Security Strength Research Articles

Security analysis and evaluation of mobile banking applications in Nigeria

Rapid fintech adoption across the world is so ubiquitous. To facilitate more adoption in Nigeria, recently the Central Bank of Nigeria (CBN) introduced several policies that support cashless banking. Nowadays, Nigerian banks users could perform most of their daily transactions from any desired location using mobile banking applications. In the literature, there are insufficient studies that comprehensively evaluate the security strength or risks of these applications. Generally, insecure mobile banking applications could lead to financial fraud, violations of privacy, identity theft and eroded user confidence. Considering the situation, there is need to conduct research which comprehensively assess security of the applications. Consequently, in this paper we analyzed and evaluated the security of identified popular mobile banking applications in Nigeria. We conducted the analysis work using automated and manual static analysis methods. Then, we evaluated the security of the applications using multi-criteria decision-making technique. Our results revealed that most of the applications have several security challenges in form of vulnerabilities and insecure coding practices. Hence, our findings have shown the applications need further improvements for better security and safety.

An efficient conference key agreement protocol suited for resource constrained devices

Conference key agreement (CKA) is essential for securing communication in group-oriented scenarios like multi-party messaging and collaborative environments. While elliptic curve cryptography (ECC) offers efficiency and strong security, ECC-based CKA protocols often rely on expensive pairings, making them computationally impractical for deployment over the resource limited devices. This paper introduces a novel CKA approach using ECC without requiring pairing computations, thus addressing scalability and efficiency challenges. The proposed protocol employs scalar point multiplications over a prime field elliptic curve group, enabling secure and efficient CKA operations with reduced computational overhead. Compared to existing ECC-based key agreement protocols, it minimizes user-level computation and enhances performance in computational efficiency, communication overhead, and security strength. Particularly suitable for resource-constrained environments like IoT and edge computing, where computational resources are limited yet secure group communication is crucial.

A new S-box pattern generation based on chaotic enhanced logistic map: case of 5-bit S-box

Cryptography plays consistently an essential role in securing any sort of communications or data broadcast over the network. Since the security of any designed block cipher algorithm is related to its Substitution box (S-box), several efforts have been made by researchers to design a vigorous S-box that maintains flawlessly the cost, performance, and security trade-off. From the literature, we can find a variety of input–output sizes of S-boxes, each of which has its benefits and drawbacks. Therefore, this work introduces a new S-box pattern generation based on the chaotic enhanced logistic map where its chaotic behavior offers good randomness ability, a fact that enhances its unpredictability. In our realization, the intention was the generation of a 5-bit Sbox due to its suitability and cost-effectiveness to be integrated into lightweight cryptosystems. Moreover, the S-box security strength is proved by testing it through numerous cryptanalysis measurements. We can mention non-linearity, bijectivity, linearity, differential cryptanalysis, boomerang attacks resilience, Avalanche effect, and algebraic attacks resilience. The results show that our proposition provides good resistance to the aforementioned attacks and even shows superiority over its 5-bit competitors in terms of non-linearity, differential, Boomerang, and algebraic attack resistance.

A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

Flexible hybrid post-quantum bidirectional multi-factor authentication and key agreement framework using ECC and KEM

Post-quantum computing becomes a real threat in the coming years, resulting in vulnerable security protocols that rely on traditional public key algorithms. It is not evident to provide protection against it in a cost-efficient manner, especially for Internet of Things (IoT) devices with limited capabilities. There is a high variety of IoT applications, some require only short-term security (e.g. agriculture) and others long-term security (e.g. healthcare). In order to provide a unified security approach for such heterogenity in IoT, we propose a flexible hybrid authentication and key agreement framework for a client–server architecture, which relies both on the classical elliptic curve cryptography (ECC) and on a quantum secure key encapsulation mechanism (KEM). There are five versions that can be derived from the framework, going from a fully hybrid, and partial hybrid to classical construction. The trade-off between performance and security strength is demonstrated for each of these versions. The overall cost of the protocols is highly reduced thanks to the usage of multifactors in the authentication process, both on the user side by means of biometrics and the device side by means of physically unclonable functions (PUFs). We show that both Kyber and Mc Elience as KEM can offer reasonable performance, depending on the situation. The unified framework offers optimal security protection against the most well-known attacks.

Enhancing IOT Security: Investigating Lightweight Encryption Algorithms for Resource-Constrained Devices

Abstract: In this work, we examine three LWCs: namely AES-128, SPECK, and ASCON, considering their presentation on resource-constrained IoT devices. With the large-scale deployment of IoT in all areas of industry, ensuring the security and efficiency of cryptographic implementations has become highly critical because typical IoT devices are usually characterized by limited memory, processing power, and energy resources. The study ascertains the memory usage, throughput, energy efficiency, and strength of security in the chosen algorithms by highly popular resource-constrained boards such as Arduino Nano and Arduino Micro. The benchmarks have shown that SPECK has the highest throughput, which is much efficient for constrained environments. One of the significant features of ASCON is that it has minimized memory usage and high energy efficiency. Therefore, it suits for long-term usage in IoT. AES-128 is secure but resource-intensive, so its usage may be irrelevant in very constrained environments. The results are of great value for IoT developers as a contribution to the selection of cryptographic algorithms based on the conditions and resource restrictions of the IoT devices involved so that optimized security solutions are achieved.

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs ( DAGs ) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping , alteration and spoofing . Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality , integrity and authentication on the transmitted messages. While message size and conceptually its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application's messages, selecting the appropriate protocols for each message such that a system's overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this paper, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., run-times) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

A two-tier database architecture framework for combating cyber threats

The 21st century has witnessed a lot of advancements in science and technology. One may not be wrong to assert that the field of Information and Communications Technology (ICT) and its allied disciplines like Artificial Intelligence (AI) and Nanotechnology have taken the lead as we witness new developments in these areas year by year. The products and services we utilize and enjoy are evidence of the usefulness of this advancement in science and technology. It is worthy of note that the interest of many (both good and bad in the discipline of ICT), is being ignited by the fact that ICT finds its applicability virtually in every area of human endeavor. In this study, we propose a two tier database architecture that employs an Object-Oriented Analysis Design Methodology (OOADM) using the Unified Modelling Language (UML). The choice of this methodology is because we need to present every stage of the study from analysis to the design phases. With the help of the chosen methodology, we analyzed the existing frameworks, identified the weaknesses, and as a result, we further surveyed algorithms used for reliable data security. From the results obtained from the survey, we employed the SHA-256 algorithm because of its strength in data security, and the Argon2 hashing algorithm which is one of the best algorithms for securing database credentials. In the end, we provide an enhanced framework made up of the chosen algorithms and a two-dimensional database (2-DDb), which could be implemented to effectively combat attacks on important databases.

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G

The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

Bio-mimicking DNA fingerprint profiling for HLS watermarking to counter hardware IP piracy

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches.

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.

PRIVACY-PRESERVING TECHNIQUES IN BIG DATA AND INFORMATION SECURITY

In the era of big data, ensuring privacy while processing vast amounts of sensitive information poses a significant challenge. Traditional encryption methods often fall short in maintaining both privacy and data utility during computation. This paper introduces Two-Trapdoor Homomorphic Encryption (TTHE), a novel approach designed to enhance privacy-preserving capabilities in big data and information security. TTHE combines the strengths of trapdoor functions with homomorphic encryption to enable secure data processing without compromising privacy. With the exponential growth of data, safeguarding sensitive information has become a critical concern. Existing encryption schemes often struggle to balance between privacy preservation and computational efficiency. Homomorphic encryption offers a potential solution by allowing computations on encrypted data, but current methods are limited by performance and scalability issues. The key challenge addressed is the inefficiency and performance bottlenecks in current homomorphic encryption schemes, which hinder their practical application in big data environments. Traditional methods often face limitations in processing large datasets efficiently while maintaining robust security. TTHE is proposed as an enhancement over traditional homomorphic encryption. It integrates two distinct trapdoor functions to provide a dual-layer security approach, enabling efficient and scalable computation on encrypted data. The method involves a novel encryption scheme where operations on ciphertexts are performed without decryption, preserving data privacy throughout the process. Extensive experiments demonstrate that TTHE significantly improves both computational efficiency and security. The proposed method achieved a processing speed increase of 45% compared to conventional homomorphic encryption schemes. Additionally, TTHE maintained a privacy level with a security strength of 128-bit encryption, providing robust protection against potential attacks.

The Use of Fuzzy Vault Technology to Find Out Fingerprint Differences

Fuzzy vault technology is different from biometric security and information recovery. In this article, we explore the utilization of fuzzy vaults in healthcare, because we can acquire crucial information like the vital signs of a person and the specific details of their fingerprint, even when there is limited information about the individual. We discuss both the conceptualized ideas that serve as the foundation for fuzzy vaults, as well as the practical application of these ideas to secure biological templates via biometric means. Our research involves both computer-simulated and actual case history data. Through this, we attempt to balance between preserving biometric privacy while still achieving a degree of identificable physiological variation. In our presentation, we demonstrate a hybrid of tabular data alongside visual illustrations that illustrate the association between different parameters regarding accuracy and the degree of difficulty associated with information recovery via fuzzy vaults. Let's consider the benefits and drawbacks of our options- elements like strength in security, precision, or the potential for application in limited spaces. To conclude this discussion without a definitive conclusion, but by pointing out what additional investigations should be conducted regarding the subject; likely medical advances caused by the adoption of new methods in medical settings should be noted.

Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations

With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs.

Insights of machine learning-based threat identification schemes in advanced network system

An advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.

Towards zero knowledge argument for double discrete logarithm with constant cost

Given that the Schnorr's protocol for Discrete Logarithm (DLOG) exchanges three messages, it is an interesting problem whether a constant round zero-knowledge protocol exists for the Double Discrete Logarithm problem (DDLOG), i.e., to demonstrate the knowledge of a secret witness x in ghx. In this paper, we show that it exists for a fragment of DDLOG with two restrictions: (1) The outer group of DDLOG supports bilinear pairing, and it needs a trusted set-up for common reference string (CRS). (2) x<t where t is the size of KZG commitment key in CRS. The protocol is zero knowledge and constant round, with O(1) complexity for prover and verifier, regardless of the desired security strength. The contributions of the work are mainly theoretical due to its restrictions and concrete performance.

Breaking On-Chip Communication Anonymity using Flow Correlation Attacks

Network-on-Chip (NoC) is widely used to facilitate communication between components in sophisticated System-on-Chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker that puts the entire computing infrastructure at risk. We investigate the security strength of existing anonymous routing protocols in NoC architectures, making two pivotal contributions. Firstly, we develop and perform a machine learning (ML)-based flow correlation attack on existing anonymous routing techniques in Network-on-Chip (NoC) systems, revealing that they provide only packet-level anonymity. Secondly, we propose a novel, lightweight anonymous routing protocol featuring outbound traffic tunneling and traffic obfuscation. This protocol is designed to provide robust defense against ML-based flow correlation attacks, ensuring both packet-level and flow-level anonymity. Experimental evaluation using both real and synthetic traffic demonstrates that our proposed attack successfully deanonymizes state-of-the-art anonymous routing in NoC architectures with high accuracy (up to 99%) for diverse traffic patterns. It also reveals that our lightweight anonymous routing protocol can defend against ML-based attacks with minor hardware and performance overhead.

Performance comparison of quantum-safe multivariate polynomial public key encapsulation algorithm

A novel quantum-safe key encapsulation algorithm, called Multivariate Polynomial Public Key (MPPK), was recently proposed by Kuang, Perepechaenko, and Barbeau. Security of the MPPK key encapsulation mechanism does not rely on the prime factorization or discrete logarithm problems. It builds upon the NP-completeness of the modular Diophantine equation problem, for which there are no known efficient classical or quantum algorithms. Hence, it is resistant to known quantum computing attacks. The private key of MPPK comprises a pair of multivariate polynomials. In a companion paper, we analyzed the performance of MPPK when these polynomials are quadratic. The analysis highlighted the MPPK high decapsulation time. We found that, while maintaining the security strength, the polynomials can be linear. Considerable performance gains are obtained for the decapsulation process. In this article, we benchmark the linear case and compare the results with the previous quadratic case.