Trends and Prospects in Security, Encryption and Encoding

Abstract submission deadline

30 November 2023

Manuscript submission deadline

29 February 2024

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Topic Information

Dear Colleagues,

Multimedia data can be defined as a combination of different data types such as text, audio, images, and video. Every day, a huge quantity of data is transmitted through the internet and other open networks. Securing the transmitted data and preventing any misuse of it is a big challenge. Various security methodologies, such as digital watermarking, data encryption, steganography, data hiding, and blockchain, have been developed for securing multimedia data.

Digital watermarking is used in copyright protection and in securing multimedia data through a networked environment. Data encryption or cryptographic methods encrypt the data at the sender side, transmit this data from the sender to the receiver, and then decrypt it at the receiver side. In image steganography, the message image is hidden in a cover image and changes its properties, providing a secret communication method which prevents hackers/attackers from detecting the message’s presence.

During the last decade, several remarkable methodologies have been developed to improve the levels of multimedia security. Blockchain is an emerging technique to keep the data within an open decentralized network.

Eligible papers will cover theoretical and applied issues including, but not limited to, the following:

• Principles of data security and emerging hybrid techniques.
• Image and video encryption, watermarking, steganography, and data hiding.
• Speech and audio encryption, watermarking, steganography, and data hiding.
• Multimedia security using blockchain.
• FPGA-based implementation for multimedia security.
• Embedded hardware for multimedia security.
• Applications of multimedia security in smart cities.
• Deep learning techniques for modelling threats and vulnerabilities in software.
• Automatic modelling of software and hardware attacks and defences using artificial intelligence algorithms.
• Adversarial machine learning techniques applied to DevSecOps.
• Automatic prediction of security flaws in software and hardware using deep learning algorithms.
• Artificial intelligence for automatic error correction.
• Artificial intelligence techniques for algorithmic verification.
• Deep learning techniques for the generation and mutation of abnormal application traffic patterns.
• Deep learning techniques for symbolic model checking.
• Use of artificial intelligence techniques for vulnerability prediction.
• Development of AI techniques to measure software resilience.
• Deep learning techniques for the detection of programming errors in binary and modern programming languages.
• Automatic abstraction techniques applicable to programming code.
• Techniques to increase privacy when sharing information.

Prof. Dr. Ki-Hyun Jung
Prof. Dr. Luis Javier García Villalba
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.838	3.7	2011	14.9 Days	2300 CHF	Submit
Cryptography cryptography	-	3.9	2017	20.5 Days	1600 CHF	Submit
Journal of Cybersecurity and Privacy jcp	-	-	2021	20.6 Days	1000 CHF	Submit
Journal of Sensor and Actuator Networks jsan	-	6.9	2012	18.4 Days	1600 CHF	Submit
Sci sci	-	-	2019	37.2 Days	1200 CHF	Submit
Symmetry symmetry	2.940	4.3	2009	14.2 Days	2000 CHF	Submit

Published Papers (3 papers)

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All Applied Sciences Cryptography JCP JSAN Sci Symmetry

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Open AccessArticle

Linear Cryptanalysis of Reduced-Round Simeck Using Super Rounds

by

Reham Almukhlifi

and

Poorvi L. Vora

Cryptography 2023, 7(1), 8; https://doi.org/10.3390/cryptography7010008 - 09 Feb 2023

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Abstract

The Simeck family of lightweight block ciphers was proposed by Yang et al. in 2015, which combines the design features of the NSA-designed block ciphers Simon and Speck. Previously, we proposed the use of linear cryptanalysis using super-rounds to increase the efficiency of [...] Read more.

The Simeck family of lightweight block ciphers was proposed by Yang et al. in 2015, which combines the design features of the NSA-designed block ciphers Simon and Speck. Previously, we proposed the use of linear cryptanalysis using super-rounds to increase the efficiency of implementing Matsui’s second algorithm and achieved good results on all variants of Simon. The improved linear attacks result from the observation that, after four rounds of encryption, one bit of the left half of the state of the cipher depends on only 17 key bits (19 key bits for the larger variants of the cipher). We were able to follow a similar approach, in all variants of Simeck, with an improvement in Simeck 32 and Simeck 48 by relaxing the previous constraint of a single active bit, using multiple active bits instead. In this paper we present improved linear attacks against all variants of Simeck: attacks on 19-rounds of Simeck 32/64, 28-rounds of Simeck 48/96, and 34-rounds of Simeck 64/128, often with the direct recovery of the full master key without repeating the attack over multiple rounds. We also verified the results of linear cryptanalysis on 8, 10, and 12 rounds for Simeck 32/64. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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Open AccessArticle

High Throughput PRESENT Cipher Hardware Architecture for the Medical IoT Applications

by

Jamunarani Damodharan

,

Emalda Roslin Susai Michael

and

Nasir Shaikh-Husin

Cryptography 2023, 7(1), 6; https://doi.org/10.3390/cryptography7010006 - 06 Feb 2023

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Abstract

The Internet of Things (IoT) is an intelligent technology applied to various fields like agriculture, healthcare, automation, and defence. Modern medical electronics is also one such field that relies on IoT. Execution time, data security, power, and hardware utilization are the four significant [...] Read more.

The Internet of Things (IoT) is an intelligent technology applied to various fields like agriculture, healthcare, automation, and defence. Modern medical electronics is also one such field that relies on IoT. Execution time, data security, power, and hardware utilization are the four significant problems that should be addressed in the data communication system between intelligent devices. Due to the risks in the implementation algorithm complexity, certain ciphers are unsuitable for IoT applications. In addition, IoT applications are also implemented on an embedded platform wherein computing resources and memory are limited in number. Here in the research work, a reliable lightweight encryption algorithm with PRESENT has been implemented as a hardware accelerator and optimized for medical IoT-embedded applications. The PRESENT cipher is a reliable, lightweight encryption algorithm in many applications. This paper presents a low latency 32-bit data path of PRESENT cipher architecture that provides high throughput. The proposed hardware architecture has been implemented and tested with XILINX XC7Z030FBG676-2 ZYNQ FPGA board 7000. This work shows an improvement of about 85.54% in throughput with a reasonable trade-off over hardware utilization. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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Open AccessArticle

Building Trusted Federated Learning: Key Technologies and Challenges

by

Depeng Chen

,

Xiao Jiang

,

Hong Zhong

and

Jie Cui

J. Sens. Actuator Netw. 2023, 12(1), 13; https://doi.org/10.3390/jsan12010013 - 06 Feb 2023

Cited by 1 | Viewed by 1126

Abstract

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy [...] Read more.

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy federation learning (TFL) to eliminate users’ anxiety. In this paper, we aim to provide a well-researched picture of the security and privacy issues in FL that can bridge the gap to TFL. Firstly, we define the desired goals and critical requirements of TFL, observe the FL model from the perspective of the adversaries and extrapolate the roles and capabilities of potential adversaries backward. Subsequently, we summarize the current mainstream attack and defense means and analyze the characteristics of the different methods. Based on a priori knowledge, we propose directions for realizing the future of TFL that deserve attention. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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