Low False Positive Rate Research Articles

Fatigue Driving Detection Based on Driver Facial Temporal Sequences

Addressing the issues of low real-time performance and high false positive rates in driver fatigue detection methods based on deep learning, this paper proposes a temporal sequence Transformer-based fatigue detection method grounded in the localization of facial landmarks in drivers. Initially, the facial positions are obtained using the single-stage face detection algorithm RetinaFace. Subsequently, a lightweight GM module is designed as the principal feature extraction module for constructing a multi-scale fusion facial landmark detection network, and facial fatigue feature parameters based on temporal sequences are calculated according to the facial landmarks. Finally, a fatigue driving classification method based on temporal sequences Transformers is developed for classifying the sequences of fatigue feature parameters. Experimental results demonstrate that the inference time required for facial detection and landmark detection is merely 16.8 milliseconds, with the per-frame inference time for facial landmark detection being only 2.5 milliseconds, thus fulfilling the real-time requirements of fatigue driving detection during the feature extraction phase. A temporal sequence of fatigue feature parameters built on the NTHU-DDD dataset and the trained temporal sequence Transformer model resulted in an accuracy rate of 91.4% for the proposed method.

Computer-aided urban energy systems cyber attach detection and mitigation: Intelligence hybrid machine learning technique for security enhancement of smart cities

This paper introduces a sophisticated computer-aided system for enhancing the security of smart cities through urban cyber attack detection and mitigation. The proposed system employs a comprehensive mathematical framework that models the dynamics of cyber attacks, detection, and mitigation, capturing the intricate relationships within a smart city environment. Leveraging a hybrid approach combining Transfer Learning and Meta-Learning, the system adapts to evolving threats and tasks, showcasing superior performance in comparison to standalone methods. Simulation results demonstrate the system’s robustness, with low False Positive and False Negative Rates, high System Security, and effective Mitigation Strategies. This research contributes a state-of-the-art solution for securing smart cities, leveraging intelligent hybrid machine learning techniques.

Development of a Real-time Force-based Algorithm for Infusion Failure Detection.

Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumps' occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed. We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumps' onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps. The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds. Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifier's performance in a real-world setting.

Probabilistic Prediction of Gastrointestinal Ischemia after Cardiothoracic Surgery.

Gastrointestinal ischemia (GIisch) is challenging to diagnose in patients after cardiothoracic surgery. Computed tomography angiography (CTA) carries substantial false-negative and false-positive rates. The aim of the study was to evaluate if a combination of readily available variables improves the diagnosis of GIisch after cardiothoracic surgery. This retrospective study included patients receiving intensive care after cardiothoracic surgery. GIisch was confirmed by surgical and/or endoscopic findings. A GIisch prediction score was developed using the Spiegelhalter-Knill-Jones system in a training cohort then tested in a validation cohort (patients without obvious signs of GIisch on CTA). The training cohort comprised 125 consecutive patients with suspected GIisch in 2008 to 2019, including 85 with confirmed GIisch. CTA, performed in 92 patients, had a high false-negative rate of 17/60 (28%) and a lower false-positive rate of 7/32 (22%). The score included cardiopulmonary bypass, negatively associated with GIisch, and six variables positively associated with GIisch: intraoperative mean arterial pressure < 50 mm Hg, aspartate aminotransferase > 15 N, lactate increase in 24 hour > 20%, and 3 CTA findings, namely, bowel dilation, bowel wall thickening, and mesenteric vasoconstriction. The area under the receiver operating characteristic was 0.82 (95% confidence interval [CI], 0.51-0.93) in the training cohort and 0.82 (95% CI, 0.68-0.96) in the validation cohort (n = 34 patients). Reliability of the predicted probabilities was greatest for probabilities ≤ 30% or ≥ 70%. In patients receiving intensive care after cardiothoracic surgery, GIisch cannot be ruled out based solely on CTA findings. A scoring system combining CTA findings with other variables may improve the diagnosis of GIisch in this population.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

Traffic Rules Violation Detection using Dashcam Footage

Traffic Rules Violation Detection using Dashcam Video Footage offers a novel strategy for improving traffic management and road safety through automated violation identification and reporting. The project's goal is to create a video processing system that can use dashcam data to identify and classify frequent traffic infractions. The system can recognize traffic infractions including speeding, lane changes without signaling, helmet recognition, and instances of triple riding on two-wheelers with high accuracy thanks to its integration of YOLO, OpenCV and RESNET50 classifier. The project also includes functionality for user account management and the implementation of a secure user authentication system. Users can examine and validate complete violation reports generated by the system prior to submission. The outcomes show a low false positive rate, rapid real-time processing, and good detection accuracy. Law enforcement organizations have shown. This initiative is an example of how important technical innovation is to solving societal issues and advancing public safety. Key Words: YOLO (You Only Look Once), OpenCV, ResNet50 (Residual Neural Network), Dashcam Video Footage.

Towards Enhanced Security: An improved approach to Phishing Email Detection

Email phishing assaults remain a widespread catastrophe to individuals and businesses, using human weaknesses to obtain unauthorised access to sensitive information. This research study presents an upgraded email phishing detection system that uses machine learning approaches. To construct a robust and adaptable model, the system includes a complete feature set such as email content analysis, sender reputation, and behavioural patterns. The one that has been proposed uses a new combination of supervised and unsupervised machine learning algorithms to analyse email properties and user behaviour, allowing for the detection of subtle phishing signs. The term of body content analysis, URL analysis, and QR code information are among the features retrieved and analysed utilising advanced natural language processing and pattern recognition algorithms. Novelty lies in the integration of supervised and unsupervised machine learning algorithms to identify subtle phishing indicators, along with advanced natural language processing and pattern recognition algorithms for analyzing email properties. A benchmark dataset is used for the proposed system's validation, and a comparison with other phishing detection techniques shows improved, lower false-positive rates. The results indicate the system's ability to effectively discern phishing emails while minimizing the impact on legitimate communication. The system demonstrates a notable improvement by including a diverse range of features and state-of-the-art machine learning algorithms, which makes it a valuable asset to the cybersecurity toolkit for safeguarding email correspondence.

Optimization techniques for IDS‐Generated traffic congestion control in VANET

AbstractVehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

Adversarially robust and real-time DDoS detection and classification framework using AutoML

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%.

Field Validation of a Universally Applicable Condition-Based Maintenance System for Mud Pumps

Summary Although mud pumps are considered to be critical rig equipment, their health monitoring currently still relies on infrequent human observation and monitoring. This approach often fails to detect pump damage at an early stage, resulting in nonproductive time (NPT) and increased well construction costs when initial damage progresses and pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated universally applicable solution to mud pump CBM. The system uses a sensor package that includes acoustic emission sensors and accelerometers in combination with anomaly detection deep learning data analysis to pinpoint any abnormal behavior of the pump and its components. The deep learning models are trained with undamaged normal state data only, and a damage score characterizing the extent of damage to the mud pump is calculated to identify the earliest signs of damage. The system can then generate alerts to notify the rig crew of the damage level of key mud pump components, prompting proactive maintenance actions. Field tests were conducted while drilling an unconventional shale well in west Texas, USA, and a geothermal well in Japan (i.e., two very different drilling operations) to verify the feasibility and general applicability of the developed pump CBM solution. Sensors were attached to pump modules, and data were collected and analyzed using the deep learning models during drilling operations. During the field tests, different hyperparameters and features were compared to select the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The system required only several hours of normal state data for training with no prior pump information. Moreover, it correctly identified the degradation of the pump, swabs, and valves and produced early alerts several hours (in the range of 0.5–17 hours) before actual pump maintenance action was taken by the rig crew. This generally applicable pump CBM system eliminates the environmental, health, and safety concerns that can occur during human-based observations of mud pump health and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system will be a fully self-contained magnetically attachable box containing sensors and a processor, generating simple indicators for recommending proactive pump maintenance tasks when needed.

Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

Cell-Free DNA Screening for Trisomy 21 in Twin Pregnancy: A Large Multicenter Cohort Study

(Abstracted from Am J Obstet Gynecol 2023;229:435.e1–435.e7 Cell-free DNA (cfDNA) screening is an effective way to identify trisomy 21 in singleton pregnancies and has been shown to have both a high detection rate and a low false-positive rate. For twin pregnancies, however, there are challenges relating to fetal fraction and the ability to obtain and distinguish fetal DNA.

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

AbstractWith the increasing availability of biomedical data from multiple platforms of the same patients in clinical research, such as epigenomics, gene expression, and clinical features, there is a growing need for statistical methods that can jointly analyze data from different platforms to provide complementary information for clinical studies. In this paper, we propose a two‐stage hierarchical Bayesian model that integrates high‐dimensional biomedical data from diverse platforms to select biomarkers associated with clinical outcomes of interest. In the first stage, we use Expectation Maximization‐based approach to learn the regulating mechanism between epigenomics (e.g., gene methylation) and gene expression while considering functional gene annotations. In the second stage, we group genes based on the regulating mechanism learned in the first stage. Then, we apply a group‐wise penalty to select genes significantly associated with clinical outcomes while incorporating clinical features. Simulation studies suggest that our model‐based data integration method shows lower false positives in selecting predictive variables compared with existing method. Moreover, real data analysis based on a glioblastoma (GBM) dataset reveals our method's potential to detect genes associated with GBM survival with higher accuracy than the existing method. Moreover, most of the selected biomarkers are crucial in GBM prognosis as confirmed by existing literature.

Deep learning-powered malware detection in cyberspace: a contemporary review

This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

Hybrid semantics-based vulnerability detection incorporating a Temporal Convolutional Network and Self-attention Mechanism

Context:Desirable characteristics in vulnerability-detection (VD) systems (VDSs) include both good detection capability (high accuracy, low false positive rate, low false negative rate, etc.) and low time overheads. The widely used VDSs based on models such as Recurrent Neural Networks (RNNs) have some problems, such as low time efficiency, failing to learn the vulnerability features better, and insufficient amounts of vulnerability features. Therefore, it is very important to construct an automatic detection model with high detection accuracy. Objective:This paper reports on training based on the source code to analyze and learn from the code’s patterns and structures by deep-learning techniques to generate an efficient VD model that does not require manual feature design. Method:We propose a software VD model based on multi-feature fusion and deep neural networks called AIdetectorX-SP. It first uses a Temporal Convolutional Network (TCN) and adds a Self-attention Mechanism (SaM) to the TCN to build a model for extracting vulnerability logic features, then transforms the source code into an image input to a Convolutional Neural Network (CNN) to extract structural and semantic information. Finally, we use feature-fusion technology to design and implement an improved deep-learning-based VDS, called AIdetectorX Sequence with Picturization (AIdetectorX-SP). Results:We report on experiments conducted using publicly-available and widely-used datasets to evaluate the effectiveness of AIdetectorX-SP, with results indicating that AIdetectorX-SP is an effective VDS; that the combination of TCN and SaM can effectively extract vulnerability logic features; and that the pictorial code can extract code structure features, which can further improve the VD capability. Conclusion:In this paper, we propose a novel detection model for software vulnerability based on TCNs, SaM, and software picturization. The proposed model solves some shortcomings and limitations of existing VDSs, and obtains a high software-VD accuracy with a high degree of stability.

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things.

The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%.

Abstract 3097: CETSA HT: Employing rapid high-throughput target engagement technology in primary screening of P53

Abstract The confirmation of target engagement for candidate drugs is a fundamental requisite in drug discovery. A significant portion of projects fail to reach the clinic due to lack of efficacy or failure to show lead candidates interacting with the intended target in a more complex environment. The utility of using the CETSA® (CEllular Thermal Shift Assay) technology to measure target engagement in intact cells is increasingly exploited in high throughput screening, where examples demonstrate low false positive rates in screens of up to 500K compounds. The p53 tumor suppressor protein, a crucial guardian against cancer, functions as a transcription factor that mediates cancer suppression by regulating genes involved in a wide range of cellular processes such as DNA repair, cell cycle and apoptosis. Unfortunately, frequent mutations in the TP53 gene hinder p53's tumor suppressor role in human cancers, thus designating it as a promising target for anticancer therapy. However, attempts to restore p53 function for therapeutic purposes have not yet advanced to clinical approval, highlighting the complexities in leveraging p53 for cancer treatment. Its undruggable nature, attributed to the absence of accessible deep pockets, lack of enzymatic activity, and its intracellular location, further complicates targeted interventions with both low and high molecular weight drugs. Here, we demonstrate the analysis of a high-throughput plate based CETSA® HT screen on the challenging target p53, performed using AlphaLISA® SureFire® Ultra™ technology and our newly developed high-throughput screening platform. Hits identified from this screen are privileged from the onset by binding to the target in a physiologically relevant cellular matrix and in a binding-mode agnostic way. This is in contrast to traditional screening methods targeting activity or in vitro interaction where cellular activity is addressed in later stages. This assay cascade highlights a rapid target-to-hit lead generation strategy placing the emphasis on finding chemical matter that interacts with endogenous target in living cells first, followed by analysis of hit material activity. The ‘shortcut’ to chemical matter active in living cells represents significant potential cost savings in comparison to assay development time on recombinantly produced proteins and/or modified cell lines for traditional HTS screening approaches that still rely on target engagement assessment downstream. Citation Format: Merve Kacal, Tomas Friman, Laurence Arnold, Victoria Brehmer, Daniel Martinez Molina, Stina Lundgren. CETSA HT: Employing rapid high-throughput target engagement technology in primary screening of P53 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3097.

Identification and analysis of stochastic deception attacks on cyber–physical systems

Cyber–Physical Systems (CPSs) refer to control systems which are composed of sensors, actuators, computers and network components. These systems are vulnerable to unforeseen failures and external malicious attacks. In this paper, we analyze the stability of CPSs under stochastic deception attacks. To this end: (i) we propose a statistical Intrusion Detection System (IDS) for detection of deception attacks in CPSs; (ii) identify the place of such attacks by taking advantage of a novel cryptographic adversarial model; (iii) based on the real-time data and characterizing the deception attacks by IDS, we analyze the effect of both adaptive and blind/random types of deception attacks on the stability of CPSs. We do this through Markov chain modeling and subsequently extract the sufficient stability conditions. (iv) as an extra effort, introduce a new adaptive and intelligent deception attack. We validate our findings by illustrative examples at the end of the paper. Our results show that proposed IDS can detect deception attacks with low false positive and negative rates in real time. The results also confirm the validity of the theoretically-predicted stability conditions.