Biblio

In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.

Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of user's eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a student's ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.

Multi-Processor Systems-on-Chips (MPSoCs) are a platform for a wide variety of applications and use-cases. The high on-chip connectivity, the programming flexibility, and the reuse of IPs, however, also introduce security concerns. Problems arise when applications with different trust and protection levels share resources of the MPSoC, such as processing units, cache memories and the Network-on-Chip (NoC) communication structure. If a program gets compromised, an adversary can observe the use of these resources and infer (potentially secret) information from other applications. In this work, we explore the cache-based attack by Bogdanov et al., which infers the cache activity of a target program through timing measurements and exploits collisions that occur when the same cache location is accessed for different program inputs. We implement this differential cache-collision attack on the MPSoC Glass and introduce an optimized variant of it, the Earthquake Attack, which leverages the NoC-based communication to increase attack efficiency. Our results show that Earthquake performs well under different cache line and MPSoC configurations, illustrating that cache-collision attacks are considerable threats on MPSoCs.

BP algorithm used by autoencoder classification algorithm. But the BP algorithm is not only complicated and inefficient, but sometimes falls into local optimum. This makes autoencoder classification algorithm are not very good. So in this paper we combie Quantum Particle Swarm Optimization (QPSO) and autoencoder classification algorithm. QPSO used to optimize the weight of autoencoder neural network and the parameter of softmax. This method has been tested on some database, and the experimental result shows that this method has got good results.

In this paper, we highlight and study the threat arising from the unattended wearable devices pre-paired with a smartphone over a wireless communication medium. Most users may not lock their wearables due to their small form factor, and may strip themselves off of these devices often, leaving or forgetting them unattended while away from homes (or shared office spaces). An “insider” attacker (potentially a disgruntled friend, roommate, colleague, or even a spouse) can therefore get hold of the wearable, take it near the user's phone (i.e., within radio communication range) at another location (e.g., user's office), and surreptitiously use it across physical barriers for various nefarious purposes, including pulling and learning sensitive information from the phone (such as messages, photos or emails), and pushing sensitive commands to the phone (such as making phone calls, sending text messages and taking pictures). The attacker can then safely restore the wearable, wait for it to be left unattended again and may repeat the process for maximum impact, while the victim remains completely oblivious to the ongoing attack activity. This malicious behavior is in sharp contrast to the threat of stolen wearables where the victim would unpair the wearable as soon as the theft is detected. Considering the severity of this threat, we also respond by building a defense based on audio proximity, which limits the wearable to interface with the phone only when it can pick up on an active audio challenge produced by the phone.

In this work, a measurement system is developed based on acoustic resonance which can be used for classification of materials. Basically, the inspection methods based on acoustic, utilized for containers screening in the field, identification of defective pills hold high significance in the fields of health, security and protection. However, such techniques are constrained by costly instrumentation, offline analysis and complexities identified with transducer holder physical coupling. So a simple, non-destructive and amazingly cost effective technique in view of acoustic resonance has been formulated here for quick data acquisition and analysis of acoustic signature of liquids for their constituent identification and classification. In this system, there are two ceramic coated piezoelectric transducers attached at both ends of V-shaped glass, one is act as transmitter and another as receiver. The transmitter generates sound with the help of white noise generator. The pick up transducer on another end of the V-shaped glass rod detects the transmitted signal. The recording is being done with arduino interfaced to computer. The FFTs of recorded signals are being analyzed and the resulted resonant frequency observed for water, water+salt and water+sugar are 4.8 KHz, 6.8 KHz and 3.2 KHz respectively. The different resonant frequency in case different sample is being observed which shows that the developed prototype model effectively classifying the materials.