Search Results (18)

The growing sports betting market works on the premise that sports are unpredictable, making it more likely to be wrong than right, as the user has to choose between win, draw, or lose. So could football, the world’s most popular sport, be predictable? This article studies this question using deep neural networks to predict the outcome of football matches using publicly available data. Data from 24,760 matches from 13 leagues over 2 to 10 years were used as input for the neural network and to generate a state-of-the-art validated feature, the pi-rating, and the parameters proposed in this work, such as relative attack, defence, and mid power. The data were pre-processed to improve the network’s interpretation and deal with missing or inconsistent data. With the validated pi-rating, data organisation methods were evaluated to find the most fitting option for this prediction system. The final network has four layers with 100, 80, 5, and 3 neurons, respectively, applying the dropout technique to reduce overfitting errors. The results showed that the most influential features are the proposed relative defending, playmaking, and midfield power, and the home team goal expectancy features, surpassing the pi-rating. Finally, the proposed model obtained an accuracy of 52.8% in 2589 matches, reaching 80.3% in specific situations. These results prove that football can be predictable and that some leagues are more predictable than others. Full article

As a bridge for international trade, maritime transportation security is crucial to the global economy. Southeast Asian waters have become a high-incidence area of global piracy attacks due to geographic location and complex security situations, posing a great threat to the development of the Maritime Silk Road. In this study, the factors affecting the risk of pirate attacks are analyzed in depth by using the Global Ship Piracy Attacks Report from the IMO Global Integrated Shipping Information System (GISIS) database (i.e., 2013–2022) in conjunction with a Bayesian Network (BN) model, and the Expectation Maximization algorithm is used to train the model parameters. The results show that piracy behaviors and the ship’s risk are the key factors affecting the risk of pirate attacks, and suggestions are made to reduce the risk of pirate attacks. This study develops a theoretical basis for preventing and controlling the risk of pirate attacks on ships, which helps maintain the safety of ship operations. Full article

Reverse engineering (RE) in Integrated Circuits (IC) is a process in which one will attempt to extract the internals of an IC, extract the circuit structure, and determine the gate-level information of an IC. In general, the RE process can be done for validation as well as Intellectual Property (IP) stealing intentions. In addition, RE also facilitates different illicit activities such as the insertion of hardware Trojan, pirating, or counterfeiting a design, or developing an attack. In this work, we propose an approach to introduce cognitive perturbations, with the aid of adversarial machine learning, to the IC layout that could prevent the RE process from succeeding. We first construct a layer-by-layer image dataset of 45 nm predictive technology. With this dataset, we propose a conventional neural network model called RecoG-Net to recognize the logic gates, which is the first step in RE. RecoG-Net is successful in recognizing the gates with more than 99.7% accuracy. Our thwarting approach utilizes the concept of adversarial attack generation algorithms to generate perturbation. Unlike traditional adversarial attacks in machine learning, the perturbation generation needs to be highly constrained to meet the fab rules such as Design Rule Checking (DRC) Layout vs. Schematic (LVS) checks. Hence, we propose CAPTIVE as a constrained perturbation generation satisfying the DRC. The experiments show that the accuracy of reverse engineering using machine learning techniques can decrease from 100% to approximately 30% based on the adversary generator. Full article

Maritime transportation is vital for the movement of cargo between different continents and distant locations but can be disrupted by the frequent occurrence of pirate attacks. Based on the pirate attacks from July 1994 to December 2019, a spatial analysis of pirate attacks using a Geographic Information System (GIS) was conducted in the present study using the data available for tankers, dry bulk carriers, container vessels, general cargo vessels, and tugs. The adoption of the kernel density analysis was intended to identify the spatial pattern of global pirate attacks. The research results demonstrated that the pirate attacks showed a clustering pattern and were mostly associated with areas experiencing economic depression, a high unemployment rate, and social unrest. Accordingly, spatiotemporal hot spot analysis was carried out to recognize the changing directions of cold spots and hot spots over a period of time. The waters off Somalia, the Strait of Malacca, the Philippines, the Bay of Bengal, the Gulf of Guinea, and the northwest of South America were found to be the common locations of pirate attacks. The cold and hot spots of pirate attacks on the three key vessel types, including tankers, dry bulk carriers, and container vessels, were found to be similar. When considering the same area, the trends of cold and hot spots of different vessel types being attacked were substantially different. This study can provide a useful guideline for the International Maritime Organization and other relevant organizations in the world to design and implement targeted strategies to combat and mitigate pirate attacks. Additionally, the introduction of a GIS may help to envision the spatial and temporal distribution of pirate attacks and to explore the characteristics of pirate behaviors at sea and the patterns of piracy. Full article

The Maritime Silk Road (MSR) is an important channel for maritime trade between China and other countries in the world. Maritime piracy has brought huge security risks to ships’ navigation and has seriously threatened the lives and property of crew members. To reduce the likelihood of attacks from pirates, it is necessary to study the risk to a ship exposed to attacks from pirates on the MSR. Firstly, risk factors were established from three risk component categories (hazard, mitigation capacity, and vulnerability and exposure) and the risk index system of piracy and armed robbery events was founded. Secondly, the dynamic Bayesian network (DBN) method was introduced to establish a pirate attack risk assessment model ad to conduct a quantitative analysis of the process risk of a ship being attacked by pirates. Finally, combined with the scene data of the MSR, the process risk of a ship being attacked by pirates was modeled and applied as an example. The results showed that the overall risk of a ship being attacked by pirates is the lowest in July and the highest in March. In the whole route, when the ship was in the Gulf of Guinea, the Gulf of Aden–Arabian Sea, and the Strait of Malacca, the risk of pirate attack was the highest. This dynamic network model can effectively analyze the level of risk of pirate attacks on ships, providing a reference for the safety decision-making of ships on ocean routes. Full article

Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among stakeholders regarding the potential security threats posed by AI in MASSs. To achieve this, we propose a hypothetical attack scenario in which a clean-label poisoning attack was executed on an object detection model, which resulted in boats being misclassified as ferries, thus preventing the detection of pirates approaching a boat. We used the poison frog algorithm to generate poisoning instances, and trained a YOLOv5 model with both clean and poisoned data. Despite the high accuracy of the model, it misclassified boats as ferries owing to the poisoning of the target instance. Although the experiment was conducted under limited conditions, we confirmed vulnerabilities in the object detection algorithm. This misclassification could lead to inaccurate AI decision making and accidents. The hypothetical scenario proposed in this study emphasizes the vulnerability of object detection models to clean-label poisoning attacks, and the need for mitigation strategies against security threats posed by AI in the maritime industry. Full article

The tarnished plant bug, Lygus lineolaris, is a major strawberry pest. Only marginally effective control methods exist to manage this pest. Various predators attack L. lineolaris, but their potential is overlooked. In this study, we explore the potential of two omnivorous predators of the tarnished plant bug: the damsel bug, Nabis americoferus, and the minute pirate bug, Orius insidiosus. Firstly, the predation rate of these predators was measured in laboratory tests. Secondly, their potential release rates and release periods were determined in the field using strawberry plants. The results show that N. americoferus feeds on all nymphal stages and adults of the tarnished plant bug, while O. insidiosus attacks only smaller nymphs (up to the N2 stage). In the field, all tested densities of N. americoferus (0.25, 0.5, and 0.75 individual/plant) reduced the population of the tarnished plant bug for several weeks compared with the control treatment, but the effect of O. insidiosus alone was marginal. Additionally, for all the release periods tested, Nabis americoferus was efficient in reducing the pest population. These results demonstrate the potential of N. americoferus to control the tarnished plant bug in strawberry fields. We discuss the possible application of these results for establishing an effective and economically viable biological control strategy. Full article

Because of the many limitations of the traditional failure mode effect and criticality analysis (FMECA), an integrated risk assessment model with improved FMECA, fuzzy Bayesian networks (FBN), and improved evidence reasoning (ER) is proposed. A new risk characterization parameter system is constructed in the model. A fuzzy rule base system based on the confidence structure is constructed by combining fuzzy set theory with expert knowledge, and BN reasoning technology is used to realize the importance ranking of the hazard degree of maritime logistics risk events. The improved ER based on weight distribution and matrix analysis can effectively integrate the results of risk event assessment and realize the hazard evaluation of the maritime logistics system from the overall perspective. The effectiveness and feasibility of the model are verified by carrying out a risk assessment on the maritime logistics of importing bauxite to China. The research results show that the priority of risk events in the maritime logistics of bauxite are “pirates or terrorist attacks” and “workers’ riots or strikes” in sequence. In addition, the bauxite maritime logistics system is at a medium- to high-risk level as a whole. The proposed model is expected to provide a systematic risk assessment model and framework for the engineering field. Full article

Fishing wealth is one of the richest resources in the Sultanate of Oman. It is considered as one of the most important economic developments that nation depends on in a larger way. The Sultanate of Oman is characterized by the presence of a large fishing fleet as the number of fishing vessels and boats in it. Good research with the application of modern technology in fishermen boats is required to increase the quality of fishing by providing fishermen with a safe and secure fishing experience. Artificial intelligence (AI) in boat automation technology is new and it is a mandatory demand for Oman’s fisheries sector. At the time of fishing, there are a lot of problems fishermen face such as weather changes, border tracking, navigation, illegal fishing, pirate attack, oil spill, technical fault in boats, etc. Therefore, the application of AI and related techniques in boat automation, information sharing, and preparation of documentation resources is very important in this sector. The main requirement for a fisherman is a high-quality fishing boat with proper communication devices to provide all the required information to fishermen and the control room. In this paper, a review has been made on fishermen’s boats with artificial intelligence for a sustainable fishing experience ensuring safety, security, navigation, and sharing information for Omani fishermen. Full article

In this paper, a new fractional-order chaotic system containing several nonlinearity terms is introduced. This new system can excite hidden chaotic attractors or self-excited chaotic attractors depending on the chosen system parameters or its fraction-order derivative value. Several dynamics of this new system, such as chaotic attractors, equilibrium points, Lyapunov exponents, and bifurcation diagrams, are analyzed analytically and numerically. Then, adaptive control laws are developed to achieve chaos synchronization in two identical new systems with uncertain parameters; one of these two new identical systems is the master, and the other is the slave. In addition, update laws for estimating the uncertain slave parameters are derived. Furthermore, in chaos application fields, these master and slave synchronized systems are applied in secure communication to act as the transmitter and receiver, respectively. Finally, the security analysis metric tests were analyzed using histograms and spectrograms to establish the communication system’s security strength. Numerical test results demonstrate the possibility of using this proposed fractional-order chaotic system in high-security communication systems. The employed communication system is also highly resistant to pirate attacks. Full article

In comparison with integer-order chaotic systems, fractional-order chaotic systems exhibit more complex dynamics. In recent years, research into fractional chaotic systems for the utilization of image cryptosystems has become increasingly highlighted. This paper describes the development, testing, numerical analysis, and electronic realization of a fractional-order memcapacitor. Then, a new four-dimensional (4D) fractional-order memcapacitive hyperchaotic system is suggested based on this memcapacitor. Analytically and numerically, the nonlinear dynamic properties of the hyperchaotic system have been explored, where various methods, including equilibrium points, phase portraits of chaotic attractors, bifurcation diagrams, and the Lyapunov exponent, are considered to demonstrate the chaos behaviour of this new hyperchaotic system. Consequently, an encryption cryptosystem algorithm is used for colour image encryption based on the chaotic behaviour of the memcapacitive model, where every pixel value of the original image is incorporated in the secret key to strengthen the encryption algorithm pirate anti-attack robustness. For generating the keyspace of that employed cryptosystem, the initial condition values, parameters, and fractional-order derivative value(s) (q) of the memcapacitive chaotic system are utilized. The common cryptanalysis metrics are verified in detail by histogram, keyspace, key sensitivity, correlation coefficient values, entropy, time efficiency, and comparisons with other recent related fieldwork in order to demonstrate the security level of the proposed cryptosystem approach. Finally, images of various sizes were encrypted and recovered to ensure that the utilized cryptosystem approach is capable of encrypting/decrypting images of various sizes. The obtained experimental results and security metrics analyses illustrate the excellent accuracy, high security, and perfect time efficiency of the utilized cryptosystem, which is highly resistant to various forms of pirate attacks. Full article

Fractional-order chaotic systems have more complex dynamics than integer-order chaotic systems. Thus, investigating fractional chaotic systems for the creation of image cryptosystems has been popular recently. In this article, a fractional-order memristor has been developed, tested, numerically analyzed, electronically realized, and digitally implemented. Consequently, a novel simple three-dimensional (3D) fractional-order memristive chaotic system with a single unstable equilibrium point is proposed based on this memristor. This fractional-order memristor is connected in parallel with a parallel capacitor and inductor for constructing the novel fractional-order memristive chaotic system. The system’s nonlinear dynamic characteristics have been studied both analytically and numerically. To demonstrate the chaos behavior in this new system, various methods such as equilibrium points, phase portraits of chaotic attractor, bifurcation diagrams, and Lyapunov exponent are investigated. Furthermore, the proposed fractional-order memristive chaotic system was implemented using a microcontroller (Arduino Due) to demonstrate its digital applicability in real-world applications. Then, in the application field of these systems, based on the chaotic behavior of the memristive model, an encryption approach is applied for grayscale original image encryption. To increase the encryption algorithm pirate anti-attack robustness, every pixel value is included in the secret key. The state variable’s initial conditions, the parameters, and the fractional-order derivative values of the memristive chaotic system are used for contracting the keyspace of that applied cryptosystem. In order to prove the security strength of the employed encryption approach, the cryptanalysis metric tests are shown in detail through histogram analysis, keyspace analysis, key sensitivity, correlation coefficients, entropy analysis, time efficiency analysis, and comparisons with the same fieldwork. Finally, images with different sizes have been encrypted and decrypted, in order to verify the capability of the employed encryption approach for encrypting different sizes of images. The common cryptanalysis metrics values are obtained as keyspace = 2⁶⁴⁸, NPCR = 0.99866, UACI = 0.49963, H(s) = 7.9993, and time efficiency = 0.3 s. The obtained numerical simulation results and the security metrics investigations demonstrate the accuracy, high-level security, and time efficiency of the used cryptosystem which exhibits high robustness against different types of pirate attacks. Full article

Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, and civilian security are only a few of the large number of applications which currently benefit from unmanned vehicles. One of the most actively studied topic is their full autonomy; i.e., the design of marine vehicles capable of pursuing a task while reacting to the changes of the environment without the intervention of humans, not even remotely. Environmental dynamicity may consist of variations of currents, the presence of unknown obstacles, and attacks from adversaries (e.g., pirates). To achieve autonomy in such highly dynamic uncertain conditions, many types of autonomous path planning problems need to be solved. There has thus been a commensurate number of approaches and methods to optimize this kind of path planning. This work focuses on game-theoretic approaches and provides a wide overview of the current state of the art, along with future directions. Full article

This article presents a novel four-dimensional autonomous fractional-order chaotic system (FOCS) with multi-nonlinearity terms. Several dynamics, such as the chaotic attractors, equilibrium points, fractal dimension, Lyapunov exponent, and bifurcation diagrams of this new FOCS, are studied analytically and numerically. Adaptive control laws are derived based on Lyapunov theory to achieve chaos synchronization between two identical new FOCSs with an uncertain parameter. For these two identical FOCSs, one represents the master and the other is the slave. The uncertain parameter in the slave side was estimated corresponding to the equivalent master parameter. Next, this FOCS and its synchronization were realized by a feasible electronic circuit and tested using Multisim software. In addition, a microcontroller (Arduino Due) was used to implement the suggested system and the developed synchronization technique to demonstrate its digital applicability in real-world applications. Furthermore, based on the developed synchronization mechanism, a secure communication scheme was constructed. Finally, the security analysis metric tests were investigated through histograms and spectrograms analysis to confirm the security strength of the employed communication system. Numerical simulations demonstrate the validity and possibility of using this new FOCS in high-level security communication systems. Furthermore, the secure communication system is highly resistant to pirate attacks. A good agreement between simulation and experimental results is obtained, showing that the new FOCS can be used in real-world applications. Full article

Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods. Full article