COMXAI A tool to explain AI USING FAULT LOCATION
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This document discusses a tool called COMXAI that uses combinatorial testing methods to help explain the decisions made by artificial intelligence and machine learning models. COMXAI is able to narrow down combinations of input factors that are responsible for certain model outcomes, which can then be presented as understandable rules. Some limitations of this approach are that it may generate very large rulesets with many input combinations, and it is vulnerable to overfitting issues in the underlying models. The authors propose future work to address these limitations, as well as improving the user interface and security of the tool.
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Multi level ransomware analysis MALCON 2019 conference
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malware. In this work, we proposed a multi-level big data mining framework combining Reverse engineering, Natural Language Processing(NLP) and Machine Learning(ML) approaches. The framework analyzes the ransomware at different levels (i.e., Dynamic link library, function call and assembly instruction level) via different supervised ML algorithms. Apache Spark was employed for faster processing of large generated feature set. Portable Executable (PE) parser and Objectdump tool of Linux system were used to get the raw data from the ransomware and normal binaries that were processed further using our custom-built NLP processing. The n-gram probabilities, term-frequency and inverse document frequency (TF-IDF) were used to generate the final feature sets. Experiments were performed with different ‘N’ values of n-gram language model that shows that the ransomware detection accuracy is inversely proportional to the value of N. Among the five chosen supervised classifiers, Logistic regression outperformed others with a detection rate of 98.59% for generated TF-IDFs trigrams at combined multi-level, which is an improved accuracy compared to individual levels.
Time expired ledger for File access blockchain
The document discusses multi-user authentication and access policies. It describes existing authentication methods like passwords and biometrics. Existing access policies like DAC, MAC, RBAC and ABAC are also outlined. The document then proposes a multi-user approval based authentication system where a user's identity must be authenticated by other users. It also proposes a multi-user permission based access policy that requires other users to approve access requests. Limitations of current systems are discussed and alternatives like blockchain, InterPlanetary File System (IPFS) and a proposed decentralized multi-user model are presented.
BigData Computing For WebSite Classifier
This document discusses classifying websites as malicious or benign using machine learning models. It presents previous work that achieved 97-98% accuracy using support vector machines and logistic regression. The document then proposes using big data techniques like Apache Spark for faster processing of large datasets with many URLs and features. It describes preprocessing data by replacing high-cardinality features with numeric values. Experiments show training decision trees, random forests, and neural networks on 1782 URLs and 20 features. Processing times are faster with PySpark compared to without it, especially as the number of URLs increases. Future work includes testing on clusters, more algorithms, more features, and test reports.
Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
This paper evaluates the performance of Apache Hadoop and Apache Spark for log file analysis on cloud computing systems. The paper presents 6 experimental scenarios analyzing Apache HTTP server log files, including measuring request trends, popular requests, detecting distributed denial of service attacks, and error analysis. The experiments found that Spark performed the analyses faster than Hadoop in most cases but Hadoop was more efficient in terms of energy consumption. The paper concludes that Hadoop and Spark can both be useful for log file analysis on cloud systems, with Spark preferred for time performance and Hadoop preferred when considering energy costs.
Stylometry In Authentication
“There is an unconscious aspect to an author’s style that cannot be consciously manipulated but which possesses quantifiable and distinctive features”
Plagiarism Checker.
Forensic analysis.
Author Identification.
Software Code analysis.
Genre classification.
Historical Study.
Context Understanding
Music Lyrics
Character Based Feature
Lexical Feature
Syntactic Feature
Content Specific Feature
Readability Feature
Information Retrieval feature
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
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Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
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This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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COMXAI A tool to explain AI USING FAULT LOCATION
- 1. An Application of Combinatorial Methods for Explainability in Artificial Intelligence and Machine Learning D. RICHARD KUHN C O M P U T E R S E C U R I T Y D I V I S I O N I N F O R M A T I O N T E C H N O L O G Y L A B O R A T O R Y RAGHU N. KACKER C O M P U T A T I O N A L A N D A P P L I E D M A T H D I V I S I O N I N F O R M A T I O N T E C H N O L O G Y L A B O R A T O R Y
- 2. Tradeoff between AI accuracy and explainability CONVOLUTIONAL NEURAL NETS (CNNS), PROVIDE NO EXPLANATIONS UNDERSTANDABLE METHODS, SUCH AS RULE-BASED, TEND TO BE LESS ACCURATE
- 3. COMXAI A TOOL TO EXPLAIN AI USING FAULT LOCATION
- 4. Fault Location • IF A COMBINATION OF FACTOR VALUES OCCURS IN A PASSING TEST, THEN CLEARLY IT DID NOT TRIGGER THE FAILURE. • COMBINATIONS THAT OCCUR ONLY IN FAILING TESTS ARE THOSE THAT ARE CONSIDERED IN NARROWING DOWN THE SET OF SUSPECT COMBINATIONS
- 5. COMXAI A COMBINATORIAL TESTING TOOL Explain why below Traits are REPTILE class
- 6. Single Value Fail observations NO SINGLE TRAIT IS SUFFICIENT AS ALL ARE MORE THAN 44% SHARED WITH REPTILES
- 7. Two Value Fail observations NO PAIR IS SUFFICIENT AS ALL ARE MORE THAN 2% SHARED WITH REPTILES
- 8. Three Value Fail observations 3-WAY COMBINATIONS WILL DISTINGUISH THIS ANIMAL FROM OTHER ANIMAL TYPES IN THIS DATABASE.
- 10. Final Output UNDERSTANDABLE RULE
- 11. Limitations 1. THOUSAND OF COMBINATIONS CAN MAKE RULE SIZE VERY LARGE 2. OVERFITTING, IN WHICH A LEARNING MODEL INCORPORATED NOISE VARIATIONS FROM THE TRAINING DATA WILL CREATE PROBLEM FOR THIS TOOL.
- 12. Future Work • DETECT OVERFITTING • IMPROVED UI FOR BETTER VALIDATION OF AI MODELS • SECURITY FROM MALWARE NEURAL NET