Cryptographic Attacks Research Articles

Hybrid Encryption using Advanced Encryption Standard and Arnold Scrambling for 3D Color Images

Digital security ensuring the confidentiality and integrity of visual data remains a paramount challenge. The escalating sophistication of cyber threats necessitates robust encryption methods to safeguard sensitive information from unauthorized access and manipulation. Despite the development of various encryption techniques, inherent vulnerabilities exist within conventional methods that can be exploited by attackers. Therefore, this research aims to investigate the effectiveness of the combined approach of Arnold Scrambling and Advanced Encryption Standard (AES) in mitigating these vulnerabilities and providing a more secure solution. The primary goal of this research is to enhance the security of digital images by mitigating vulnerabilities associated with conventional encryption methods. Arnold Scrambling introduces chaotic mapping to disperse pixel values, while Advanced Encryption Standard (AES) provides robust cryptographic strength through its substitution-permutation network. By combining these methods in an ensemble fashion, the encryption process achieves heightened resilience against various cryptographic attacks. The proposed methodology was evaluated by using standard metrics including Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and entropy analysis. Results indicate consistent performance across multiple test images, namely: Lena, Mandrill, Cameraman, and Plane with Unified Average Changing Intensity (UACI) averaging 33.6% and Number of Pixels Change Rate (NPCR) nearing 99.8%. Entropy values approached maximum, affirming the efficacy of the encryption in generating highly randomized outputs.

A Technique for Image Encryption Using the Modular Multiplicative Inverse Property of Mersenne Primes

Mersenne prime numbers, expressed in the form (2n − 1), have long captivated researchers due to their unique properties. The presented work aims to develop a symmetric cryptographic algorithm using a novel technique based on the logical properties of Mersenne primes. Existing encryption algorithms exhibit certain challenges, such as scalability and design complexity. The proposed novel modular multiplicative inverse property over Mersenne primes simplifies the encryption/decryption process. The simplification is achieved by computing the multiplicative inverse using cyclic bit shift operation. The proposed image encryption/decryption scheme involves a series of exor, complement, bit shift, and modular multiplicative inversion operations. The image is segmented into blocks of 521 bits. Each of these blocks is encrypted using a 521-bit key, ensuring high entropy and low predictability. The inclusion of cyclic bit shifting and XOR operations in the encryption/decryption process enhances the diffusion properties and resistance against attacks. This approach was experimentally proven to secure the image data while preserving the image structure. The experimental results demonstrate significant improvements in security metrics, including key sensitivity and correlation coefficients, confirming the technique’s effectiveness against cryptographic attacks. Overall, this method offers a scalable and secure solution for encrypting high-resolution digital images without compromising computational efficiency.

A New Image Encryption Method Using an Optimized Smart Codebook

Information attacks have increased worldwide as more information is available in digital form. Image encryption is essential to prevent attackers from unauthorized access to confidential images. In this paper, we introduce a novel image encryption method called the smart codebook, which combines an intelligent codebook technique with the RSA (Rivest–Shamir–Adleman) algorithm. The proposed method is designed to be dynamic as it selects the most effective codebooks in encrypting each image to increase its ambiguity. The smart codebook method divides an image into several segments based on image dimensions, and multiple random codebooks are generated for each segment. Moreover, images go through several processing stages before they are converted into cipher images, including noise, segmentation, and encryption. The best codebooks are selected to create the encrypted image in the next stage. Finally, the RSA algorithm sends the recipient the code books with the cipher image. The evaluation of the proposed smart codebook depends on several factors, including entropy, unified averaged changed intensity (UACI), number of changing pixel rate (NPCR), and correlation coefficient analysis. The assessment of the proposed encryption method demonstrates its resilience against cryptographic attacks, affirming its security and precision. The results show an impressive improvement in securing images compared to previous related efforts.

Machine Learning-Based Reconnaissance Bee Colony with Custom AES & AE for Robust IoT Data Security and Transmission

Objectives: To propose a robust method to enhance data security and transmission in IoT to address the challenges in real-time data transmission and create a comprehensive security solution. The new method takes credit for the machine learning-based bioinspired method to minimize data loss and maximize end-to-end data transmission rate. Methods: Custom-AES with Avalanche Effect (AE) and Machine Learning-based Reconnaissance Bee Colony are employed to secure the data with enhanced encryption by creating a multi-layered IoT security solution. Key stretching is used to provide high resistance to cryptographic attacks. In order to optimize threat detection, Pareto-based selection and dynamic parameter tuning with ML-RBC are used. To improve attack resilience, rank, and tournament selection methods are employed to target DDoS and MMIT attacks with a minimized encryption time. The NSL-KDD IoT network dataset is used to test the proposed CAES-AE with the ML-RBC model using a network simulator. To assess the performance of the proposed work, the results are compared with prevailing IoT data transmission techniques such as ERSA, QoS-BFT, AES, Custom AES, and RBC. Findings: The suggested CAES-AE with ML-RBC data security and transmission method attains the promising results of 97% authentication success rate, 6 seconds response time to security actions, 96% vulnerability detection speed, 97% data encryption strength, 5 seconds incident response time, and 97.8% network performance (energy efficiency & lifetime), which is higher than earlier methods. Novelty: This research provides a secured data transmission method in an IoT environment to address the real-time data transmission challenges faced in sensor-based networks. This will function as a multi-layered IoT security model that optimizes real-time attack detection and resilience. The performance metrics are evaluated with multiple packet transfer rates to boost the network performance. Keywords: Machine Learning, Advanced Networking, Data Security, Secured Data Transmission, Bio­Inspired Algorithm

A novel chirp-based 2D hyperchaotic map for enhanced image encryption

Abstract This paper presents a novel image encryption algorithm based on a newly proposed two-dimensional hyperchaotic map derived from the chirp signal. Performance evaluations of the proposed map include bifurcation analysis, phase portrait visualization, sensitivity to initial conditions, Lyapunov exponent calculations, entropy measurements, and NIST tests. These evaluations confirm the map’s strong randomness and broad chaotic behavior. The proposed encryption algorithm utilizes the high sensitivity to initial conditions and wide chaotic range of the hyperchaotic map to enhance security. The algorithm achieves a high degree of confusion and diffusion through bit-level manipulation, chaotic permutation, and randomized row-column diffusion processes. As a result, it can effectively encrypt images of any size, whether color or grayscale. Comprehensive security evaluations, such as key analysis, histogram analysis, Shannon entropy analysis, correlation analysis, differential analysis, and robustness analysis, confirm the algorithm’s resilience against a wide range of cryptographic attacks. Thus, the proposed algorithm offers a promising solution for secure image transmission.

Compressive sensing and DNA coding operation: Revolutionary approach to colour medical image compression‐encryption algorithm

AbstractWith advancements in medical imaging technology, colour medical images make lesion diagnosis more intuitive. However, when transmitting these high‐capacity images, doctors and researchers must not only address the challenges of storage and transmission efficiency but also guard against unauthorized access and data security risks. To address these issues, a revolutionary approach for colour medical image compression encryption based on compressive sensing and deoxyribonucleic acid (DNA) coding operation is introduced in this study. Randomness and sparse optimizations are performed on three floating‐point matrices obtained through discrete wavelet and sparse transforms of plain colour medical images by employing position scrambling and reduced‐stiffness operations. Subsequently, the floating‐point matrices are measured and quantized to generate three 8‐bit integer matrices. Further, pixel‐by‐pixel DNA encoding, DNA‐base scrambling, DNA XOR, and DNA decoding operations are performed to achieve DNA base‐position scrambling and value diffusion. Finally, the regrouped bit planes help yield the compressed encrypted images. A comprehensive analysis of the proposed algorithm's encryption and decryption effectiveness, compression performance, and security was conducted. The results show that, with a compression ratio of 0.5, average PSNR = 42.7153 dB and average MSSIM = 0.9779, key space is , average entropy = 7.9986 bits, average histogram variance = 509.53, and the correlation coefficients are close to 0. Moreover, the algorithm shows some immunity to common cryptographic attacks, such as differential, known‐plaintext, noise, and occlusion attacks. Thus, the proposed algorithm addresses the challenges posed by the sensitive nature of patient information and limited storage space.

ChessCrypt: enhancing wireless communication security in smart cities through dynamically generated S-Box with chess-based nonlinearity

In the contemporary landscape of smart cities, ensuring the security and confidentiality of transmitted data has become paramount. The proliferation of Internet of Things (IoT) devices, mobile communication platforms, and wireless sensor networks has magnified the need for robust cryptographic solutions to safeguard sensitive information from malicious adversaries. In response to these challenges, we introduce ChessCrypt—a novel approach to enhancing wireless communication security through the development of a new S-Box algorithm inspired by the nonlinear movement patterns of chess pieces. ChessCrypt leverages the dynamic and unpredictable nature of chess piece movements, namely knights, kings, and bishops, to introduce a high degree of nonlinearity and confusion into the encryption process. By incorporating elements of randomness and unpredictability, our proposed S-Box algorithm offers robust protection against a wide range of cyber threats, including eavesdropping, data interception, and cryptographic attacks. We demonstrate the effectiveness and resilience of ChessCrypt through rigorous testing and analysis, showcasing its superiority over traditional cryptographic methods in enhancing the security of wireless communication networks. Our results underscore the significance of ChessCrypt as a promising solution for fortifying wireless communication security in an increasingly interconnected world of ours.

A Hybrid Approach of Substitution and Permutation techniques for Modern Image-Cryptosystem

Abstract This paper proposes a new image encryption algorithm based on the introduction of cellular automata with Galois field-based methods to further encourage a higher level of security. This begins the encryption process by creating an initial substitution box from the irreducible polynomials in the Galois field, followed by the final substitution box obtained through permutation operations. This lays
the foundation for a sound cryptographic process. In order to handle different cryptographic attack types, balance in the substitution box has been closely analyzed. Additionally, we leverage the chaotic nature of the logistic map to incorporate a random element, significantly strengthening the encryption scheme against diverse cryptanalytic attacks. Nonlinear dynamics, as introduced by cellular automata, further develop the encryption process and disperse the image’s data, making it more intricate for any potential attacker. The differential analysis validates the encryption scheme’s resistance to differential attacks, thereby confirming its potential for secure image transmission and storage. The proposed hybrid approach presents a comprehensive solution for image encryption, ensuring high security and computational efficiency. This contribution is an added advantage in the development of image cryptosystems,
addressing the modern challenges facing secure data within the digital landscape.

Enhanced Data Security Using 5x5 Hill Cipher with Modular 53

This research presents an optimized approach to the Hill Cipher encryption and decryption algorithm using a 5x5 matrix and modular 53 arithmetic. The traditional Hill Cipher, a well-known symmetric key algorithm, typically utilizes smaller matrices and modular arithmetic, which may not provide sufficient security for contemporary applications. By expanding the key matrix to a 5x5 structure and adopting a larger modulus of 53, the complexity and security of the cipher are significantly enhanced. The study details the methodology for constructing and implementing the 5x5 key matrix, as well as the processes for encryption and decryption under the modular 53 system. The computational efficiency and security improvements achieved through this optimization are analyzed. Comparative assessments with the conventional Hill Cipher demonstrate that the enhanced approach offers superior resistance against cryptographic attacks while maintaining manageable computational requirements. The results of this research indicate that the proposed optimized Hill Cipher can serve as a robust encryption method suitable for securing sensitive data in various modern applications. This study contributes to the field of cryptography by providing a more secure and efficient variant of the classical Hill Cipher algorithm

Cyber attacks on radiological systems

The base of today’s radiological devices are computers and networks. The radiology department has a specific way of working and there are standards such as DICOM for medical image records, PACS for archiving and communication, and HL7 for information exchange in the medical system. As radiology becomes an economically interesting branch, it becomes a target for cyber-attacks. At the same time, radiological systems contain a lot of personal data that are valuable. The reasons for the attacks are often financial gain, political, ideological or personal. The start of an attack can be physical access to radiological devices or network access. DICOM files can be the trigger of an attack. We divide attacks into those that directly affect patients, those that have an indirect impact, and those that affect the infrastructure. Well known types are Denial-Of-Service, malware, cryptographic attacks and making changes of device settings. When defending against cyber-attacks, it is important to secure communication by e-mail and to keep software updated. The IT department of the radiology department should observe accounts of all users and check the authorizations. Networks must have access restrictions according to workplaces and purposes to prevent unwanted access. Web proxy protection restricts access to Internet sites that are potentially dangerous. The basics of the department’s network, such as servers, must be physically secured from access. DICOM files should be encrypted with the most secure algorithms available. In response to cyber-attacks, it is necessary to have standard procedures and such a system must always be on standby. Known attacks on radiological systems are Kwampiris, Petja/NotPetya, Ryuk, Wannacry, Conti group and BianLian.

On the Security of Selectively Encrypted HEVC Video Bitstreams

With the growing applications of video, ensuring its security has become of utmost importance. Selective encryption (SE) has gained significant attention in the field of video content protection due to its compatibility with video codecs, favorable visual distortion, and low time complexity. However, few studies consider SE security under cryptographic attacks. To fill this gap, we analyze the security concerns of encrypted bitstreams by SE schemes and propose two known plaintext attacks (KPAs). Then the corresponding defense is presented against the KPAs. To validate the effectiveness of the KPA, it is applied to attack two existing SE schemes with superior visual degradation in HEVC videos. Firstly, the video sequences are encoded using H.265/HEVC. During encoding, the selected syntax elements are recorded. Secondly, the recorded syntax elements are imported into the HEVC decoder. By utilizing the encryption parameters and the imported data in the decoder, it becomes possible to reconstruct a significant portion of the original syntax elements before encryption. Finally, the reconstructed syntax elements are compared with the encrypted syntax elements in the decoder, allowing the design of a pseudo-key stream (PKS) through the inverse of the encryption operations. The PKS is used to decrypt the existing SE scheme, and the experimental results provide evidence that the two existing SE schemes are vulnerable to the proposed KPAs. In the case of single bitstream estimation (SBE), the average correct rate of key stream estimation exceeds 93%. Moreover, with multi-bitstream complementation (MBC), the average estimation accuracy can be further improved to 99%.

Private pathological assessment via machine learning and homomorphic encryption

PurposeThe objective of this research is to explore the applicability of machine learning and fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on the inference phase of support vector machines (SVM) for the classification of confidential medical data.MethodsA framework is introduced that utilizes the Cheon-Kim-Kim-Song (CKKS) FHE scheme, facilitating the execution of SVM inference on encrypted datasets. This framework ensures the privacy of patient data and negates the necessity of decryption during the analytical process. Additionally, an efficient feature extraction technique is presented for the transformation of medical imagery into vectorial representations.ResultsThe system’s evaluation across various datasets substantiates its practicality and efficacy. The proposed method delivers classification accuracy and performance on par with traditional, non-encrypted SVM inference, while upholding a 128-bit security level against established cryptographic attacks targeting the CKKS scheme. The secure inference process is executed within a temporal span of mere seconds.ConclusionThe findings of this study underscore the viability of FHE in enhancing the security and efficiency of bioinformatics analyses, potentially benefiting fields such as cardiology, oncology, and medical imagery. The implications of this research are significant for the future of privacy-preserving machine learning, promoting progress in diagnostic procedures, tailored medical treatments, and clinical investigations.

A New Framework for Enhancing VANETs through Layer 2 DLT Architectures with Multiparty Threshold Key Management and PETs

This work proposes a new architectural approach to enhance the security, privacy, and scalability of VANETs through threshold key management and Privacy Enhancing Technologies (PETs), such as homomorphic encryption and secure multiparty computation, integrated with Decentralized Ledger Technologies (DLTs). These advanced mechanisms are employed to eliminate centralization and protect the privacy of transferred and processed information in VANETs, thereby addressing privacy concerns. We begin by discussing the weaknesses of existing VANET architectures concerning trust, privacy, and scalability and then introduce a new architectural framework that shifts from centralized to decentralized approaches. This transition applies a decentralized ledger mechanism to ensure correctness, reliability, accuracy, and security against various known attacks. The use of Layer 2 DLTs in our framework enhances key management, trust distribution, and data privacy, offering cost and speed advantages over Layer 1 DLTs, thereby enabling secure vehicle-to-everything (V2X) communication. The proposed framework is superior to other frameworks as it improves decentralized trust management, adopts more efficient PETs, and leverages Layer 2 DLT for scalability. The integration of multiparty threshold key management and homomorphic encryption also enhances data confidentiality and integrity, thus securing against various existing cryptographic attacks. Finally, we discuss potential future developments to improve the security and reliability of VANETs in the next generation of networks, including 5G networks.

A novel lightweight multi-factor authentication scheme for MQTT-based IoT applications

The present authentication solutions employed in the Internet of Things (IoT) are either inadequate or computationally intensive, given the resource-constrained nature of IoT devices. This challenges the researchers to devise efficient solutions to embed an important security tenet like authentication. In IoT, the most popular machine-to-machine communication protocol used at the application layer is Message Queuing Telemetry Transport (MQTT). However, the MQTT protocol inherently lacks security-related functions, like authentication, authorization, confidentiality, access control, and data integrity, which is unacceptable for IoT-driven mission-critical applications when connected over public networks. In such a situation, the security is hardened by employing a transport layer security protocol like TLS, which entails significant computational overheads. This paper presents a novel scheme to enhance MQTT security by providing a lightweight multi-factor authentication scheme based on Elliptical curve cryptography. The proposed scheme uses a low-cost signature and a fuzzy extractor to correct errors in imprinted biometrics in noisy environments. This scheme attains mutual authentication, generates a securely agreed-upon session key for secret communication, and guarantees perfect forward secrecy. Furthermore, the rigorous informal security analysis shows the proposed scheme resists cryptographic attacks, including known session critical attacks. Furthermore, an empirical study has been carried out to assess the effectiveness of the proposed scheme in the Cooja simulated environment.

ACM and rectangular images: Overlapping partitions, implementation, and periodicity analysis.

The Arnold Cat Map (ACM) is a popular chaotic map used in image encryption. Chaotic maps are known for their sensitivity to initial conditions and their ability to permute, or rearrange, pixels. However, ACM is periodic, and its period is relatively short. This periodicity decreases the effective key-space and security of a cryptosystem using ACM. Further, ACM is typically only able to be performed on square images. To solve the low periodicity and typical limitation to square images, this paper proposes performing ACM on overlapping square partitions which cover the entirety of an image. The presence of overlap results in a greatly increased image period. The resulting system will be referred to as overlapping ACM or OACM. Several papers have already discussed systems involving overlapping ACM. However, they did not discuss the implementation or periodicity of such a system in detail. This paper does cover the implementation and periodicity analysis of OACM and proposes a simple symmetric encryption system which uses OACM. The proposed encryption system is not as sophisticated or secure as other modern encryption schemes, since it is mainly intended as an initial test of OACM's utility. Histogram and sensitivity analyses did however indicate a level of security against various cryptographic attacks, and OACM performed reasonably in both the permutation and diffusion stages of the cryptosystem.

Advancing cryptography: a novel hybrid cipher design merging Feistel and SPN structures

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments.<p> </p>

Image privacy protection scheme based on high-quality reconstruction DCT compression and nonlinear dynamics

The protection of digital image privacy in Big Data environment is a hot issue of increasing concern. To address the contradiction between efficiency and security for privacy protection, this paper proposes a high-quality restored image privacy protection scheme based on Discrete Cosine Transform (DCT) frequency domain compression and nonlinear dynamics. First, the RGB space of the color image is converted to YCbCr domain with 4:2:0 sampling. Then, the image is divided into sub-blocks in the spatial domain, followed by block-wise DCT and quantization into frequency domain information. Next, both Alternating Current (AC) and Direct Current (DC) coefficients of all sub-blocks are individually extracted and subjected to compression encoding. Finally, a Rubik’s Cube permutation and filtering diffusion encryption algorithm based on a nonlinear dynamical chaotic system is designed. Moreover, a mechanism for generating dynamic chaotic sequences associated with plaintext is introduced to effectively counter cryptographic attacks. The combined approach of compression and encryption significantly reduces computational complexity while improving encryption efficiency. Through experimental simulation results, the compression encryption scheme demonstrates high compression ratios as well as excellent image recovery quality while providing enhanced security against common cryptographic attacks. The work in this paper provides a preferred joint compression and encryption technical solution for digital image privacy protection in Big Data network.

A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks

Recently, Sahoo et al. introduced a three-factor authentication scheme for Wireless Sensor Networks (WSNs) based on an elliptic curve cryptosystem. Nonetheless, upon closer examination, we have identified critical vulnerabilities in their scheme, including susceptibility to user impersonation, gateway impersonation, sensor node impersonation attacks, and a breach in the three-factor security aspect. Further, the scheme fails to withstand offline sensor node identity guessing attacks, man-in-the-middle attacks, and known session-specific temporary information attacks. Intending to elevate both security and efficiency, we propose a novel three-factor authentication scheme that capitalizes on the strengths of a fuzzy extractor and a cryptographic one-way hash function. The proposed scheme’s security has been rigorously assessed using the SCYTHER tool, confirming its validity under the real-or-random (ROR) model. Moreover, a heuristic analysis exemplifies that the scheme effectively withstands various known cryptographic attacks. Consequently, the performance comparisons establish the superiority of our scheme over related approaches in terms of security and efficiency. Additionally, its suitability for WSNs is evident due to the minimal overhead on the sensor nodes, making it a highly promising solution for real-world implementation.

Audio encryption framework based on chaotic map and DNA encoding

In this work, an audio encryption framework is proposed based on the chaos theory and DNA encoding. Traditional encryption algorithms designed for text data might not be efficient for handling the bulk data of audio files, leading to slow processing times and increased storage requirements. Striking the right balance between robust encryption and efficient processing is crucial. Additionally, some encryption methods can introduce noise or artifacts into the audio signal, impacting its clarity. Maintaining high-fidelity audio quality while ensuring strong encryption remains an ongoing area of research. Despite these challenges, the need for secure audio communication is undeniable. The RCM chaotic map is used to introduce chaotic properties in the encryption framework. A novel pseudorandom bit generator approach is proposed and used for encryption purposes. The proposed work is a symmetric encryption approach and is completely lossless. The proposed approach uses some lightweight computational procedures that make the algorithm simple and computationally less expensive which makes it suitable for real-time applications. Furthermore, the proposed approach shows significant performance and resilience against various cryptographic attacks. A new scrambling algorithm is proposed to add a layer of security. The proposed scramble algorithm uses logistic map beside the RCM map. The proposed approach achieved 98.28 % NSCR value and 33.63 % UACI values on average. The experimental outcomes are encouraging for practical applications of the proposed approach.

Applications of Post-Quantum Cryptography

With the constantly advancing capabilities of quantum computers, conventional cryptographic systems relying on complex math problems may encounter unforeseen vulnerabilities. Unlike regular computers, which are often deemed cost-ineffective in cryptographic attacks, quantum computers have a significant advantage in calculation speed. This distinction potentially makes currently used algorithms less secure or even completely vulnerable, compelling the exploration of post-quantum cryptography (PQC) as the most reasonable solution to quantum threats. This review aims to provide current information on applications, benefits, and challenges associated with the PQC. The review employs a systematic scoping review with the scope restricted to the years 2022 and 2023; only articles that were published in scientific journals were used in this paper. The review examined the articles on the applications of quantum computing in various spheres. However, the scope of this paper was restricted to the domain of the PQC because most of the analyzed articles featured this field. Subsequently, the paper is analyzing various PQC algorithms, including lattice-based, hash-based, code-based, multivariate polynomial, and isogeny-based cryptography. Each algorithm is being judged based on its potential applications, robustness, and challenges. All the analyzed algorithms are promising for the post-quantum era in such applications as digital signatures, communication channels, and IoT. Moreover, some of the algorithms are already implemented in the spheres of banking transactions, communication, and intellectual property. Meanwhile, despite their potential, these algorithms face serious challenges since they lack standardization, require vast amounts of storage and computation power, and might have unknown vulnerabilities that can be discovered only with years of cryptanalysis. This overview aims to give a basic understanding of the current state of post-quantum cryptography with its applications and challenges. As the world enters the quantum era, this review not only shows the need for strong security methods that can resist quantum attacks but also presents an optimistic outlook on the future of secure communications, guided by advancements in quantum technology. By bridging the gap between theoretical research and practical implementation, this paper aims to inspire further innovation and collaboration in the field.