Computational Intelligence Paradigms

The evolution of communication networks and information systems, to support wireless access, cloud and grid computing, and big data, provides great business opportunities. However, it also generates a new trend of sophisticated network threats and offers several challenges in securing information and systems confidentiality, integrity and availability. The traditional techniques used by security experts are mostly static and lack the much needed characteristics of adaptation and self-organization, computational efficiency and error resilience to deal with evolving attacks. The inherent characteristics of computational intelligence (CI) paradigms provide a promising alternative that has gained popularity resulting in significant applications in information security. There is a plethora of CI paradigms commonly used in this domain including artificial neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. This chapter provides an overview of the widely-recognized CI paradigms and shades the light on some of their potential applications in information security.

Randomness has stimulated mankind's attention and imagination ever since we began to observe the behavior of nature. It was by understanding the randomness and patterns that humans learned to control crops, for example, which led to the creation of the first communities. In the modern world, with the aim of mimicking the randomness of natural phenomena, computers are used to generate sequences that look as random as possible running pseudo-random number generators (PRNG). PRNGs have diverse applications in information security, digital games, simulations, modeling, gambling, arts, among others. Despite this fact, existing methods of randomness measure do not offer a definitive solution to the need of classifying PRNGs. The main methods of randomness measurement consist of statistical tests through which the sequences generated by the algorithms are analyzed. Once PRNGs are analyzed by these test suits, they are considered (un)satisfactory random. This paper explores an aspect that has been neglected in statistical tests: the spatial distribution of pseudo-random sequences. It is conjectured that this distribution is the source of undisclosed patterns in test suites. One way to study this arrangement more depth is using models that explore the relation of values and iterations. The relation of elements in space has to be the basis of the paradigm. This work applies theory of graphs and computational geometry methods to find patterns in pseudo-random sequences. The analyzed sequences are plotted in a Cartesian plan generating a set of points that are converted into graphs considering the Euclidean distance between the points within a radius. The best combination of descriptors formed by measurements of graphs and geometric properties is selected. When patterns emerge, one can point out flaws in the widely used methods for measuring PRNGs quality. It is intended to suggest a complementary approach for the evaluation of PRNGs, contributing to a better classification of the PRNGs and, consequently, to cause improvements in the studies on information security. This includes identifying patterns in sequences generated by algorithms that are considered pseudo-random by current statistical tests and thus identifying the limitations of these assessments.

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.

Optimization problems arise in various fields of science, engineering, and industry. In many occasions, such optimization problems, particularly in the present scenario, involve a variety of decision variables and complex structured objectives, and constraints. Often, the classical or traditional optimization techniques face difficulty in solving such real world optimization problems in their original form. Due to deficiencies of classical optimization algorithms in solving large-scale, highly non-linear, and often non-differentiable problems, there is a need to develop efficient and robust computational algorithms, which can solve problems, numerically irrespective of their sizes. Taking inspiration from nature to develop computationally efficient algorithms is one way to deal with real world optimization problems. Broadly, one can put these algorithms in the field of computational sciences and in particular, to computational intelligence. Formally, computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to solve complex real world problems. The major constituents of CI are Fuzzy Systems (FS), Neural Networks (NN), and Swarm Intelligence (SI) and Evolutionary Computation (EC). Computational intelligence techniques are powerful, efficient, flexible, and reliable. Swarm Intelligence and Evolutionary Computation are two very useful components of computational intelligence that are primarily used to solve optimization problems. This book primarily concerns with various swarm and evolutionary optimization algorithms. This chapter provides a brief introduction to swarm and evolutionary algorithms.

Tree data analysis has many applications in information security. In particular, HTML pages' DOM trees are an important target of analysis because web pages can be vectors for, and targets of, major cyberattacks like phishing. Previous attempts to incorporate tree data analysis into security applications, however, have been hampered by the lack of efficient methods for tree data analysis in machine learning. As such, most security research has focused on data representable as vectors of real numbers, like most machine learning work. Recent work, however, has yielded several efficiency break-throughs in tree analysis. One example is kernel methods, a methodological bridge that fills the gap between discretely-structured data (like trees) and multivariate analysis. Kernel methods enable applying a variety of multivariate analysis techniques such as SVM and PCA to trees. The method we are interested in is the subpath kernel. The subpath kernel offers the following advantages: (1) it is invariant over ordered and unordered trees; (2) it can be computed using an extremely fast linear-time algorithm compared to the quadratic time required to compute values of most tree kernels; (3) its excellent prediction accuracy has been proven through intensive experiments. This paper proposes a subpath kernel-based method for tree-structured security data. To demonstrate the effectiveness of our method, we apply it to the problem of detecting fake e-commerce sites, a sub-problem of phishing detection with a significant real-world financial cost. In an experiment on a real dataset of fake sites provided by a major e-commerce company, our method exhibited accuracy as high as 0.998 when training SVM with as few as 1,000 instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reaches 0.996. While previous phishing detection methods relied on textual content, URL components, and blacklists, our approach is the first to leverage DOM trees, which makes it both more effective and more robust against adversarial attacks. Unlike URL or content changes, changing a page's DOM structure incurs large costs to criminals.

Gallium-doped MXene/cellulose nanofiber composite membranes with electro/photo thermal conversion property for high performance electromagnetic interference shielding

The papers in this special section focus on security in computer architectures. Computer architectures are profoundly affected by a new security landscape, caused by the dramatic evolution of information technology over the past decade. First, secure computer architectures have to support a wide range of security applications that extend well beyond the desktop environment, and that also include handheld, mobile, and embedded architectures, as well as high-end computing servers. Second, secure computer architectures have to support new applications of information security and privacy, as well as new information security standards. Third, secure computer architectures have to be protected and be tamperresistant at multiple abstraction levels, covering network, software, and hardware. This Special Section in Transactions on Computers aims to capture this evolving landscape of secure computing architectures, to build a vision of opportunities and unresolved challenges.

Achieving high‐temperature‐resistant afterglow is highly desirable yet challenging, primarily due to exciton deactivation at elevated temperatures. In this work, a co‐pyrolysis strategy was proposed to achieve such afterglow by modulating the emission dynamics of organic molecules through introducing of structural defects. These defects facilitate exciton transfer to guest molecules, extending and balancing the lifetimes of both thermally activated delayed fluorescence and phosphorescence processes. The structural defects also promote thermoluminescence, wherein elevated temperatures trigger the release of trapped electrons to the guest molecules, thereby contributing significantly to the exceptional high‐temperature afterglow stability. Additionally, the pyrolysis process generates ammelide, which protects excitons from deactivation. These synergistic effects yield afterglow materials exhibiting a photoluminescence quantum yield of 59.9% and an emission lifetime of 3.5 s. Remarkably, intense afterglow emission persists from 25 to 150°C with negligible changes in emission color, intensity, and decay kinetics. These findings provide valuable insights for modulating the emission dynamics of afterglow materials, suggesting promising applications in information security, biomedical diagnosis, and chemical sensing.

Low‐dimensional metal halides have emerged as promising anti‐counterfeiting materials. However, achieving a multi‐mode and multi‐color anti‐counterfeiting system in metal halides remains challenging. In this study, copper‐halide (TBP)2Cu4Br6 (TBP+ = C16H36P+) single crystals are synthesized using a cooling crystallization method, which exhibits efficient dual‐band emissions (542 and 708 nm), large Stokes shifts (282 and 330 nm), and a high photoluminescence quantum yield (PLQY) of 92.7% for 542 nm. These exceptional properties are attributed to the unique 0D structure of (TBP)2Cu4Br6 single crystals, which facilitates the formation of two different self‐trapped excitons (STEs). Furthermore, based on (TBP)2Cu4Br6, a multi‐mode and multi‐color digital anti‐counterfeiting system integrated is designed with Morse code information encryption, demonstrating promising applications in information security and anti‐counterfeiting. This work not only illustrates an emitter in copper halides but also paves the way for achieving multi‐mode and multi‐color anti‐counterfeiting systems.

Information security and temperature sensing play vital roles in various areas such as financial transactions, product authentication, and privacy protection. However, integrating precise control in information security technologies with highly sensitive temperature sensing remains a significant challenge. Here, a multilayer core–shell upconversion nanoparticle (UCNP) system is presented that enables dynamic dual‐color emission switching and highly sensitive temperature sensing through controlled energy transfer mechanisms. By tuning the excitation wavelength, power density, and pulse width, the system exhibits a green‐to‐blue emission transition with multimodal tunability. Using fluorescence intensity ratio techniques, maximum relative sensitivity of 1.85% K−1, demonstrating superior temperature detection performance is achieved. Furthermore, the tunable multicolor emission properties of UCNPs enable diverse applications in information security, including dynamic pattern recognition, Morse code encryption, and quick response code‐based anti‐counterfeiting. This research not only broadens the application scope of UCNPs in security and environmental monitoring but also offers perspectives on the design of multifunctional optical materials.

A dual-stimuli-responsive intelligent layered lanthanide hydroxide for application in information security and latent fingerprint identification

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

The Government and industry are currently using information and communication technology a lot. The Government and companies in Indonesia have regulations on operational standards and management procedures for information and communication systems security at Government regarding implementing information system security. Information security is essential because there are many threats to information security. Information security in this era of Information and Communication Technology (ICT) is fundamental. Information Exchange Environment (IEE) vulnerabilities have increased as threats become more widespread and complex; therefore, information security has become a fundamental problem for businesses, organizations, and governments. So, in this paper, we will explain the review of the impact of Information Security For Government and companies in terms of threats and types of information security. Some security system methods are analyzed, among others—application Security, cloud security, Cryptography, security infrastructure, Incident response, and vulnerability management. The results of this review analysis, it known threat has several solutions, each depending on the type of threat for both the Government and the Company. Furthermore, Information Security is considered very important, especially for data stored in Government and companies.

The problem of exponentiation over a finite field is to compute A e for a field element A and a positive integer e. This problem has many useful applications in cryptography and information security. In this paper, we present an efficient exponentiation algorithm in optimal extension field (OEF) GF(p m ), which uses the fact that the Frobenius map, i.e., the p-th powering operation is very efficient in OEFs. Our analysis shows that the new algorithm is twice as fast as the conventional square-and-multiply exponentiation. One of the important applications of our new algorithm is random generation of a base point for elliptic curve cryptography, which is an attractive public-key mechanism for resource-constrained devices. We present a further optimized exponentiation algorithm for this application. Our experimental results show that the new technique accelerates the generation process by factors of 1.62–6.55 over various practical elliptic curves.

Applications and Techniques in Information Security