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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms 4

Unsupervised Insider Threat Detection Using Multi-Head Self-...

引用

4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Bertrand, Simon Germain, Pascal Tawbi, Nadia Laval University Dept. of Computer Science & Software Engineering Québec Canada

ISBN: (纸本)9798350354775

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%. © 2024 IEEE.

关键词： Deep neural networks

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

引用

2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yang, Jiafeng Rusak, Alena Belozubov, Alexander ITMO University Software Engineering and Computer Technology Dept. Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification. © 2024 IEEE.

关键词： Distillation

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Using ChatGPT in Undergraduate computer Science and software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer Science and software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer Science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems 50

DC Fault Detection Using Time Derivative of High-Pass Filter...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Khan, Saad Ahmed Ukil, Abhisek Nair, Nirmal-Kumar C. Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation. © 2024 IEEE.

关键词： Chebyshev filters

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Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks using Reinforcement Learning 35

Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks usi...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Ara, Tarana Liscano, Ramiro Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350362244

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold Q value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization Technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots. © 2024 IEEE.

关键词： Scheduling algorithms

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal 8

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

引用

8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Ince, Cemile Ince, Kenan Hanbay, Davut Inönü University Computer Engineering Dept Malatya Turkey Inönü University Software Engineering Dept. Malatya Turkey

ISBN: (纸本)9798331531492

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version. © 2024 IEEE.

关键词： Pixels

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A Novel Dual Channel Isolated Current Source Gate Driver for Fast Switching MOSFETs 50

A Novel Dual Channel Isolated Current Source Gate Driver for...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Elahi, Asjad Youssef, Mohamed Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology OshawaON Canada

ISBN: (纸本)9781665464543

A new dual-channel discontinuous mode isolated current source gate driver (ICSGD) is proposed in this paper. The proposed gate driver can provide two isolated gate drive signals for two power MOSFETs in circuits where these switches are in sync. The proposed gate driver can achieve very fast turn on and turn off transitions, due to its current mode nature. In addition, this technique can achieve partial gate energy recovery by recovering a fraction of the RMS (CV2) energy, which is typically dissipated over the gate drive resistors in voltage source gate drive (VSGD) techniques. Thus, provides better efficiency. The proposed circuit consists of six transistors and a small pulse transformer to allow swift operation over a wide range of duty cycles. Current through the transformer is discontinuous to avoid conduction losses due to circulating currents such as in some resonant gate drivers (RGD). Consequently, the proposed ICSGD provides superior performance over its VSGD counterparts, especially in high-frequency (HF) operation. The design is implemented in both PSIM and Typhoon HIL (hardware in the loop) platforms. The model is verified with a match of results from PSIM and Typhoon. The PSIM & HIL results show that the ICSGD achieves a minimum of 20% efficiency improvement. © 2024 IEEE.

关键词： High frequency transformers

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A software Framework Based on JUnit for Automated software Testing in computer Science Courses 54

A Software Framework Based on JUnit for Automated Software T...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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