Biblio

We propose versatile hardware framework for ECC. The framework supports arithmetic operations over P-256, Ed25519 and Curve25519 curves, enabling easy implementation of various ECC algorithms. Framework finds its application area e.g. in FIDO2 attestation or in nowadays rapidly expanding field of hardware wallets. As the design is intended to be ASIC-ready, we designed it to be area efficient. Hardware units are reused for calculations in several finite fields, and some of them are superior to previously designed circuits in terms of time-area product. The framework implements several attack countermeasures. It enables implementation of certain countermeasures even in later stages of design. The design was validated on SoC FPGA.

Recently, the threats of phishing attacks have in-creased. As a countermeasure against phishing attacks, phishing detection systems using deep learning techniques have been considered. However, deep learning techniques are vulnerable to adversarial examples (AEs) that intentionally cause misclassification. When AEs are applied to a deep-learning-based phishing detection system, they pose a significant security risk. Therefore, in this paper, we assess the vulnerability of a phishing detection system by inputting AEs generated based on a dataset that consists of phishing sites’ URLs. Moreover, we consider countermeasures against AEs and clarify whether these defense methods can prevent misclassification.

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data.

In this proposed method, the traditional elevators are upgraded in such a way that any alarming situation in the elevator can be detected and then sent to a main center where further action can be taken accordingly. Different emergency situation can be handled by implementing the system. Smart elevator system works by installing different modules inside the elevator such as speed sensors which will detect speed variations occurring above or below a certain threshold of elevator speed. The smart elevator system installed within the elevator sends a message to the emergency response center and sends an automated call as well. The smart system also includes an emotion detection algorithm which will detect emotions of the individual based on their expression in the elevator. The smart system also has a whisper detection system as well to know if someone stuck inside the elevator is alive during any hazardous situation. A broadcast signal is used as a check in the elevator system to evaluate if every part of the system is in stable state. Proposed system can completely replace the current elevator systems and become part of smart homes.

Quadrature compressive sampling (QuadCS) is a newly introduced sub-Nyquist sampling for acquiring inphase and quadrature components of radio-frequency signals. This paper develops a target detection scheme of pulsed-type radars in the presence of digital radio frequency memory (DRFM) repeat jammers with the radar echoes sampled by the QuadCS system. For diversifying pulses, the proposed scheme first separates the target echoes from the DRFM repeat jammers using CS recovery algorithms, and then removes the jammers to perform the target detection. Because of the separation processing, the jammer leakage through range sidelobe variation of the classical match-filter processing will not appear. Simulation results confirm our findings. The proposed scheme with the data at one eighth the Nyquist rate outperforms the classic processing with Nyquist samples in the presence of DRFM repeat jammers.