Search Results (8)

Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that supports modern cyber defense, from cryptographic primitives that ensure confidentiality and integrity to behavioral intelligence algorithms that provide predictive security. Classical symmetric and asymmetric schemes such as AES, ChaCha20, RSA, and ECC define the computational backbone of confidentiality and authentication in current systems. Intrusion and anomaly detection mechanisms range from deterministic pattern matchers exemplified by Aho-Corasick and Boyer-Moore to probabilistic inference models such as Markov Chains and HMMs, as well as deep architectures such as CNNs, RNNs, and Autoencoders. Malware forensics combines graph theory, entropy metrics, and symbolic reasoning into a unified diagnostic framework, while network defense employs graph-theoretic algorithms for routing, flow control, and intrusion propagation. Behavioral paradigms such as reinforcement learning, evolutionary computation, and swarm intelligence transform cyber defense from reactive automation to adaptive cognition. Hybrid architectures now merge deterministic computation with distributed learning and explainable inference to create systems that act, reason, and adapt. This review identifies and contextualizes over 50 foundational algorithms, ranging from AES and RSA to LSTMs, graph-based models, and post-quantum cryptography, and redefines them not as passive utilities, but as the cognitive genome of cyber defense: entities that shape, sustain, and evolve resilience within adversarial environments. Full article

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman’s rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer–Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection. Full article

Intraoperative accidental extubation on a known difficult-airway patient requires prompt attention. A good understanding of the steps to re-establish the airway is critical, especially when the patient is known to have a difficult airway documented or discovered on induction or acquires a difficult airway secondary to intraoperative events. The situation becomes even more complicated if the case has been handed off to another anesthesiologist, where specific and detailed information may not have been conveyed. This simulation was designed to train first-year clinical anesthesia residents. It was a 50 min encounter that focused on the management of complete loss of an airway during a thyroidectomy on a known difficult-airway patient. The endotracheal tube dislodgement was simulated by deliberate tube manipulation through the cervical access window of the mannequin. Learners received a formative assessment of their performance during the debrief, and most of the residents met the educational objectives. Learners were asked to complete a survey of their experience, and the feedback was positive and constructive. The response rate was 68% (17/25). Our simulation program helped anesthesiology residents develop intraoperative emergency airway management skills in a safe environment, as well as foster communication skills among anesthesiologists and the surgery team. Full article

Anesthesiologists may encounter multiple obstacles in communication when attempting to collect information for emergency surgeries. Occult tension pneumothorax that was asymptomatic in the emergency department (ED) could become apparent upon positive pressure ventilation and pose a critical threat to the patient intraoperatively. Here, we describe a simulation exercise that was developed as a curriculum module for the Indiana University (IU) Anesthesiology residency program. It is primarily designed for first-year clinical anesthesia residents (CA-1/PGY-2). It is a 50 min encounter with two scenarios. The first scenario focuses on information collection and communication with a non-cooperative patient with multiple distractors. The second scenario focuses on the early diagnosis of tension pneumothorax and subsequent treatment. The residents were given formative feedback and met the educational objectives. Commonly missed critical actions included misdiagnosing the tension pneumothorax as mainstem intubation, bronchospasm, pulmonary thromboembolism, and anaphylaxis. Residents rated the feedback and debriefing as “extremely useful” or “very useful.” Time constraints limit the number of residents who can sit in the “hot seat.” The structure of the mannequin limits the ability to diagnose pneumothorax by auscultation and ultrasound. In the future, the scenarios may also be utilized to educate student anesthesiologist assistants and other non-physician anesthesia learners. Full article

Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm. Full article

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform. Full article

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with $O\left(mk\right)$ as the preprocessing time and $O(nklog\sigma )$ as the matching time, where n, m, k, $\sigma$ , and $\mathit{occ}$ denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice. Full article

We propose a framework for the exact probabilistic analysis of window-based pattern matching algorithms, such as Boyer–Moore, Horspool, Backward DAWG Matching, Backward Oracle Matching, and more. In particular, we develop an algorithm that efficiently computes the distribution of a pattern matching algorithm’s running time cost (such as the number of text character accesses) for any given pattern in a random text model. Text models range from simple uniform models to higher-order Markov models or hidden Markov models (HMMs). Furthermore, we provide an algorithm to compute the exact distribution of differences in running time cost of two pattern matching algorithms. Methodologically, we use extensions of finite automata which we call deterministic arithmetic automata (DAAs) and probabilistic arithmetic automata (PAAs) [1]. Given an algorithm, a pattern, and a text model, a PAA is constructed from which the sought distributions can be derived using dynamic programming. To our knowledge, this is the first time that substring- or suffix-based pattern matching algorithms are analyzed exactly by computing the whole distribution of running time cost. Experimentally, we compare Horspool’s algorithm, Backward DAWG Matching, and Backward Oracle Matching on prototypical patterns of short length and provide statistics on the size of minimal DAAs for these computations. Full article