max reflection at 8.9 and 28.11 GHz.
Metasurface also shows max reflection at same frequencies.
Min transmission indicates power reflected back.
Conclusion
Unit cell and metasurface designed and analyzed.
Show max reflection and min transmission at 8.9 and 28.11 GHz.
Can enhance antenna gain when placed at backside.
Gain improved by rotating unit cells.
MICROSTRIP CIRCULAR PATCH ANTENNA DESIGN
Introduction
Microstrip antennas advantageous over traditional antennas for
wireless applications like Bluetooth, WLAN.
Bluetooth technology requires antennas operating around 2.4 GHz.
Microstrip patch antenna consists of conducting patch, substrate,
and ground plane.
Antenna Design
Circular patch antenna on FR4 substrate with dielectric constant 4.4.
Fed by 50 ohm microstrip line with quarter wave transformer.
Rectangular slot added at patch center to improve gain.
Results and Discussion
Simulated in HFSS and fabricated on FR4 substrate.
Without slot: 2.6 GHz resonance, -22 dB return loss, 2 dBi gain.
With slot: 2.4 GHz resonance, -30 dB return loss, 2.1 dBi gain.
Slot shifts resonance down and improves return loss and gain.
Fabricated Antenna
Fabricated design tested using VNA.
Shows 2.47 GHz resonance at -25.16 dB.
Close agreement between simulated and measured results.
Conclusions
Circular patch antenna designed for 2.4 GHz Bluetooth band.
Slot addition improves resonant frequency, return loss and gain.
Test results validate simulations.
Can be used for Bluetooth applications at 2.4 GHz.
Array can improve gain further.
GRAPH-BASED MODEL FOR DISCOVERING HOST-BASED HOOK
ATTACKS
Introduction
Hacktivists and cyber-criminals today are capable of writing malwares with
advanced evading techniques and continue to evolve different techniques
with the intent of assaulting end-user’s privacy. A new type of malware is
launched every day by modifying its predecessor. The AV-Test report
detected more than 500 million malware samples in 2018, making manual
analysis a tedious process. Hence, there is a need for an automated
malware analysis technique to craft virus definitions.
Malware Detection Techniques
Malware developers often integrate rootkit techniques, which mainly use
an API hook technique into malware software to avoid detection. A
malware detector is a software program that can be operated locally on
the victim computer to discover and locate a malware. There are two
different kinds of inputs given to a malware detector: the unique signature
of the malware or monitoring its behavior.
Challenges in Malware Detection
A huge number of malicious samples are submitted frequently to security
companies for analysis. To expose hijacked API calls, we need a behavioral
monitoring system which categorizes malicious activities and legitimate
activities. However, analyzing a large amount of malware-infected
information to recognize its intended attack is typically a difficult issue.
Limitations of Network-Based Analysis
Network-based analysis approaches have several limitations. A malware
packet may imitate as a legitimate packet to avert detection. If the
payload of a malware is encrypted, then collecting and analyzing network
traffic cannot reveal its presence. Network-based approaches fail to sense
malicious activities when they cannot communicate with a remote
attacker.
Host-Based Malicious Code Detection Techniques
In addition to signature-based approach, another fitting place to supervise
and investigate malware behavior is at the end-host. We can detect a
malicious code attack even before it gets executed in the victim computer.
However, current host-based malicious code detection techniques do not
use effective models.
API Call Graph (ACG)
An API call graph (ACG) is a suitable data illustration of the data and
control flood of software programs. It offers information about local data
usage of a procedure and global data that can be exchanged between
different procedures. Call graph acts as a suitable tool either to study the
behavior of a program or for tracking the flow values between different
components of a program.
Proposed ACG Framework
In this paper, an ACG framework for detecting malicious software that
uses API hook attacks based on the synthesis of static and dynamic
analysis technique is presented. The theme includes:
Use of static and dynamic analysis methods for the identification
and extraction of API invocation calls and its associated parameters.
Devising an API system call-dependent graph algorithm to generate
graphs from the extracted information.
Implementation of ACG algorithm to compare all data-dependent
graphs which can identify whether an API call made by the
executable is either legitimate or malicious.
