FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features

Classification of Real and Fake Human Faces Using Deep Learning

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%

Long Term Spectral Statistics for Voice Presentation Attack Detection

Automatic speaker verification systems can be spoofed through recorded, synthetic or voice converted speech of target speakers. To make these systems practically viable, the detection of such attacks, referred to as presentation attacks, is of paramount interest. In that direction, this paper investigates two aspects: (a) a novel approach to detect presentation attacks where, unlike conventional approaches, no speech signal related assumptions are made, rather the attacks are detected by computing first order and second order spectral statistics and feeding them to a classifier, and (b) generalization of the presentation attack detection systems across databases. Our investigations on Interspeech 2015 ASVspoof challenge dataset and AVspoof dataset show that, when compared to the approaches based on conventional short-term spectral processing, the proposed approach with a linear discriminative classifier yields a better system, irrespective of whether the spoofed signal is replayed to the microphone or is directly injected into the system software process. Cross-database investigations show that neither the short-term spectral processing based approaches nor the proposed approach yield systems which are able to generalize across databases or methods of attack. Thus, revealing the difficulty of the problem and the need for further resources and research

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

Selected Computing Research Papers Volume 5 June 2016

An Analysis of Current Computer Assisted Learning Techniques Aimed at Boosting Pass Rate Level and Interactivity of Students (Gilbert Bosilong) ........................................ 1 Evaluating the Ability of Anti-Malware to Overcome Code Obfuscation (Matthew Carson) .................................................................................................................................. 9 Evaluation of Current Research in Machine Learning Techniques Used in Anomaly-Based Network Intrusion Detection (Masego Chibaya) ..................................................... 15 A Critical Evaluation of Current Research on Techniques Aimed at Improving Search Efficiency over Encrypted Cloud Data (Kgosi Dickson) ........................................ 21 A Critical Analysis and Evaluation of Current Research on Credit Card Fraud Detection Methods (Lebogang Otto Gaboitaolelwe) .......................................................... 29 Evaluation of Research in Automatic Detection of Emotion from Facial Expressions (Olorato D. Gaonewe) ......................................................................................................... 35 A Critical Evaluation on Methods of Increasing the Detection Rate of Anti-Malware Software (Thomas Gordon) ................................................................................................ 43 An Evaluation of the Effectiveness of the Advanced Intrusion Detection Systems Utilizing Optimization on System Security Technologies (Carlos Lee) ............................ 49 An Evaluation of Current Research on Data Mining Techniques in Decision Support (Keamogetse Mojapelo) ...................................................................................................... 57 A Critical Investigation of the Cognitive Appeal and Impact of Video Games on Players (Kealeboga Charlie Mokgalo) ................................................................................ 65 Evaluation of Computing Research Aimed at Improving Virtualization Implementation in the Cloud (Keletso King Mooketsane) ................................................. 73 A Critical Evaluation of the Technology Used In Robotic Assisted Surgeries (Botshelo Keletso Mosekiemang) ....................................................................................... 79 An Evaluation of Current Bio-Metric Fingerprint Liveness Detection (George Phillipson) ........................................................................................................................... 85 A Critical Evaluation of Current Research into Malware Detection Using Neural-Network Classification (Tebogo Duduetsang Ramatebele) ................................................ 91 Evaluating Indirect Detection of Obfuscated Malware (Benjamin Stuart Roberts) ......... 101 Evaluation of Current Security Techniques for Online Banking Transactions (Annah Vickerman) ....................................................................................................................... 10