Algorithms for Cyber Defense: From Cryptography to Behavioral Analysis

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2245

Special Issue Editor


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Guest Editor
1. Faculty of Engineering in Foreign Languages, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania
2. Military Technical Academy "Ferdinand I", 050141 Bucharest, Romania
Interests: algorithms; cybersecurity; software; electronics; bioinformatics

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to explore the full spectrum of algorithms used in modern cyber defense. We welcome both theoretical and applied contributions in areas such as encryption and decryption methods, authentication schemes, secure key exchange, anomaly detection, static analysis, intrusion detection, reverse engineering of malicious code, adversarial learning, and security-aware AI models. We encourage submissions that bridge formal algorithm design with practical implementations in security-critical systems, ranging from embedded devices to large-scale infrastructure. This Issue serves as a platform for engineers, data scientists, and security researchers to share breakthroughs that redefine digital defense.

In addition to original research articles, review papers are also welcome in this Special Issue, and in fact, they are highly encouraged. The academic field of cyber security is still in its infancy, only just beginning to open its eyes. The field is so young that authors often find themselves compelled to step outside traditional academic norms, citing online resources and web-links instead of well-established works in the literature (which is almost blasphemy from a classical perspective). This situation highlights the crucial importance of review articles: they not only provide structure and clarity in a rapidly evolving academic domain, but are also bound to become highly cited references in the future.

Dr. Paul A. Gagniuc
Guest Editor

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Keywords

  • algorithms
  • cyber defense
  • encryption and decryption methods
  • authentication schemes
  • secure key exchange
  • anomaly detection

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Published Papers (3 papers)

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Research

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25 pages, 1667 KB  
Article
A Bidirectional Bridge for Cross-Chain Revocation of Verifiable Credentials in Segregated Blockchains
by Matei Sofronie, Andrei Brînzea, Alexandru Bratu, Iulian Aciobăniței and Florin Pop
Algorithms 2025, 18(12), 734; https://doi.org/10.3390/a18120734 - 21 Nov 2025
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Abstract
Verifiable Credentials (VCs) are a core component of decentralized identity systems, enabling individuals to prove claims without centralized intermediaries. However, managing VC revocation across segregated blockchain networks remains a key interoperability challenge. In this paper, we present a bidirectional blockchain bridge that enables [...] Read more.
Verifiable Credentials (VCs) are a core component of decentralized identity systems, enabling individuals to prove claims without centralized intermediaries. However, managing VC revocation across segregated blockchain networks remains a key interoperability challenge. In this paper, we present a bidirectional blockchain bridge that enables the cross-chain verification of VCs between two Ethereum-compatible private blockchain networks: Geth and Besu. The system allows credentials issued and revoked on one chain to be validated from another without duplicating infrastructure or compromising security. Our architecture combines on-chain smart contracts with an off-chain relay, ensuring auditable, low-latency credential checks across chains. Our proposal is validated through an open-source working prototype. It is particularly relevant for domains where independent organizations must validate shared credentials across segregated blockchain infrastructures, including education, healthcare, and governmental identity services. Full article
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15 pages, 1428 KB  
Article
A Decision Tree Regression Algorithm for Real-Time Trust Evaluation of Battlefield IoT Devices
by Ioana Matei and Victor-Valeriu Patriciu
Algorithms 2025, 18(10), 641; https://doi.org/10.3390/a18100641 - 10 Oct 2025
Viewed by 481
Abstract
This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data [...] Read more.
This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article
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Review

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16 pages, 1871 KB  
Review
Foundational Algorithms for Modern Cybersecurity: A Unified Review on Defensive Computation in Adversarial Environments
by Paul A. Gagniuc
Algorithms 2025, 18(11), 709; https://doi.org/10.3390/a18110709 - 7 Nov 2025
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Abstract
Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that supports modern cyber defense, from cryptographic primitives that ensure confidentiality and integrity to [...] Read more.
Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that supports modern cyber defense, from cryptographic primitives that ensure confidentiality and integrity to behavioral intelligence algorithms that provide predictive security. Classical symmetric and asymmetric schemes such as AES, ChaCha20, RSA, and ECC define the computational backbone of confidentiality and authentication in current systems. Intrusion and anomaly detection mechanisms range from deterministic pattern matchers exemplified by Aho-Corasick and Boyer-Moore to probabilistic inference models such as Markov Chains and HMMs, as well as deep architectures such as CNNs, RNNs, and Autoencoders. Malware forensics combines graph theory, entropy metrics, and symbolic reasoning into a unified diagnostic framework, while network defense employs graph-theoretic algorithms for routing, flow control, and intrusion propagation. Behavioral paradigms such as reinforcement learning, evolutionary computation, and swarm intelligence transform cyber defense from reactive automation to adaptive cognition. Hybrid architectures now merge deterministic computation with distributed learning and explainable inference to create systems that act, reason, and adapt. This review identifies and contextualizes over 50 foundational algorithms, ranging from AES and RSA to LSTMs, graph-based models, and post-quantum cryptography, and redefines them not as passive utilities, but as the cognitive genome of cyber defense: entities that shape, sustain, and evolve resilience within adversarial environments. Full article
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