Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. However, local model or weight updates can still leak sensitive information. Secure aggregation protocols mitigate this risk by ensuring that only the aggregated updates are revealed. Among these, single-setup secure aggregation protocols, where key generation and exchange occur only once, are the most efficient due to reduced communication and computation overhead. However, existing single-setup secure aggregation protocols often lack support for dynamic user participation and do not provide strong privacy guarantees such as forward and backward secrecy.

This paper presents a novel methodology for near-real-time intrusion detection tailored to micro-enterprise environments, where resources are limited but cyber risks are escalating. The experimental design uses the lightweight machine learning techniques in Weka with robust cross-validation to ensure reliable detection while minimising overfitting and underfitting. To complement this, we introduce the Agile Cybersecurity Maturity Model (ACMM), which supports adaptive and proactive security practices. The key contributions of this paper are: (i) a practical intrusion detection framework optimised for micro-enterprise constraints, (ii) the integration of machine learning and validation techniques for near-real-time monitoring, and (iii) the proposal of ACMM as a maturity model to strengthen long-term resilience. Together, these advances provide micro-enterprises with an affordable, scalable, and accessible defence against increasingly sophisticated threats, while supporting the protection of broader community and critical infrastructure.

Abstract Ransomware threats are growing in frequency and severity, posing significant challenges to cybersecurity defences. Machine learning (ML) has gained attention as a promising tool for detecting ransomware, but the lack of realistic ransomware datasets for training and evaluating ML models has limited progress. This paper introduces RADAR, a comprehensive dataset designed to address this challenge and advance ransomware detection. With over 400,000 system events from seven prominent ransomware families and benign activities, RADAR overcomes the limitations of existing datasets that rely on outdated samples and fail to capture the evolving nature of ransomware. RADAR is structured as a continuous stream of system events and incorporates realistic scenarios, including data drift and class imbalance. The dataset features 48 attributes extracted from Sysmon logs and 19 additional engineered features to improve the analysis of behavioural patterns. By simulating data drift and reflecting the minority-class nature of ransomware, RADAR provides a realistic environment for evaluating ML models in conditions that replicate real-world operations. The utility of RADAR is demonstrated through an experimental framework using an adaptive random forest algorithm in an online incremental learning setting. The results underscore the importance of continually adapting detection methods to effectively address evolving ransomware threats. This research lays a solid foundation for improving ML algorithms and fostering innovative methods for real-time ransomware detection.

Network Intrusion Detection Systems face increasing challenges from sophisticated evasion techniques that manipulate traffic timing patterns. This paper presents a Temporal Evasion Generation Algorithm (TEGA) for creating adversarial examples by exploiting temporal vulnerabilities, and an Adaptive Temporal Defense System (ATDS) to counter these attacks. We formalize temporal evasion mathematically and evaluate both systems using the CIC-IDS2018 dataset. TEGA achieves evasion success rates of 72.4%, significantly outperforming conventional techniques such as standard delay injection (45.6%) and burst pattern manipulation (53.2%). Conversely, ATDS demonstrates robust defense capabilities, with detection accuracy reaching 95% after adaptation and false positive rates reduced to 1.5%. Our comparative analysis reveals that sequence-based feature extraction combined with SVM classification provides optimal resilience against temporal evasion. The adaptive framework rapidly responds to new attack patterns, typically requiring only 2-3 update cycles to achieve over 90% detection accuracy. This research contributes to network security by addressing an emerging attack vector while offering promising directions for developing next-generation intrusion detection systems.

This paper proposes a novel approach to adversarial attacks against machine learning-based network intrusion detection systems (NIDS). Unlike conventional methods that apply feature-space perturbations, we implement realistic problem-space attacks by manipulating network traffic through advanced packet modification techniques using the Scapy framework. Our methodology targets multiple attack vectors, including recon-naissance, data exfiltration, and command and control communications. Experiments evaluate four popular machine learning models (GBDT, DNN, Random Forest, and SVM) against both legitimate and adversarially manipulated traffic. Results demonstrate significant detection performance degradation across all models, with evasion rates reaching 72% for reconnaissance traffic. We observe strong transferability of adversarial examples between different model architectures and analyze the underlying mechanisms through feature space visualization. Our findings highlight fundamental vulnerabilities in ML-based intrusion detection and emphasize the need for defensive techniques specifically addressing realistic traffic manipulations rather than abstract feature perturbations.

This research paper presents an attack framework between NIDS and CAN-based intrusion detection that secures vehicle-to-everything (V2X) environments. The connection of vehicles to external networks exposes critical security concerns because public network attacks can penetrate into vehicle systems. Our methodology performs three steps to enable crossdomain attacks through network security domain and automotive security domain integration: (1) ensemble feature selection discovering common vulnerabilities, (2) V2X-Aware calibrated gradient descent that follows protocol constraints, and (3) CAN-Deep PackGen produces valid network-based CAN frames. Our attacks achieve evasion rates of up to 87.9% during experiments on KDDCup99 and UNSW-NB15 for NIDS and CICIoV2024 for CAN IDS. The transfer success rate for timing-related features surpasses 85% due to their vulnerability. The attack success rate receives 24.7% improvement from ensemble feature selection which exhibits strong correlation with feature importance in predicting attack transferability. The diagnostic CAN messages demonstrate the maximum vulnerability to attacks by achieving a$\text{9 2. 3 \%}$evasion rate. These findings highlight critical security implications for connected vehicles and demonstrate the need for cross-domain defensive strategies in automotive cybersecurity, providing a foundation for understanding and mitigating adversarial threats in interconnected transportation.

The increasing diffusion of Smart Environments enabled by the Internet of Things (IoT) technologies has evidenced the limitations of traditional Internet Protocol (IP), thus pushing for a paradigm shift from host-centric to Information-Centric Networking (ICN). The Named Data Networking (NDN) is a particular ICN implementation that prospects more efficient and effective communication and service provision, reason why it is widely considered as an enabler towards Future Internet. Driven by the PRISMA methodology, in this work we systematically survey the current literature and analyze opportunities and limitations of NDN adoption within Smart Environments, targeted application areas, adopted technologies and research gaps. In particular, by means of a research framework, we highlight how, by shifting from the traditional IP-based to NDN, Smart Environments can benefit from unseen degrees of mobility, scalability, security and performance, paving the way to innovative and cutting-edge cyberphysical services.

Network Intrusion Detection Systems (NIDS) are increasingly vulnerable to sophisticated packet-mutation attacks that evade traditional detection methods. This paper presents a runtime adaptive machine-learning strategy to combat such adversarial attacks. We introduce an Adaptive Layered Mutation Algorithm (ALMA) for generating advanced adversarial examples and a runtime adaptive learning framework for real-time detection and response. Our approach integrates these components to create a robust, self-evolving NIDS. Experiments comparing various feature extractors and machine learning classifiers demonstrate that our adaptive approach achieves up to $\mathbf{9 8 \%}$ detection accuracy, significantly improving the identification of mutated packets over static models. The integrated system rapidly adapts to new attack patterns, achieving over $\mathbf{9 0 \%}$ detection accuracy for novel attacks within 2-3 update cycles. This research contributes to network security by presenting an adaptive, high-performance approach to intrusion detection that effectively addresses challenges posed by evolving packet-mutation attacks, offering promising directions for next-generation NIDS development.

In lightweight block cipher design, generating permutation boxes (P-boxes) is critical to security and efficiency. This paper introduces an innovative approach to P-box generation by integrating nonlinear feedback shift registers (NFSRs) to enhance cryptographic strength. NFSRs are known for their capacity to generate obscure and unpredictable sequences, making them a promising candidate for improving P-box design. This research investigates the intricacies of this novel method, highlighting its potential benefits and implementation challenges. The proposed NFSR-based P-box generation method offers improved diffusion properties, presenting an attractive option for creating secure and efficient lightweight block ciphers.

Protecting software supply chains from malicious packages is paramount in the evolving landscape of software development. Attacks on the software supply chain involve attackers injecting harmful software into commonly used packages or libraries in a software repository. For instance, JavaScript uses Node Package Manager (NPM), and Python uses Python Package Index (PyPi) as their respective package repositories. In the past, NPM has had vulnerabilities such as the event-stream incident, where a malicious package was introduced into a popular NPM package, potentially impacting a wide range of projects. As the integration of third-party packages becomes increasingly ubiquitous in modern software development, accelerating the creation and deployment of applications, the need for a robust detection mechanism has become critical. On the other hand, due to the sheer volume of new packages being released daily, the task of identifying malicious packages presents a significant challenge. To address this issue, in this paper, we introduce a metadata-based malicious package detection model, MeMPtec. This model extracts a set of features from package metadata information. These extracted features are classified as either easy-to-manipulate (ETM) or difficult-to-manipulate (DTM) features based on monotonicity and restricted control properties. By utilising these metadata features, not only do we improve the effectiveness of detecting malicious packages, but also we demonstrate its resistance to adversarial attacks in comparison with existing state-of-the-art. Our experiments indicate a significant reduction in both false positives (up to 97.56%) and false negatives (up to 91.86%).

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks.

Security concerns for Internet of Things (IoT) applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and, therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning based ResNet model and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

Supervised machine learning (ML) approaches effectively derive valuable insights from big data. These approaches, on the other hand, require an extensive amount of high quality annotated data for training, created manually by domain experts through a costly and time-consuming process. To overcome this challenge, active learning (AL) is a promising approach, which can support a fast, cost-efficient and common strategy to deal with big data with limited labeling effort. Instead of annotating a large pool of unlabeled data, as in standard supervised learning, AL reduces the volume of data that requires manual annotation by effectively selecting subsets of highly informative samples for manual annotation within an iterative process. In this paper, we aim to present a robust approach utilizing AL to mitigate the aforementioned challenges and help the decision-makers. To be precise, we propose a framework involving a support vector machine (SVM) technique in AL for mining big data to manage inaccessible data situations. The proposed approach is tested on five different semi-supervised data sets. The performance of the proposed framework is evaluated using traditional ML classifiers such as Naïve Bayes (NB), Decision Tree (DT), Sequential Minimal Optimization (SMO), Random Forest (RF), Bagging and Adaboost. Among the reported classifiers, bagging achieves the best outcome, delivering 99.19% accuracy. According to the results of the experiment conducted we find that the proposed method increases the efficiency of the classifiers in AL with fewer training instances.

With the emergence and extensive deployment of biometric based user authentication system, ensuring the security of biometric template is becoming a growing concern in research community. One approach of securing biometric data is cancellable biometric which transforms the original biometric features into a non-invertible form for enrolment and matching. However, most of the schemes for generating cancellable template are alignment-based requiring an accurate alignment of query and enrolled images, which is very difficult to achieve. In this paper, we propose an alignment-free technique for generating revocable fingerprint template that exploits the local features i.e., minutiae details in a fingerprint image. A rotation and translation invariant values are extracted from the neighbouring region of each minutia. The invariant values are then used as inputs in a transformation function and combined with a stored and a user-specific key based random vectors using the type and orientation information of the minutiae. Hence, by varying the stored and user-specific keys in the transformation, multiple application-specific templates can be generated to preserve users’ privacy. Besides, if the transformed template is compromised, a new template can be reissued by assigning different keys for transformation to achieve revocability. Furthermore, the proposed approach preserves the actual geometric relationships between the enrolled and query templates even after transformation and offers reasonable recognition rate. Experiments conducted on FVC2000DB1 demonstrate that the proposed method exhibits promising performance in terms of recognition accuracy, computational complexity, security along with diversity, revocability and non-invertibility that are the key issues of cancellable template generation.

Applications and techniques

Summary In this paper, we extend our earlier work (where we presented a cross‐domain Session Initiation Protocol solution for mobile ad hoc networks using dynamic clustering) to handle packet losses affecting wireless networks and deal with outbound requests using reputation method. The (extended) solution is designed also to avoid the inherent shortcomings associated with centralized approaches (e.g. single point of failure). Using simulations, we evaluate the extended solution under different conditions. Findings from the evaluations demonstrate the utility of our solution. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Human detection and localization has attracted much attention in security applications because of the increasing demand of safety and security in different environments, including surveillance systems, secure access control, person recognition, border monitoring, preventing criminal acts, intrusion detection, alarm monitoring, and so on. This article proposes a robust approach for human detection and localization by analyzing and matching corresponding facial features extracted from video sequences. The proposed technique captures the video scenes using a stereo system consisting of two cameras: left and right cameras with similar intrinsic parameters. The system first tracks the human by detecting the face area from the video scenes using an efficient fuzzy face detection algorithm. To localize the human position, the depth information is computed from the extracted face images by using a robust stereo matching algorithm. A neural network is used to match the correspondence pixels between the left and the right face images. Experimental evaluation demonstrates the competence and robustness of the proposed method. The low computation time required for the detection and localization of human objects compared with other methods raises its suitability toward its use in real‐time applications. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti‐virus engines employ a signature‐template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti‐virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression‐based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper‐based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute‐force search strategies and order of (m‐1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m‐1). The proposed approach uses multi‐linear regression and thep‐value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi‐collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine‐based template approach with complete data and provides a better statistical fitness. Copyright © 2016 John Wiley & Sons, Ltd.

Image filtering is a key technology in image processing applications for de-noising corrupted images. Digital images are often polluted by noise during capturing and hence they may not show the features or colors clearly. Image filtering removes the noise in an image and improves the contrast to provide better input for various image processing applications. This paper proposes an efficient image filtering technique using fuzzy logic. The proposed method employs fuzzy membership functions in order to replace the noisy pixels based on the degree of membership of the neighboring pixels within a filter mask. Experimental results confirm that our method is very effective and fast for removing impulsive noise while preserving the small and sharp details in the image.

Microstrip patch antennas are becoming essential component in many emerging medical applications. The increase use of these antennas in such devices is due to a number of attractive properties of these antennas. In this paper, we present a novel design of microstrip patch antenna which can be used in mobile devices for microwave, wireless and eHealth applications. The proposed design of the antenna is based on rectangular structured slots in order to operate at multiple frequency bands. The slots are designed on the rectangular patch and fed by a microstrip feeder line. The combination of the proposed design and quarter wave transformer feeding technique allow the antenna to operate at multiple frequencies in the range of 3 – 12 GHz which is used for most of the wireless applications. It is shown that five different operating frequency bands have VSWR ≤ 2 which is an acceptable range for short to medium range wireless communication. The operating bands of frequency are: 4.7 GHz, 6.7 GHz, 8.9 GHz, 9.8 GHz and 10.9 GHz with VSWR ≤ 2. It is also observed that the gain of proposed design is higher than the conventional patch antenna which makes it more attractive choice for many applications. The proposed design ensures secure and efficient transmission as well as better transmission of input power, i.e. low values of Return Loss.

In this paper, we propose an algorithm for connected p-percent coverage problem in Wireless Sensor Networks(WSNs) to improve the overall network life time. In this work, we investigate the p-percent coverage problem(PCP) in WSNs which requires p% of an area should be monitored correctly and to find out any additional requirements of the connected p-percent coverage problem. We propose pDCDS algorithm which is a learning automaton based algorithm for PCP. pDCDS is a Degree-constrained Connected Dominating Set based algorithm which detect the minimum number of nodes to monitor an area. The simulation results demonstrate that pDCDS can remarkably improve the network lifetime.

This paper considers the sum-rate of wireless broadcast systems with multiple antennas at the base station. In a conventional MIMO-BC system with a large number of users, selecting an optimal subset of users to maximizing the overall system capacity is a key design issue. This paper presents a novel approach to investigate the sum-rate using Eigen Value Decomposition (EVD). Particularly, we derive the lower bound on sum-rate of a conventional MIMO-BC using a completely different approach compared to the existing approaches. The paper formulates the rate maximization problem for any number of users and any number of transmitting antennas using EVD approach of the channel matrix. This also shows the impact of channel angle information on the sum-rate of conventional MIMO-BC. Numerical results confirm the benefits of our technique in various MIMO communication scenarios.

The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository, to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.

Abstract This paper presents an innovative fusion‐based multi‐classifier e‐mail classification on a ubiquitous multi‐core architecture. Many previous approaches used text‐based single classifiers to identify spam messages from a large e‐mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e‐mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion‐based multi‐classifier classification technique in a multi‐core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi‐classifier‐based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e‐mail classification accuracy substantially compared with single classification techniques. Copyright © 2009 John Wiley & Sons, Ltd.

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

This paper presents the realization of multiple-valued flip-flops (MVFF) using pass transistor logic. Realization of MVFF has been discussed by many authors. The existing techniques are mainly extensions of binary flip-flops, based on CMOS or TTL logic. We propose here, two different design techniques for MVFF realized by pass transistors, which can be a promising alternative to static CMOS for deep sub-micron design. We have introduced a circuit consisting of multiple valued pass transistors which we call 'logical sum circuit'. This particular circuit is used as the elementary design component for our second approach in MVFF design. Our proposed MVFF circuits can be attractive for its inherent lesser power and component demands in comparison with existing techniques using MOS or TTL logic.

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generationand- test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. The Pattern Decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass makes it more efficient to mine all frequent patterns in a large dataset. This algorithm avoids the costly process of candidate set generation and saves a great amount of counting time to evaluate support with reduced datasets. In this paper, some existing frequent pattern generation algorithms are explored, their comparisons are discussed, which shows that the PD algorithm outperforms an improved version of Apriori named Direct Count of candidates & Prune transactions (DCP) by one order of magnitude and is faster than an improved FP-tree (Frequent Pattern) named as Predictive Item Pruning (PIP). Further, PD is also more scalable than the DCP and PIP.

Data refers to extracting or mining knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data in order to discover the relevant knowledge for diagnosis. It is very useful if results of data can be communicated to humans in an understandable way. In this paper, we present our work on data in medical image archiving systems. We investigate the use of a very efficient data technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

In this paper, an improved design of easily testable PLAs has been proposed, based on input decoder augmentation using pass transistor (PT) logic along with improved conditions for product line grouping. The proposed technique primarily increases the fault coverage area of easily testable PLAs due to augmented PT and reduced testing time due to grouping the product lines. A simultaneous testing technique has been applied within the group that reduces the testing time. This approach ensures the detection of certain bridging faults, which were not considered by the existing techniques. A modified testing technique has also been presented in this paper. It is shown that the new grouping technique enhances the devices in all respects.

Reversible logic is of the growing importance to many future technologies. A reversible circuit maps each output vector, into a unique input vector, and vice versa. This paper introduces an approach to synthesise the generalized multi-rail reversible cascades and minimizing the "garbage bit" and number of reversible gates, which is the main challenge of reversible logic synthesis. This proposed full-adder circuit contains only three gates and two garbage outputs whereas earlier full-adder circuit by M. Perkowski et al. (2001) requires four gates and produces two garbage outputs and another existing full-adder circuit by Md. H. H Azad Khan (2002) requires three gates but produces three garbage outputs. Thus, the proposed full-adder circuit is efficient in terms of number of gates with compared to M. Perkowski et al. (2001) as well as in terms of number of garbage outputs with compared to Md. H. H Azad Khan (2002).