Ponzi schemes, a more than a century-old fraud, have recently infiltrated blockchain-based cryptocurrency domain led by an explosion of such schemes in two most popular cryptocurrencies: Bitcoin and Ethereum. On these two platforms alone, the perpetrators of these frauds have fleeced gullible investors of billions of dollars annually. Smart Ponzi schemes are a hazard to these cryptocurrency ecosystems, diminishing investor confidence in these cutting-edge technologies, threatening their integrity, and hindering their growth and broader adaptation. These smart Ponzi schemes have also created a nightmare for law enforcement as tracking and taking countermeasures against fraudsters and recovering the victims’ investment is challenging. Over the years, researchers have utilized significant advances in machine learning and AI to detect and promptly caution users against investing in Ponzi schemes on Bitcoin and Ethereum. However, this research still exists in silos, and there is a lack of a detailed survey paper critically analyzing various aspects of the approaches focusing on the menace of smart Ponzi schemes. This article surveys the state-of-the-art techniques proposed in the literature to detect smart Ponzi schemes on two popular blockchain platforms: Bitcoin and Ethereum. We list, categorize, and discuss papers that contributed benchmark datasets, developed novel features concerning various aspects of smart Ponzi schemes, and proposed novel machine-learning approaches to detect them.

Cross-chain bridges are essential for enabling interoperability between diverse blockchain networks. At the same time, these technologies have introduced new avenues for illicit financial activity, such as chain-hopping, a method used to obscure transactional provenance by rapidly transferring assets across multiple chains. This study analyzes cross-chain token flows on the Wormhole bridge, focusing specifically on identifying suspicious token transfer patterns. We propose a methodology inspired by state-of-the-art anomaly detection techniques to identify and examine suspicious transaction patterns. Our findings provide valuable insights into cross-chain analysis and lay the groundwork for developing monitoring systems that can detect and analyze illicit behavior within cross-chain ecosystems.

Blockchain networks securely record transactions and enable decentralised transactions using cryptocurrencies. However, the pseudonymity nature of the participants makes the blockchain network a platform for illegal activities, such as money laundering, which poses significant threats to financial security and regulatory compliance. Money laundering activities undermine the integrity of financial systems, foster criminal enterprises, and enable tax evasion. This research explores the impact of timestamp-based (Time step) features in detecting money laundering activities within the Bitcoin network, utilising the Elliptic++ dataset. A correlation-based analysis revealed that the first block appeared in feature was the most strongly correlated with the Time step. Additionally, classification results highlighted XGBoost as the most effective classifier, with the first block appeared in feature identified as the most influential, based on Shapley values from the eXplainable Artificial Intelligence (XAI) technique.

Smart contracts are a key part of blockchain applications, and attackers can exploit them to manipulate contract behaviour or steal assets. Smart contracts often contain security vulnerabilities, either accidentally introduced by developers or due to flawed business logic. In this paper, we focus on finding an optimal Machine Learning based framework for detecting vulnerable smart contracts by analysing the smart contracts as embedding vectors. CodeBERT, a pre-trained transformer model, is used for feature extraction in the proposed framework. The framework has shown approximately 97% accuracy in detecting smart contracts that contain various vulnerabilities. Additionally, the research explores the performance of CodeBERT variants for this task. The results of the experiments have proven the favourability of this framework in detecting vulnerable smart contracts.

This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI), especially in the context of federated learning, for credit scoring in financial sectors to improve the credit assessment process. Research shows that integration of these cutting-edge technologies is in its infancy, specifically in the areas of embracing broader data, model verification, behavioural reliability and model explainability for intelligent credit assessment. The conventional credit risk assessment process utilises historical application data. However, reliable and dynamic transactional customer data are necessary for robust credit risk evaluation in practice. Therefore, this research proposes a framework for integrating blockchain and XAI to enable automated credit decisions. The main focus is on effectively integrating multi-party, privacy-preserving decentralised learning models with blockchain technology to provide reliability, transparency, and explainability. The proposed framework can be a foundation for integrating technological solutions while ensuring model verification, behavioural reliability, and model explainability for intelligent credit assessment.

Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance. However, the participation of a few actors with malicious intentions challenges law enforcement authorities, regulators and other users. These challenges revolve around dealing with an array of illegal activities such as asset trades in dark markets, receiving payments for cyber-attacks, and facilitating money laundering. Developing an efficient mechanism to identify malicious actors in blockchain networks is a pressing need to build confidence among the stakeholders and ensure regulatory adherence. The raw data of blockchain transactions do not readily reveal the dynamic behavioural changes and their interconnection between transactions and accounts. These behavioural patterns can be useful for identifying malicious actors. Machine Learning (ML)-based models for early warning and/or detection are considered one of the potential approaches. In ML, feature engineering plays a crucial role in enhancing the predictive performance of a model. This study proposes different categories of features and unified feature extraction approaches for raw Bitcoin and Ethereum transaction data and their interconnection information. As far as we are aware, there has been no study that considered a feature engineering approach for identifying malicious activities. The significance of the engineered features was validated against eight classifiers, including Random Forest (RF), XG-boost (XG), Silas, and neural network-based classifiers. The results showed that these features contribute to higher classification accuracy and higher Area Under the Receiver Operating Characteristic Curve (AUC) value for both Bitcoin and Ethereum transactions. This work also analysed the influence of engineered features in classification using the eXplainable Artificial Intelligence (XAI) technique SHapley Additive exPlanations (SHAP) values. The feature importance scores confirmed the significance of the proposed engineered features towards implementing classification models to identify, target and disrupt malicious activities in blockchain networks.

Blockchain technology is becoming a promising technological solution for enterprise applications with the rise of interoperable solutions. A cross-chain architecture facilitates interoperability, thus improves its chain efficiency, reduces fragmentation, and allows users and features to flow more freely across multiple blockchains. However, enabling interoperability in silo networks will make a significant functional trade-off on the security and performance of the system. This paper review trade-offs in blockchain technologies related to interoperability.

Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.

The future of multi-blockchain architecture depends on the emergence of new protocols that achieve communication between trustless cross-chain participants. However, interoperability between blockchains remains an open problem. Existing approaches provide integration through solutions using a middleware system, which makes it harder to gain confidence mainly in terms of security and correctness of the process. A cross-chain protocol needs to provide a self-verifiable state-proof that embeds trust in the transfer process. We propose a Burn-to-Claim cross-chain protocol to seamlessly exchange assets between networks. Our scheme transfers assets from one blockchain system to another in a way that the asset is burned from the source blockchain and claimed on the destination blockchain. Our mechanism employs combinations of crypto mechanisms such as digital signatures and time lock to operate the protocol in a distributed manner. We provide an analysis which proves that our cross-chain protocol transfers assets correctly and securely.

Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosenplaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size M × N and with L different color intensities, the number n of required chosen plain-images to break the permutation-only image encryption algorithm is n = ΓlogL(MN)1. The complexity of the proposed attack is O (n · M N) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-theart cryptanalytic methods.

Due to widespread applications of 3D vision technology, the research into 3D object protection is primarily important. To maintain confidentiality, encryption of 3D objects is essential. However, the requirements and limitations imposed by 3D objects indicate the impropriety of conventional cryptosystems for 3D object encryption. This suggests the necessity of designing new ciphers. In addition, the study of prior works indicates that the majority of problems encountered with encrypting 3D objects are about point cloud protection, dimensional and spatial stability, and robustness against surface reconstruction attacks. To address these problems, this paper proposes a 3D object encryption scheme, based on a series of random permutations and rotations, which deform the geometry of the point cloud. Since the inverse of a permutation and a rotation matrix is its transpose, the decryption implementation is very efficient. Our statistical analyses show that within the cipher point cloud, points are randomly distributed. Furthermore, the proposed cipher leaks no information regarding the geometric structure of the plain point cloud, and is also highly sensitive to the changes of the plaintext and secret key. The theoretical and experimental analyses demonstrate the security, effectiveness, and robustness of the proposed cipher against surface reconstruction attacks.

In this paper a new energy-efficient error control mechanism based on Redundant Residue Number System (RRNS) is proposed for Wireless Sensor Networks (WSNs). In general, WSNs suffer from burst errors due to their inherent nature. An error detection and correction approach has been developed to handle burst errors more efficiently. Residue encoders transform the sensed data to small-sized and independent code-words with redundant information. When an error is detected at the base station, a new module of remainder code-words will be requested from the encoder nodes. This approach creates spatial diversity against fading in a wireless channel. Overall, the proposed scheme results in saving transmission energy, adding negligible encoder energy consumption.

Increasing throughput of modern high-speed networks needs accurate real-time Intrusion Detection System (IDS). A traditional packet-based Network IDS (NIDS) is time-intensive as it inspects all packets. A flow-based anomaly detector addresses scalability issues by monitoring only packet headers. This method is capable of detecting unknown attacks in high speed networks. An Artificial Neural Network (ANN) is employed in this research to detect anomalies in flow-based traffic. Metaheuristic optimization algorithms have the potential to achieve global optimal solution. In this paper, two metaheuristic algorithms, Cuckoo and PSOGSA, are examined to optimize the interconnection weights of a Multi-Layer Perceptron (MLP) neural network. This optimized MLP is evaluated with two different flow-based data sets. We then compare the performance of these algorithms. The results show that Cuckoo and PSOGSA algorithms enable high accuracy in classifying benign and malicious flows. However, the Cuckoo has lower training time.

This paper studies and analyzes the influence of temperature, depth and salinity on speed of acoustic signal for a vertical water column. In underwater wireless sensor networks (UWSN), determination of precise acoustic signal is sometimes necessary to determine the coordinates of sensors for the validity of data. The effect of temperature on sound speed is predominant where temperature change is noticeable. In the deep ocean where temperature change is very nominal, depth (pressure) becomes predominant and salinity effect is even less. In this paper we have shown how these variables affect acoustic signal speed for a specific depth of water column where temperature, depth and salinity changes are linear.

This paper investigates the problem of localizing submerged sensors and provides a new mechanism to determine the coordinates of those sensors using only one beacon node. In underwater wireless sensor networks (UWSN), precise coordinate of the sensors that actuate or collect data is vital, as data without the knowledge of its actual origin has limited value. Mostly, multi-lateration technique is used to determine the location of the sensors with respect to three or more known beacon nodes where distance between them is measured considering the roundtrip time of acoustic signal. However, this method of measuring distances gives erroneous results due to a number of factors, including relative angular stand of the nodes. In this study, a new method of determining the underwater distances between beacon and sensor nodes has been presented using combined radio and acoustic signals, which has better immunity from multipath fading. Moreover, Cayley-Menger determinant is used to determine the coordinates of the nodes where none of nodes have a priori knowledge about its location. Simulation results validate the proposed mathematical models by computing coordinates of sensor nodes with negligible errors.

In this paper we have proposed an energy efficient clique based clustering and routing scheme for wireless sensor networks. Developing energy efficient routing protocol is one of the major challenges in wireless sensor networks. Since the Sensor Nodes (SN) are small-sized battery operated devices, energy conservation is considered a critical factor in maximizingnetwork lifetime. Our proposed routing protocol partitions sensor networks into several disjoint cliques such that each node is in single hop distance from all of its neighbour nodes. At the beginning of the clustering process, each node obtains a list of its neighbours' connectivity as well as their degree of connection. Then, the node with the highest degree of connection initiates clique formation process and makes the cluster. Among the members of a cluster, the node with the maximum energy is selected as cluster head. Cluster head rotation depends on threshold value, which is obtained by averaging residual energy of all the nodes in a cluster and then making it half. Simulation results with MATLAB show that our proposed protocol is effective in terms of network lifetime as well as total energy dissipation.

Mobile Ad hoc Networks (MANETs) consist of a large number of relatively low-powered mobile nodes communicating in a network using radio signals. Clustering is one of the techniques used to manage data exchange amongst interacting nodes. Each group of nodes has one or more elected Cluster head(s), where all Cluster heads are interconnected for forming a communication backbone to transmit data. Moreover, Cluster heads should be capable of sustaining communication with limited energy sources for longer period of time. Misbehaving nodes and cluster heads can drain energy rapidly and reduce the total life span of the network. In this context, selection of best cluster heads with trusted information becomes critical for the overall performance. In this paper, we propose Cluster head(s) selection algorithm based on an efficient trust model. This algorithm aims to elect trustworthy stable cluster head(s) that can provide secure communication via cooperative nodes. Simulations were conducted to evaluate trusted Cluster head(s) in terms of clusters stability, longevity and throughput.

Rapid convergence of heterogeneous wireless communication technologies such as Wireless Local Area Networks (WLAN), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) etc., attract new opportunities for collaborative usage. More and more applications are emerging to benefit from their advantages. However, with the range of approaches that are used to authenticate the wireless devices in such heterogeneous environments, users are skeptical and looking for more user friendly, flexible and reliable ways to interconnect and utilize these different classes of wireless networks. Wireless network users access the different types of wireless networks either independently or cooperatively. In either case, adequate security provision is critical for the successful operation of the networks. Moreover, emerging technologies should provide seamless transition / migration between these networks. Hence the ability to use a single but unique set of credentials to authenticate the wireless devices in heterogeneous wireless network environments would be an anticipated desire of most users. In this paper a number of authentication mechanisms are examined and evaluated for their advantages and limitations. We then propose a unified authentication protocol that can be encapsulated within the RADIUS protocol utilizing the advantages of public key infrastructure. The preliminary experimental results demonstrate that the proposed protocol is feasible and relatively fast.

The inherent freedom in self-organized mobile ad-hoc networks (MANETs) introduces challenges for trust management; particularly when nodes do not have any prior knowledge of each other. Furthermore in MANETs, the nodes themselves should be responsible for their own security. We propose a novel approach for trust management in MANETs that is based on the nodes' own responsibility of building their trust level and node-level trust monitoring. The main contribution of this work is in the introduction of a Node based Trust Management (NTM) scheme in MANET based on the assumption that individual nodes are themselves responsible for their own trust level. We explore and develop the mathematical framework of trust in NTM. Finally, in this context, we demonstrate our scheme with notations, algorithms, analytical model and prove of its correctness.

Wireless networks are being used increasingly in industrial, health care, military and public-safety environments. In these environments security is extremely important because a successful attack against the network may pose a threat to human life. To secure such wireless networks against hostile attack requires both preventative and detective measures.In this paper we propose a novel intrusion detection mechanism that identifies man-in-the-middle and wormhole attacks against wireless mesh networks by external adversaries. A simple modification to the wireless MAC protocol is proposed to expose the presence of an adversary conducting a frame-relaying attack. We evaluate the modified MAC protocol experimentally and show the detection mechanism to have a high detection rate, no false positives and a small computational and communication overhead.

APCO Project 25 (P25) is the digital communications standard that has widespread deployment amongst emergency first-responders in several different countries. This paper describes the implementation of a low-cost software-defined radio receiver for APCO Project 25 signals. The OP25 Receiver has been developed as part of an investigation into the security of the P25 protocol suite and provides low-level access to the actual message traffic using the WireShark packet sniffer. The proposed OP25 Receiver is a useful diagnostic and security analysis tool. Our initial experience suggests that the flexibility of the software-defined radio approach is well-suited to meeting the varying needs of public-safety radio communications.

A wireless sensor network can be used in a home health care system to monitor the elderly or patients with chronic diseases. The quality of security of the home health care system is an important requirement. If the security services or servers malfunction then the system itself may become compromised. We show that with multi--server protocols, even if one or more servers become unavailable or untrustworthy, it may still be possible for the sensor nodes to establish a good session key. We compare and contrast different multi--server protocols. We also show which protocols are more suited for our system. The protocols were implemented in TinyOS and run on mica2 motes. The time elapsed, complexity of the code, and memory requirements are analysed in detail. We show that a symmetric key implementation has advantages over an asymmetric implementation.

Advancement in Information and Communications Technologies (ICT) has increased privacy concerns among citizens. In this paper we examine how interconnected modules could be developed to mitigate those concerns and foster trust among e-Government stakeholders. Current e-Government measurement models do not adequately address privacy issues. We suggest that privacy enforcements should be added as major criteria of evaluation. Moreover, users should be able to choose the level of privacy and security needed for any system interaction. Thus, a conceptual model that allows for users to easily control the selection of privacy preferences is needed. A Decision Fusion Module takes the user inputs, such as level of privacy, pseudonymity and trust, and processes them to output the best possible selection of security mechanisms corresponding to those user preferences. In fact, e-Government models could benefit from the earned user trust.

With the increasing dependence on wireless LANs (WLANs), businesses, educational institutions and other organizations are in need of a reliable security mechanism. The latest security protocol, the IEEE 802.11i assures rigid security for WLANs with the support of IEEE 802.1x protocol for authentication, authorization and key distribution. Nevertheless, fresh security threats are emerging often to oust these new defense mechanisms. Further, many organizations based on superficial vendor literature, believe their wireless security is sufficient enough to prevent any unauthorized access. Having wide ranging options for security configurations, users are camouflaged into profound uncertainty. This volatile state of affairs has prevented many organizations from fully deploying WLANs for their secure communication needs, though WLANs may be cost effective and flexible. In this paper, we present an anomaly based mechanism to detect and substantiate both known and unknown security threats in WLANs. Our method exploits both timing and behavioral anomalies. We first observe for timing and/or behavior anomalies during the security association process and use outlier based data association approaches to substantiate their legitimacy. The proposed concept was tested on our experimental setup and the results obtained from EAP TLS authenticated hosts are presented here.

With the increasing dependence on wireless LANs (WLANs), businesses and educational institutions are in desperate need of a robust security mechanism. The latest WLAN security protocol, the IEEE 802.11i introduces Robust Security Networks (RSN) and assures rigid security for wireless environments with the support of IEEE 802.1x protocol for authentication, authorization and key distribution. Nevertheless, users remain skeptical since they lack confidence on the trustworthiness of these security mechanisms. In this paper we investigate and test a novel Early Warning System (EWS), built on the foundations of IEEE 802.11i security architecture. Our system is developed to detect anomalies and prevent intrusions in real-time. It has several levels of defence to protect the wireless hosts from a range of security threats. Security alerts are raised only when the legitimacy of abnormal conditions is validated using effective outlier based data association techniques.

As a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signature's contour using the modified direction feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available database of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%.

Signatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signature's contour. A novel combination of the modified direction feature (MDF) and additional distinguishing features such as the centroid, surface area, length and skew are used for classification. A resilient backpropagation (RBP) neural network and a radial basis function (RBF) network were compared in terms of verification accuracy. Using a publicly available database of 2106 signatures (936 genuine and 1170 forgeries), verification rates of 91.21% and 88.0% were obtained using RBF and RBP respectively.

This paper describes the Generic Automated Marking Environment (GAME) and provides a detailed analysis of its performance in assessing student programming projects and exercises. GAME has been designed to automatically assess programming assignments written in a variety of languages based on the "structure" of the source code and the correctness of the program's output. Currently, the system is able to mark programs written in Java, C++ and the C language. To use the system, instructors are required to provide a simple "marking schema" for any given assessment item, which includes pertinent information such as the location of files and the model solution. In this research, GAME has been tested on a number of student programming exercises and assignments. The results obtained, have been analysed and compared against a human marker providing encouraging results.

Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2D-DFT transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings produced very promising results. The results from large sets of experiments and detailed analysis are included in this paper.