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Decision tree (DT)-based machine learning (ML) algorithms are one of the preferred solutions for real-time internet traffic classification in terms of their easy implementation on hardware. However, the rapid increase in today's newly developed applications and the resulting diversity in internet traffic greatly increases the size of DTs. Therefore, the tree-based hardware classifiers cannot keep up with this growth in terms of resource usage and classification speed. To alleviate the problem, we propose to group application classes by certain rules and create an individual small DT per each group. In this article, a pipelined organization of multiple DT data structures, called pipelined decision trees, is proposed as a scalable solution to tree-based traffic classification. We also propose two distinct algorithms, namely confusion matrix-based class aggregation and leaf count-based class aggregation algorithms, to set group creation rules that allows traffic classification on pipelined smaller DTs in a hierarchical order. We further designed an hardware engine on field programmable gate arrays, which can search those pipelined trees within a single clock cycle by transforming them into bit vectors and implementing multiple range comparisons in parallel. Our architecture with 12 classes can run in 928.88 giga bit per second and achieve 96.04% accuracy. [ABSTRACT FROM AUTHOR]

Network intrusion detection systems (NIDSs) monitor Internet Protocol (IP) traffic to detect anomalous and malicious activities on a network. Despite the plethora of studies in this field, hardware-based string matching engines that can accommodate the advancements in optical networking technology are still in high demand. Furthermore, memory efficient data structures to store intrusion patterns have recently received a great deal of research attention. In this paper, we introduce a tree-based pattern matching (TPM) scheme that comprises a forest of Binary Search Tree (BST) data structures and an accommodating high-throughput multi-pipelined architecture for scalable string matching on hardware. To improve the resource efficiency in hardware implementations, we enhanced TPM scheme (extended-TPM) with two novel tree structures, namely BST-epsilon ( BST ∊ ) and hierarchical BST (H-BST). Our entire design accomplishes a memory efficiency of 1.07 bytes/char for the latest Snort dictionary. Utilizing a state-of-the-art Field Programmable Gate Arrays (FPGAs), TPM architecture can sustain a throughput of 2.7 Gbps. [ABSTRACT FROM AUTHOR]

Dynamically updateable and memory-efficient search structures for Internet protocol (IP) lookup have lately attracted a great deal of attention from the researchers. In this paper, we focus on the next-generation IPv6 routing protocol comprising large and sparsely distributed routing tables. The existing data structures either suffer from inefficient resource and memory usage (trie-based algorithms), or require complicated construction processes such as converting routing prefixes into their longer representatives and sorting (tree-based algorithms), or both. We propose a novel data structure denoted value-coded trie (VC-trie) for IP lookup. VC-trie provides significant memory saving in comparison with that of the existing solutions in both IPv4 and IPv6 domains. Thereby, our structure can support longer prefix lengths and larger routing tables. We also design an static random access memory (SRAM)-based pipelined architecture to assist the VC-trie structure to improve the throughput. The architecture is implemented utilizing a state-of-the-art field programmable gate array (FPGA) device and sustainable throughput of 448 million lookups per second (with a routing table consisting of 324 K prefixes) is achieved. Furthermore, the architecture can be enhanced with external SRAMs to relax the limitations of the existing FPGA device in on-chip memory. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 − 100% and 81.08 − 100%, respectively. [ABSTRACT FROM AUTHOR]

Highlights: [•] We proposed two compact data structures for Internet Protocol (IP) lookup. [•] The node size variety leads to the memory inefficiency in hardware implementations. [•] Compact Trie Forest (CTF) uses multiple disjoint pipelines to solve the problem. [•] Compact Trie ∊ (CT ∊ ) splits nodes into sequentially connected multiple small nodes. [•] To support each data structure, two pipelined SRAM-based architectures are proposed. [Copyright &y& Elsevier]

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network–based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel high throughput internet protocol (IP) lookup engine, which is built upon a recently proposed multiple pipeline array architecture that has parallel two-dimensional circular search capabilities on intersecting and variable length pipelines. Our new engine is composed of specially designed processing elements (PEs) including dual input/output static random access memory units and bidirectional links, hence allowing search to proceed in all directions and admitting search requests from all PEs at the boundary of the array. We propose a novel data structure called compact clustered trie (CCT), which is better than traditional binary trie in terms of memory requirement and number of memory accesses. We develop novel approaches including a CCT forwarding table construction method, a mapping strategy and a suitable IP lookup algorithm. Our new lookup engine achieves a much higher average case throughput and a much lower average delay compared with existing IP lookup solutions making, for example, an 8 Tbps high-speed router front end possible. The engine is also well suited for the IPv6 addressing scheme. [ABSTRACT FROM PUBLISHER]

Purpose: The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. Methods: A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. Results: The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. Conclusion: The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future. [ABSTRACT FROM AUTHOR]