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1. Spammer Detection via Combined Neural Network

   https://doi.org/10.1007/978-3-319-96136-1_28  

   Pei, Weiping; Xie, Youye; Tang, Gongguo (July 2018, Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science)

   Social networks, as an indispensable part of our daily lives, provide ideal platforms for entertainment and communication. However, the appearance of spammers who spread malicious information pollutes a network’s reliability. Unlike email spammers detection, a social network account has several types of attributes and complicated behavior patterns, which require a more sophisticated detection mechanism. To address the above challenges, we propose several efficient profiles and behavioral features to describe a social network account and a combined neural network to detect the spammers. The combined neural network can process the features separately based on their mutual correlation and handle data with missing features. In experiments, the combined neural network outperforms several classical machine learning approaches and achieves 97.5% accuracy on real data. The proposed features and the combined neural network have already been applied commercially. 

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2. Real-Time Posture Classification on Resource-Constrained Runtime Environments: A Comparative Study of Traditional and Deep Learning Models

   https://doi.org/10.1109/SoutheastCon56624.2025.10971620  

   Shejuti, Zarin; Fuad, Muztaba (March 2025, IEEE)

   While machine learning models perform well on offline data, assessing their performance in real-world, resource-constrained environments-considering accuracy, prediction time, power consumption, and memory usage-is crucial for practical applications. This research implements a mobile-based Human Activity Recognition solution to classify three postures-sitting, standing, and walking-using smartphone sensors, specifically accelerometer, gyroscope, and magnetometer. Time-domain features extracted from these sensors were used, with Random Forest employed for feature selection. One traditional machine learning model, Logistic Regression, and one deep learning model, Convolutional Neural Network, were trained and deployed via an Android application for real-time evaluation. While the Convolutional Neural Network achieved higher accuracy and better memory efficiency, Logistic Regression demonstrated faster prediction times during real-time use. Both models showed reduced accuracy for standing and walking postures in real-world conditions, emphasizing the challenges of deploying machine learning models in dynamic environments. This study highlights the importance of evaluating machine learning models in real-world settings to ensure reliability and efficiency, particularly in resource-constrained environments. 

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3. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging

   https://doi.org/10.3390/cancers15051548  

   Joel, Marina Z.; Avesta, Arman; Yang, Daniel X.; Zhou, Jian-Ge; Omuro, Antonio; Herbst, Roy S.; Krumholz, Harlan M.; Aneja, Sanjay (March 2023, Cancers)

   Deep learning (DL) models have demonstrated state-of-the-art performance in the classification of diagnostic imaging in oncology. However, DL models for medical images can be compromised by adversarial images, where pixel values of input images are manipulated to deceive the DL model. To address this limitation, our study investigates the detectability of adversarial images in oncology using multiple detection schemes. Experiments were conducted on thoracic computed tomography (CT) scans, mammography, and brain magnetic resonance imaging (MRI). For each dataset we trained a convolutional neural network to classify the presence or absence of malignancy. We trained five DL and machine learning (ML)-based detection models and tested their performance in detecting adversarial images. Adversarial images generated using projected gradient descent (PGD) with a perturbation size of 0.004 were detected by the ResNet detection model with an accuracy of 100% for CT, 100% for mammogram, and 90.0% for MRI. Overall, adversarial images were detected with high accuracy in settings where adversarial perturbation was above set thresholds. Adversarial detection should be considered alongside adversarial training as a defense technique to protect DL models for cancer imaging classification from the threat of adversarial images. 

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4. Euler : Detecting Network Lateral Movement via Scalable Temporal Link Prediction

   https://doi.org/10.1145/3588771  

   King, Isaiah J.; Huang, H. Howie (August 2023, ACM Transactions on Privacy and Security)

   Lateral movement is a key stage of system compromise used by advanced persistent threats. Detecting it is no simple task. When network host logs are abstracted into discrete temporal graphs, the problem can be reframed as anomalous edge detection in an evolving network. Research in modern deep graph learning techniques has produced many creative and complicated models for this task. However, as is the case in many machine learning fields, the generality of models is of paramount importance for accuracy and scalability during training and inference. In this article, we propose a formalized approach to this problem with a framework we call Euler . It consists of a model-agnostic graph neural network stacked upon a model-agnostic sequence encoding layer such as a recurrent neural network. Models built according to the Euler framework can easily distribute their graph convolutional layers across multiple machines for large performance improvements. Additionally, we demonstrate that Euler -based models are as good, or better, than every state-of-the-art approach to anomalous link detection and prediction that we tested. As anomaly-based intrusion detection systems, our models efficiently identified anomalous connections between entities with high precision and outperformed all other unsupervised techniques for anomalous lateral movement detection. Additionally, we show that as a piece of a larger anomaly detection pipeline, Euler models perform well enough for use in real-world systems. With more advanced, yet still lightweight, alerting mechanisms ingesting the embeddings produced by Euler models, precision is boosted from 0.243, to 0.986 on real-world network traffic. 

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5. Large Language Model can Reduce the Necessity of Using Large Data Samples for Training Models

   https://doi.org/10.1109/CAI64502.2025.00173  

   Rahman, Md Abdur; Francia, Guillermo; Shahriar, Hossain; Ahamed, Sheikh Iqbal (May 2025, IEEE)

   This work introduces an novel approach to improving cybersecurity systems to focus on spam email-based cyberattacks. The proposed technique tackles the challenge of training Machine Learning (ML) models with limited data samples by leveraging Bidirectional Encoder Representations from Transformers (BERT) for contextualized embeddings. Unlike traditional embedding methods, BERT offers a nuanced representation of smaller datasets, enabling more effective ML model training. The methodology will use several pre-trained BERT models for generating contextualized embeddings using data samples, and these embeddings will be fed to various ML algorithms for effective training. This approach demonstrates that even with scarce data, BERT embeddings significantly enhance model performance compared to conventional embedding approaches like Word2Vec. The technique proves especially advantageous for insufficient instances of high-quality dataset. The result of this proposed work outperforms traditional techniques to mitigate phishing attacks with few data samples. This work provides a robust accuracy of 99.25% when we use multilingual BERT (M-BERT) to embed dataset. 

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