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21 pages, 2289 KiB

Open AccessArticle

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

by Mazen Gazzan and Frederick T. Sheldon

Information 2024, 15(5), 262; https://doi.org/10.3390/info15050262 - 5 May 2024

Cited by 1 | Viewed by 1464

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES [...] Read more.

Ransomware poses a significant threat by encrypting files or systems demanding a ransom be paid. Early detection is essential to mitigate its impact. This paper presents an Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique for optimizing Deep Belief Networks (DBNs) in ransomware detection. UA-DES leverages Bayesian methods, dropout techniques, and an active learning framework to dynamically adjust the number of epochs during the training of the detection model, preventing overfitting while enhancing model accuracy and reliability. Our solution takes a set of Application Programming Interfaces (APIs), representing ransomware behavior as input we call “UA-DES-DBN”. The method incorporates uncertainty and calibration quality measures, optimizing the training process for better more accurate ransomware detection. Experiments demonstrate the effectiveness of UA-DES-DBN compared to more conventional models. The proposed model improved accuracy from 94% to 98% across various input sizes, surpassing other models. UA-DES-DBN also decreased the false positive rate from 0.18 to 0.10, making it more useful in real-world cybersecurity applications. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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17 pages, 2060 KiB

Open AccessArticle

An Incremental Mutual Information-Selection Technique for Early Ransomware Detection

by Mazen Gazzan and Frederick T. Sheldon

Information 2024, 15(4), 194; https://doi.org/10.3390/info15040194 - 31 Mar 2024

Viewed by 1344

Abstract

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training [...] Read more.

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training such a detection model is choosing a set of relevant and non-redundant features, which is challenging when data are scarce. Therefore, this paper proposes an incremental mutual information-selection technique as a method for selecting the relevant features at the early stages of ransomware attacks. It introduces an adaptive feature-selection technique that processes data in smaller, manageable batches. This approach lessens the computational load and enhances the system’s ability to quickly adapt to new data arrival, making it particularly suitable for ongoing attacks during the initial phases of the attack. The experimental results emphasize the importance of the proposed technique in estimating feature significance in limited data scenarios. Such results underscore the significance of the incremental approach as a proactive measure in addressing the escalating challenges posed by ransomware. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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15 pages, 400 KiB

Open AccessArticle

A Novel Hardware Architecture for Enhancing the Keccak Hash Function in FPGA Devices

by Argyrios Sideris, Theodora Sanida and Minas Dasygenis

Information 2023, 14(9), 475; https://doi.org/10.3390/info14090475 - 28 Aug 2023

Cited by 8 | Viewed by 2323

Abstract

Hash functions are an essential mechanism in today’s world of information security. It is common practice to utilize them for storing and verifying passwords, developing pseudo-random sequences, and deriving keys for various applications, including military, online commerce, banking, healthcare management, and the Internet [...] Read more.

Hash functions are an essential mechanism in today’s world of information security. It is common practice to utilize them for storing and verifying passwords, developing pseudo-random sequences, and deriving keys for various applications, including military, online commerce, banking, healthcare management, and the Internet of Things (IoT). Among the cryptographic hash algorithms, the Keccak hash function (also known as SHA-3) stands out for its excellent hardware performance and resistance to current cryptanalysis approaches compared to algorithms such as SHA-1 and SHA-2. However, there is always a need for hardware enhancements to increase the throughput rate and decrease area consumption. This study specifically focuses on enhancing the throughput rate of the Keccak hash algorithm by presenting a novel architecture that supplies efficient outcomes. This novel architecture achieved impressive throughput rates on Field-Programmable Gate Array (FPGA) devices with the Virtex-5, Virtex-6, and Virtex-7 models. The highest throughput rates obtained were 26.151 Gbps, 33.084 Gbps, and 38.043 Gbps, respectively. Additionally, the research paper includes a comparative analysis of the proposed approach with recently published methods and shows a throughput rate above 11.37% Gbps in Virtex-5, 10.49% Gbps in Virtex-6 and 11.47% Gbps in Virtex-7. This comparison allows for a comprehensive evaluation of the novel architecture’s performance and effectiveness in relation to existing methodologies. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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28 pages, 1891 KiB

Open AccessArticle

Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios

by Athanasios Psaltis, Kassiani Zafeirouli, Peter Leškovský, Stavroula Bourou, Juan Camilo Vásquez-Correa, Aitor García-Pablos, Santiago Cerezo Sánchez, Anastasios Dimou, Charalampos Z. Patrikakis and Petros Daras

Information 2023, 14(6), 342; https://doi.org/10.3390/info14060342 - 16 Jun 2023

Cited by 2 | Viewed by 1874

Abstract

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training [...] Read more.

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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21 pages, 684 KiB

Open AccessArticle

Secure and Efficient Exchange of Threat Information Using Blockchain Technology

by Maryam Pahlevan and Valentin Ionita

Information 2022, 13(10), 463; https://doi.org/10.3390/info13100463 - 28 Sep 2022

Cited by 3 | Viewed by 1997

Abstract

In recent years, sharing threat information has been one of the most suggested solutions for combating the ever-increasing number of cyberattacks, which stem from the system-wide adoption of Information and Communication Technology (ICT) and consequently endangers the digital and physical assets of organizations. [...] Read more.

In recent years, sharing threat information has been one of the most suggested solutions for combating the ever-increasing number of cyberattacks, which stem from the system-wide adoption of Information and Communication Technology (ICT) and consequently endangers the digital and physical assets of organizations. Several solutions, however, were proposed to facilitate data exchange between different systems, but none were able to address the main challenges of threat sharing such as trust, privacy, interoperability, and automation in a single solution. To address these issues, this paper presents a secure and efficient threat information sharing system that leverages Trusted Automated Exchange of Intelligence Information (TAXII^TM) standard and private blockchain technology to automate the threat sharing procedure while offering privacy, data integrity, and interoperability. The extensive evaluation of the solution implementation indicates its capability to offer secure communication between participants without sacrificing data privacy and overall performance as opposed to existing solutions. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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19 pages, 328 KiB

Open AccessArticle

Strategic Assessment of Cyber Security Contenders to the Brazilian Agribusiness in the Beef Sector

by Virgínia de Melo Dantas Trinks, Robson de Oliveira Albuquerque, Rafael Rabelo Nunes and Gibran Ayupe Mota

Information 2022, 13(9), 431; https://doi.org/10.3390/info13090431 - 13 Sep 2022

Cited by 2 | Viewed by 3483

Abstract

The current international commercial structure places Brazilian Agribusiness in constant conflict to protect its interests before other nations in the global market. Technological innovations are used in all stages from the simplest production tasks, up to the design of negotiation tactics at high-level [...] Read more.

The current international commercial structure places Brazilian Agribusiness in constant conflict to protect its interests before other nations in the global market. Technological innovations are used in all stages from the simplest production tasks, up to the design of negotiation tactics at high-level affairs. This paper has the objective of finding Brazilian contenders in the beef market with cyber capabilities and commercial interest to act in favor of their interests. To construct such a list, a review of the literature on Threat and Cyber Threat Intelligence is presented, followed by a background presentation of how embedded technology is in nowadays agriculture and supply chains in general, and the real necessity for those sectors to be seen as critical infrastructure by governments in general. Also as background information recent cyber attack cases and attacker countries are shown. A Step-by-Step multidisciplinary method is presented that involves the extent of international trade, the interest on specific markets, and the intersection of country cyber capacity index. After applying the method and criteria generated a list of five contender countries. The method may be replicated and/or applied, considering adequate data source assessment and following specifics of each sector. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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14 pages, 708 KiB

Open AccessArticle

Introducing the Architecture of FASTER: A Digital Ecosystem for First Responder Teams

by Evangelos Katsadouros, Dimitrios G. Kogias, Charalampos Z. Patrikakis, Gabriele Giunta, Anastasios Dimou and Petros Daras

Information 2022, 13(3), 115; https://doi.org/10.3390/info13030115 - 26 Feb 2022

Cited by 3 | Viewed by 2546

Abstract

Emergency first responders play an important role during search and rescue missions, by helping people and saving lives. Thus, it is important to provide them with technology that will maximize their performance and their safety on the field of action. IFAFRI, the “International [...] Read more.

Emergency first responders play an important role during search and rescue missions, by helping people and saving lives. Thus, it is important to provide them with technology that will maximize their performance and their safety on the field of action. IFAFRI, the “International Forum to Advanced First Responder Innovation” has pointed out several capability gaps that are found in the existing solutions. Based on them, there is a need for the development of novel, modern digital solutions that will better assist responders by helping them on the field and, at the same time, better protect them. The work presented here introduces the logical architecture implemented in the Horizon 2020 project called FASTER (First responders Advanced technologies for Safe and efficienT Emergency Response), which is an innovating digital ecosystem for emergency first response teams. It is a system that meets the requirements of the consortium members but also fills all the gaps that IFARFI has pointed out and consists of mechanisms and tools for data communication, data analysis, monitoring, privacy protection and smart detection mechanisms. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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14 pages, 1906 KiB

Open AccessArticle

A Review of Tabular Data Synthesis Using GANs on an IDS Dataset

by Stavroula Bourou, Andreas El Saer, Terpsichori-Helen Velivassaki, Artemis Voulkidis and Theodore Zahariadis

Information 2021, 12(9), 375; https://doi.org/10.3390/info12090375 - 14 Sep 2021

Cited by 69 | Viewed by 11328

Abstract

Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide [...] Read more.

Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently, which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN), and it has great potential for tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN, and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN, and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on an NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods, we argue and evaluate the most prominent GANs for tabular network data synthesis. Full article

(This article belongs to the Special Issue Novel Approaches for Information Security in Complex Cyber-Physical Systems)

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