Semantics aware malware detection ppt
•Download as PPTX, PDF•
1 like•2,071 views
This document presents a technique for semantics-aware malware detection. It aims to design a malware detection algorithm that uses the semantics of instructions to identify malware even after it has been obfuscated. The technique specifies malicious behaviors as templates containing instructions, variables, and symbolic constants. It then uses a formal semantics approach and translation-validation to determine if programs are semantically equivalent and discover malicious programs despite polymorphism or metamorphism techniques used by malware writers. The strengths are robustness to code changes, while limitations include challenges with certain obfuscation techniques.
Report
Share
Report
Share
Recommended
Malware Detection - A Machine Learning Perspective
This document discusses machine learning approaches for malware detection. It notes that millions of new malware are created each year, making it difficult for signature-based antivirus software to keep up. Machine learning is presented as a potential solution by automatically constructing models to detect malware based on training data. However, the quality of the training data and features is critical, as machine learning risks producing garbage outputs from garbage inputs. Different machine learning algorithms and evaluation benchmarks are also discussed.
Data mining techniques for malware detection.pptx
This document discusses data mining techniques for malware detection. It introduces the concepts of data mining, describes common types of malware like viruses, worms and trojans, and covers techniques for malware detection including anomaly-based detection, signature-based detection and clustering algorithms like k-means. Applications of data mining are also discussed along with potential advantages and disadvantages.
Malware classification and detection
The document discusses automatic malware clustering and detection. It covers the current state of antivirus classification, which relies primarily on signature-based methods. Automatic malware clustering aims to recognize known malware to filter it out and focus on new threats. The clustering process typically involves malware analysis, feature extraction, and clustering algorithms. Inconsistent labeling of malware families by different antivirus vendors poses challenges. The document advocates improving classification by describing the full malware lifecycle.
Malware Classification and Analysis
The document discusses malware analysis using machine learning. It proposes collecting malware binaries from online sources and using Cuckoo Sandbox to analyze their behavior dynamically. Features would be extracted from the analysis reports and used to classify the malware into families using machine learning algorithms. The goal is to develop an automated malware classification system that can identify both known and unknown malware types.
Malware Classification Using Structured Control Flow
This document summarizes a system for classifying malware using control flow graph signatures. It discusses:
1) Using entropy analysis to identify and unpack packed malware through application-level emulation.
2) Generating control flow graph signatures using a "structuring" technique and calculating similarities to signatures in a malware database.
3) Evaluating the system on real malware, showing high similarities between variants and low similarities between unrelated programs.
Malware classification using Machine Learning
Uses examples from book titled "Malware Data Science" to explain how AV companies use Machine learning to identify malware. Also, refers to open-source project "Ember" which provides a data set and python code to train and classify malware.
Adversarial machine learning for av software
Introduce practical guidances for developing adversarial machine model for anti-malware software. I didn't use reinforcement model yet, just proof-of-concept. If you have any questions about my work, email to me :)
nababora@naver.com
Malware Dectection Using Machine learning
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
Recommended
Malware Detection - A Machine Learning Perspective
This document discusses machine learning approaches for malware detection. It notes that millions of new malware are created each year, making it difficult for signature-based antivirus software to keep up. Machine learning is presented as a potential solution by automatically constructing models to detect malware based on training data. However, the quality of the training data and features is critical, as machine learning risks producing garbage outputs from garbage inputs. Different machine learning algorithms and evaluation benchmarks are also discussed.
Data mining techniques for malware detection.pptx
This document discusses data mining techniques for malware detection. It introduces the concepts of data mining, describes common types of malware like viruses, worms and trojans, and covers techniques for malware detection including anomaly-based detection, signature-based detection and clustering algorithms like k-means. Applications of data mining are also discussed along with potential advantages and disadvantages.
Malware classification and detection
The document discusses automatic malware clustering and detection. It covers the current state of antivirus classification, which relies primarily on signature-based methods. Automatic malware clustering aims to recognize known malware to filter it out and focus on new threats. The clustering process typically involves malware analysis, feature extraction, and clustering algorithms. Inconsistent labeling of malware families by different antivirus vendors poses challenges. The document advocates improving classification by describing the full malware lifecycle.
Malware Classification and Analysis
The document discusses malware analysis using machine learning. It proposes collecting malware binaries from online sources and using Cuckoo Sandbox to analyze their behavior dynamically. Features would be extracted from the analysis reports and used to classify the malware into families using machine learning algorithms. The goal is to develop an automated malware classification system that can identify both known and unknown malware types.
Malware Classification Using Structured Control Flow
This document summarizes a system for classifying malware using control flow graph signatures. It discusses:
1) Using entropy analysis to identify and unpack packed malware through application-level emulation.
2) Generating control flow graph signatures using a "structuring" technique and calculating similarities to signatures in a malware database.
3) Evaluating the system on real malware, showing high similarities between variants and low similarities between unrelated programs.
Malware classification using Machine Learning
Uses examples from book titled "Malware Data Science" to explain how AV companies use Machine learning to identify malware. Also, refers to open-source project "Ember" which provides a data set and python code to train and classify malware.
Adversarial machine learning for av software
Introduce practical guidances for developing adversarial machine model for anti-malware software. I didn't use reinforcement model yet, just proof-of-concept. If you have any questions about my work, email to me :)
nababora@naver.com
Malware Dectection Using Machine learning
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
Malware detection-using-machine-learning
This document discusses using machine learning and deep learning for malware detection. It notes that over 350,000 new malware are created daily, posing a significant threat. Traditional signature-based detection has limitations in detecting new malware. The document reviews research applying machine learning and deep learning techniques to malware detection using static and dynamic analysis of features. It then describes the authors' approach of using opcode frequency models with random forest and neural networks to classify files, achieving 97-98% precision and recall on a test set. The conclusion is that machine learning and deep learning can help address limitations of traditional approaches by enabling detection of new malware.
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...
В докладе речь пойдёт о применении алгоритмов машинного обучения для обнаружения вредоносных приложений для Android. Я расскажу, как на базе Матрикснета в Яндексе был спроектирован высокопроизводительный инструмент для решения этой задачи. А также продемонстрирую, в каких случаях аналитические методы выявления вредоносного ПО помогают блокировать множество простых образцов вирусного кода. Затем мы поговорим о том, как можно усовершенствовать такие методы для обнаружения более хитроумных вредных программ.
Machine Learning for Malware Classification and Clustering
1) Machine learning can be used as a replacement for antivirus software by using statistical techniques to learn patterns from large malware datasets.
2) Boosted decision trees are well-suited for malware classification because they perform like a game of 20 questions to maximize discrimination between malware and benign classes.
3) Features used in machine learning models require a balance between complexity, which provides more information but less explainability, and explainability, which provides insights to analysts but may not help classification.
Malware Detection using Machine Learning
This document discusses using machine learning for malware detection. It begins with an introduction to machine learning and feature selection for classification problems. Popular supervised learning algorithms that can be used for malware detection are discussed, including K-Nearest Neighbors (KNN), K-Means clustering, Support Vector Machines (SVM), Decision Trees, and Random Forests. Features that could be used for malware detection, such as file size, entropy, and behavioral patterns, are also outlined. The document concludes with brief mentions of neural networks, deep learning, and Python libraries that can be used like Scikit-Learn.
An Introduction to Malware Classification
With more than 1 million new pieces of malware released every day, security vendors are turning toward machine learning to automate threat detection. This talk aims to give new researchers the background they need for contributing to this field. We'll talk about sources for malicious PE files, consistently top-performing machine learning algorithms, extracting features, and how to prevent overfitting. (20 minute)
The Future of Automated Malware Generation
The document discusses the current and future states of automated malware generation and malware defense techniques. It describes how malware distribution networks currently work and trends showing rising malware samples. The future of malware defense is proposed to apply more machine learning and statistical techniques to model malware behaviors and attributes in order to handle growing sample volumes. This would involve training machine learning classifiers on features identified by human experts to classify and cluster malware more effectively.
Fighting advanced malware using machine learning (English)
In this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
Automated In-memory Malware/Rootkit Detection via Binary Analysis and Machin...
Discussion and demonstration of an automated approach
for pairing Memory Forensics with Binary Analysis and
Machine Learning to analyze the execution behavior of
binaries collected from a set of hosts to detect advanced
persistent threats (APT)s that may evade detection by
hooking and "traditional" emulation.
Android Malware Analysis
This document analyzes the "machine.apk" Android malware sample using static and dynamic analysis techniques. The summary includes:
1. Static analysis is performed using tools like apktool, dex2jar, and jd-gui to disassemble the app and examine its AndroidManifest.xml and Java source code. This reveals the malware requests dangerous permissions like location, SMS/calls, and implements tapping and tracking services.
2. Dynamic analysis with CopperDroid and Anubis is able to monitor the app's network traffic and activity timeline. The analysis finds the app extracts the phone number and operator, sends this data to Thailand, can make automatic calls, and tracks the device location.
3.
Metamorphic Malware Analysis and Detection
ABSTRACT :
--------------------
Modern malware that are metamorphic or polymorphic in nature mutate their code by employing code obfuscation and encryption methods to thwart detection. Thus, conventional signature based scanners fail to detect these malware. In order to address the problems of detecting known variants of metamorphic malware, we propose a method using bioinformatics techniques effectively used for Protein and DNA matching. Instead of using exact signature matching methods, more sophisticated signature(s) are extracted using multiple sequence alignment (MSA). The results show that the proposed method is capable of identifying malware variants with minimum false alarms and misses. Also, the detection rate achieved with our proposed method is better compared to commercial antivirus products used in the study.
Status:
----------
This work has been accepted by 8th IEEE International Conference on Innovations in Information Technology (Innovations'12).
Link:
-------
http://ieeexplore.ieee.org/xpl/login.jsp?reload=true&tp=&arnumber=6207739&url=http://ieeexplore.ieee.org/iel5/6203543/6207707/06207739.pdf?arnumber=6207739
e-mail: grijesh.mnit@gmail.com
Malware Detection Using Machine Learning Techniques
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
Vulnerability and Exploit Trends: Combining behavioral analysis and OS defens...
Despite the best efforts of the security community—and big claims from security vendors—large areas of vulnerabilities and exploits remain to be leveraged by adversaries.You will learn about:
- A new perspective on the current state of software flaws.
- The wide margin between disclosed vulnerabilities and
public exploits including a historical analysis and
trending patterns.
- Effective countermeasures that can be deployed to
detect, and prevent, the exploitation of vulnerabilities.
- The limitations of Operating System provided mitigations,
and how a combination of increased countermeasures
with behavioral analysis will get defenders closer to
preventing the largest number of threats.
Nguyen Huu Trung - Building a web vulnerability scanner - From a hacker’s view
This document discusses building a high performance web application vulnerability scanner. It begins with an introduction of the speaker and agenda. It then defines what a WAVS is and why they are needed for both penetration testers and businesses to discover vulnerabilities. The document discusses why building your own WAVS is typically not recommended and reviews common challenges. It proposes an architecture with core and plugin components and discusses approaches like crawling and fuzzing, CPE and CVE mapping, and public exploit testing. Recommendations are provided around programming languages, code design patterns, and challenges like JavaScript crawling, high overhead, false positives, and other considerations.
Setup Your Personal Malware Lab
This document discusses how to set up a malware analysis lab. It begins by introducing malware analysis and explaining why it is important. It then defines a malware lab as a safe, isolated environment for analyzing malware, noting both physical and virtual options. The document outlines the steps to build a malware lab, including isolating systems, installing behavioral and code analysis tools, and using online analysis tools. Finally, it lists specific tools that could be included in a malware lab, such as honeypots, behavioral monitors, disassemblers, sandboxes, and online scanners.
Detecting Evasive Malware in Sandbox
This presentation talk about some of the challenges in detecting advanced malware which uses evasion techniques such as inline assembly or previously unknown approaches. The presentation also focuses on leveraging the static code analysis as an opportunity to detect these evasive malware in the sandbox
A malware detection method for health sensor data based on machine learning
The document proposes a malware detection method for health sensor data based on machine learning. It aims to identify malware patterns in health sensor data rather than just detecting small changes. It will use XGBoost, LightGBM, and Random Forest models to analyze health sensor data from terabytes of benign and malware programs. The challenges are selecting features from the health sensor data, modifying the models for training and testing, and evaluating the features and models. When a malware program is detected by one model, its pattern will be broadcast to the other models to prevent malware intrusion more effectively.
Malware Detection Using Data Mining Techniques
This document discusses techniques for malware detection using data mining. It begins by defining the problem of malware as one of the most serious issues faced on the internet. It then discusses types of malware like viruses, worms, trojans, and rootkits. It describes how rootkits can hide themselves and their activities. The document outlines static and dynamic analysis methods for malware detection and describes signature-based and behavior-based detection techniques. It shows results from using the Weka tool achieving over 97% success in rootkit detection. Advanced techniques discussed include n-grams and analyzing API/system calls.
Advanced malware analysis training session6 malware sandbox analysis
Advanced malware analysis training session6 malware sandbox analysisCysinfo Cyber Security Community
This document provides an overview and demonstration of sandbox analysis for malware samples. It discusses how sandboxing allows executing samples in a controlled and monitored environment to observe behavior. It then introduces Sandbox.py, an open source Python tool that automates static, dynamic, and memory analysis using other tools. Sandbox.py runs samples in a virtual machine sandbox, monitors the system, and generates reports of the sample's activities and artifacts for later analysis. The document demonstrates Sandbox.py by running a sample, and screenshots show the static, dynamic, and memory analysis results it produces.Understand How Machine Learning Defends Against Zero-Day Threats
Detection Challenges
Machine Learning Approaches
Modeling Machine Learning classifiers
Attacks on Machine Learning Defenses
Real Protect
Deep Learning in Sandbox
Full-System Emulation Achieving Successful Automated Dynamic Analysis of Evas...
This document discusses techniques for achieving successful automated dynamic analysis of evasive malware through full system emulation. It begins by introducing the speaker and their background in malware research. It then discusses the goals of automated malware analysis, different analysis approaches (such as system call hooking and process emulation), and how full system emulation provides the highest visibility and fidelity while maintaining good performance. The document also covers challenges posed by malware evasion techniques and ways analysis systems can work to bypass triggers and detect stalling code.
Android Malware Detection Mechanisms
The document discusses Android malware detection mechanisms. It outlines the major types of Android malware like backdoors and spyware. It then describes several approaches to malware detection like static analysis of APK files to examine permissions, activities, and API calls. Signature-based analysis uses a signature database to classify apps as benign or malware. Tools for static analysis like apktool, aapt, and dex2jar are also mentioned. The document concludes with comparisons of different Android malware detection systems and their abilities.
Android malware overview, status and dilemmas
This document summarizes research on malicious Android apps from 2012-2013. Some key points:
- Over 1,260 app samples were analyzed in 2012, with many found to steal user information, turn devices into bots, or generate revenue through premium calls/texts.
- Early malware like DroidDream, Plankton, and BaseBridge leveraged exploits to gain root access and stealthily update themselves. Later malware got more sophisticated with techniques like anti-analysis tricks.
- Malware has been distributed through third-party app stores and underground affiliate programs. Estimates found infection rates between 0.0009-1% of Android devices by 2013.
- While most stole data or made money fraud
More Related Content
What's hot
Malware detection-using-machine-learning
This document discusses using machine learning and deep learning for malware detection. It notes that over 350,000 new malware are created daily, posing a significant threat. Traditional signature-based detection has limitations in detecting new malware. The document reviews research applying machine learning and deep learning techniques to malware detection using static and dynamic analysis of features. It then describes the authors' approach of using opcode frequency models with random forest and neural networks to classify files, achieving 97-98% precision and recall on a test set. The conclusion is that machine learning and deep learning can help address limitations of traditional approaches by enabling detection of new malware.
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...
В докладе речь пойдёт о применении алгоритмов машинного обучения для обнаружения вредоносных приложений для Android. Я расскажу, как на базе Матрикснета в Яндексе был спроектирован высокопроизводительный инструмент для решения этой задачи. А также продемонстрирую, в каких случаях аналитические методы выявления вредоносного ПО помогают блокировать множество простых образцов вирусного кода. Затем мы поговорим о том, как можно усовершенствовать такие методы для обнаружения более хитроумных вредных программ.
Machine Learning for Malware Classification and Clustering
1) Machine learning can be used as a replacement for antivirus software by using statistical techniques to learn patterns from large malware datasets.
2) Boosted decision trees are well-suited for malware classification because they perform like a game of 20 questions to maximize discrimination between malware and benign classes.
3) Features used in machine learning models require a balance between complexity, which provides more information but less explainability, and explainability, which provides insights to analysts but may not help classification.
Malware Detection using Machine Learning
This document discusses using machine learning for malware detection. It begins with an introduction to machine learning and feature selection for classification problems. Popular supervised learning algorithms that can be used for malware detection are discussed, including K-Nearest Neighbors (KNN), K-Means clustering, Support Vector Machines (SVM), Decision Trees, and Random Forests. Features that could be used for malware detection, such as file size, entropy, and behavioral patterns, are also outlined. The document concludes with brief mentions of neural networks, deep learning, and Python libraries that can be used like Scikit-Learn.
An Introduction to Malware Classification
With more than 1 million new pieces of malware released every day, security vendors are turning toward machine learning to automate threat detection. This talk aims to give new researchers the background they need for contributing to this field. We'll talk about sources for malicious PE files, consistently top-performing machine learning algorithms, extracting features, and how to prevent overfitting. (20 minute)
The Future of Automated Malware Generation
The document discusses the current and future states of automated malware generation and malware defense techniques. It describes how malware distribution networks currently work and trends showing rising malware samples. The future of malware defense is proposed to apply more machine learning and statistical techniques to model malware behaviors and attributes in order to handle growing sample volumes. This would involve training machine learning classifiers on features identified by human experts to classify and cluster malware more effectively.
Fighting advanced malware using machine learning (English)
In this paper, behavioral-based detection powered by machine learning is introduced. As the result, detection ratio is dramatically improved by comparison with traditional detection.
Needless to say that malware detection is getting harder today. Everybody knows signature-based detection reaches its limit, so that most anti-virus vendors use heuristic, behavioral and reputation-based detections altogether. About targeted attack, basically attackers use undetectable malware, so that reputation-based detection doesn't work well because it needs other victims beforehand. And it is a fact that detection ratio is not enough though we use heuristic and behavioral-based detections. In our research using the Metascan, average detection ratio of newest malware by most anti-virus scanner is about 30 %( the best is about 60 %).
By the way, heuristic and behavioral-based detections are developed by knowledge and experience of malware analyst. For example, most analysts know that following features are indicator that those programs are malicious.
- A file imports VirtualAlloc, VirtualProtect and LoadLibrary only and has a strange section name
- An entry point that does not fall within declared text or code section
- Creating remote threads into a legitimate process like explore.exe
- After unpacking, calling OpenMutex and CreateMutex to avoid multiple infections
- Register itself to auto start extension points like services and registry
- Creating a .bat file and try to delete own itself through executing the file with cmd.exe
- Setting global hook to capture keystroke using SetWindowsHookEx
Heuristic and behavioral-based detections are developed based on those pre-determined features like above. Analysts are finding those features day by day. But, this kind of work is not appropriate for human. Therefore we classified programs as malware or benign by machine learning through dynamic analysis results. Thereby, detection ratio is dramatically improved and we could recognize that which features are strongly related to malware by numeric score. And then, we could find the features which we’ve never found by this method. Finally, the outlook and challenges of this method will be tackled.
Automated In-memory Malware/Rootkit Detection via Binary Analysis and Machin...
Discussion and demonstration of an automated approach
for pairing Memory Forensics with Binary Analysis and
Machine Learning to analyze the execution behavior of
binaries collected from a set of hosts to detect advanced
persistent threats (APT)s that may evade detection by
hooking and "traditional" emulation.
Android Malware Analysis
This document analyzes the "machine.apk" Android malware sample using static and dynamic analysis techniques. The summary includes:
1. Static analysis is performed using tools like apktool, dex2jar, and jd-gui to disassemble the app and examine its AndroidManifest.xml and Java source code. This reveals the malware requests dangerous permissions like location, SMS/calls, and implements tapping and tracking services.
2. Dynamic analysis with CopperDroid and Anubis is able to monitor the app's network traffic and activity timeline. The analysis finds the app extracts the phone number and operator, sends this data to Thailand, can make automatic calls, and tracks the device location.
3.
Metamorphic Malware Analysis and Detection
ABSTRACT :
--------------------
Modern malware that are metamorphic or polymorphic in nature mutate their code by employing code obfuscation and encryption methods to thwart detection. Thus, conventional signature based scanners fail to detect these malware. In order to address the problems of detecting known variants of metamorphic malware, we propose a method using bioinformatics techniques effectively used for Protein and DNA matching. Instead of using exact signature matching methods, more sophisticated signature(s) are extracted using multiple sequence alignment (MSA). The results show that the proposed method is capable of identifying malware variants with minimum false alarms and misses. Also, the detection rate achieved with our proposed method is better compared to commercial antivirus products used in the study.
Status:
----------
This work has been accepted by 8th IEEE International Conference on Innovations in Information Technology (Innovations'12).
Link:
-------
http://ieeexplore.ieee.org/xpl/login.jsp?reload=true&tp=&arnumber=6207739&url=http://ieeexplore.ieee.org/iel5/6203543/6207707/06207739.pdf?arnumber=6207739
e-mail: grijesh.mnit@gmail.com
Malware Detection Using Machine Learning Techniques
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
Vulnerability and Exploit Trends: Combining behavioral analysis and OS defens...
Despite the best efforts of the security community—and big claims from security vendors—large areas of vulnerabilities and exploits remain to be leveraged by adversaries.You will learn about:
- A new perspective on the current state of software flaws.
- The wide margin between disclosed vulnerabilities and
public exploits including a historical analysis and
trending patterns.
- Effective countermeasures that can be deployed to
detect, and prevent, the exploitation of vulnerabilities.
- The limitations of Operating System provided mitigations,
and how a combination of increased countermeasures
with behavioral analysis will get defenders closer to
preventing the largest number of threats.
Nguyen Huu Trung - Building a web vulnerability scanner - From a hacker’s view
This document discusses building a high performance web application vulnerability scanner. It begins with an introduction of the speaker and agenda. It then defines what a WAVS is and why they are needed for both penetration testers and businesses to discover vulnerabilities. The document discusses why building your own WAVS is typically not recommended and reviews common challenges. It proposes an architecture with core and plugin components and discusses approaches like crawling and fuzzing, CPE and CVE mapping, and public exploit testing. Recommendations are provided around programming languages, code design patterns, and challenges like JavaScript crawling, high overhead, false positives, and other considerations.
Setup Your Personal Malware Lab
This document discusses how to set up a malware analysis lab. It begins by introducing malware analysis and explaining why it is important. It then defines a malware lab as a safe, isolated environment for analyzing malware, noting both physical and virtual options. The document outlines the steps to build a malware lab, including isolating systems, installing behavioral and code analysis tools, and using online analysis tools. Finally, it lists specific tools that could be included in a malware lab, such as honeypots, behavioral monitors, disassemblers, sandboxes, and online scanners.
Detecting Evasive Malware in Sandbox
This presentation talk about some of the challenges in detecting advanced malware which uses evasion techniques such as inline assembly or previously unknown approaches. The presentation also focuses on leveraging the static code analysis as an opportunity to detect these evasive malware in the sandbox
A malware detection method for health sensor data based on machine learning
The document proposes a malware detection method for health sensor data based on machine learning. It aims to identify malware patterns in health sensor data rather than just detecting small changes. It will use XGBoost, LightGBM, and Random Forest models to analyze health sensor data from terabytes of benign and malware programs. The challenges are selecting features from the health sensor data, modifying the models for training and testing, and evaluating the features and models. When a malware program is detected by one model, its pattern will be broadcast to the other models to prevent malware intrusion more effectively.
Malware Detection Using Data Mining Techniques
This document discusses techniques for malware detection using data mining. It begins by defining the problem of malware as one of the most serious issues faced on the internet. It then discusses types of malware like viruses, worms, trojans, and rootkits. It describes how rootkits can hide themselves and their activities. The document outlines static and dynamic analysis methods for malware detection and describes signature-based and behavior-based detection techniques. It shows results from using the Weka tool achieving over 97% success in rootkit detection. Advanced techniques discussed include n-grams and analyzing API/system calls.
Advanced malware analysis training session6 malware sandbox analysis
Advanced malware analysis training session6 malware sandbox analysisCysinfo Cyber Security Community
This document provides an overview and demonstration of sandbox analysis for malware samples. It discusses how sandboxing allows executing samples in a controlled and monitored environment to observe behavior. It then introduces Sandbox.py, an open source Python tool that automates static, dynamic, and memory analysis using other tools. Sandbox.py runs samples in a virtual machine sandbox, monitors the system, and generates reports of the sample's activities and artifacts for later analysis. The document demonstrates Sandbox.py by running a sample, and screenshots show the static, dynamic, and memory analysis results it produces.Understand How Machine Learning Defends Against Zero-Day Threats
Detection Challenges
Machine Learning Approaches
Modeling Machine Learning classifiers
Attacks on Machine Learning Defenses
Real Protect
Deep Learning in Sandbox
Full-System Emulation Achieving Successful Automated Dynamic Analysis of Evas...
This document discusses techniques for achieving successful automated dynamic analysis of evasive malware through full system emulation. It begins by introducing the speaker and their background in malware research. It then discusses the goals of automated malware analysis, different analysis approaches (such as system call hooking and process emulation), and how full system emulation provides the highest visibility and fidelity while maintaining good performance. The document also covers challenges posed by malware evasion techniques and ways analysis systems can work to bypass triggers and detect stalling code.
What's hot (20)
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...
Machine Learning for Malware Classification and Clustering
Machine Learning for Malware Classification and Clustering
Fighting advanced malware using machine learning (English)
Fighting advanced malware using machine learning (English)
Automated In-memory Malware/Rootkit Detection via Binary Analysis and Machin...
Automated In-memory Malware/Rootkit Detection via Binary Analysis and Machin...
Malware Detection Using Machine Learning Techniques
Malware Detection Using Machine Learning Techniques
Vulnerability and Exploit Trends: Combining behavioral analysis and OS defens...
Vulnerability and Exploit Trends: Combining behavioral analysis and OS defens...
Nguyen Huu Trung - Building a web vulnerability scanner - From a hacker’s view
Nguyen Huu Trung - Building a web vulnerability scanner - From a hacker’s view
A malware detection method for health sensor data based on machine learning
A malware detection method for health sensor data based on machine learning
Advanced malware analysis training session6 malware sandbox analysis
Advanced malware analysis training session6 malware sandbox analysis
Understand How Machine Learning Defends Against Zero-Day Threats
Understand How Machine Learning Defends Against Zero-Day Threats
Full-System Emulation Achieving Successful Automated Dynamic Analysis of Evas...
Full-System Emulation Achieving Successful Automated Dynamic Analysis of Evas...
Viewers also liked
Android Malware Detection Mechanisms
The document discusses Android malware detection mechanisms. It outlines the major types of Android malware like backdoors and spyware. It then describes several approaches to malware detection like static analysis of APK files to examine permissions, activities, and API calls. Signature-based analysis uses a signature database to classify apps as benign or malware. Tools for static analysis like apktool, aapt, and dex2jar are also mentioned. The document concludes with comparisons of different Android malware detection systems and their abilities.
Android malware overview, status and dilemmas
This document summarizes research on malicious Android apps from 2012-2013. Some key points:
- Over 1,260 app samples were analyzed in 2012, with many found to steal user information, turn devices into bots, or generate revenue through premium calls/texts.
- Early malware like DroidDream, Plankton, and BaseBridge leveraged exploits to gain root access and stealthily update themselves. Later malware got more sophisticated with techniques like anti-analysis tricks.
- Malware has been distributed through third-party app stores and underground affiliate programs. Estimates found infection rates between 0.0009-1% of Android devices by 2013.
- While most stole data or made money fraud
Malware
The document provides an overview of malware types and techniques. It discusses viruses, worms, trojans, rootkits, and other malware. It describes how malware infects systems, propagates, and hides. Historic malware examples like Morris worm, Code Red, and SQL Slammer are summarized. Methods for malware detection like signatures, heuristics, sandboxing, and network monitoring are also covered at a high level.
Intro to Malware Analysis
This document provides an introduction to malware analysis. It discusses analyzing malware to understand adversaries and gather intelligence indicators that can be used to better protect systems. The document outlines that malware analysis involves taking malware apart to study it, identifies why it's important to profile attackers, and discusses recommended tools and tips for malware analysis including automated analysis tools, manual analysis tools, and sources for indicators of compromise. It provides an example analysis of the "Magneto" malware that exploits a Firefox vulnerability to gather system information from targeted machines.
AVTOKYO2012 Android Malware Heuristics(en)
1) The researcher analyzed over 14,000 Android malware samples and found that they were signed with only 589 unique certificates, showing that many malwares reuse the same certificates.
2) Further analysis of the largest malware family, FakeInst, showed that its 4,911 samples were signed by only 31 certificates, with the most used certificate signing 2,602 samples.
3) Some certificates were found to be used by malwares for over a year, indicating they may be shared between malware developers.
Fingerprint identification technique
This document summarizes a research paper on fingerprint identification using wavelet-based feature extraction. The paper proposes a Wavelet-Bands Selection Features (WBSF) method to enhance fingerprint recognition rates. Fingerprint images from 49 individuals were collected and preprocessed before feature extraction. In closed-set tests, recognition rates were initially below 90% but increased to 100% with the WBSF method. The WBSF descriptors provided efficient representation for fingerprint variability. Open-set verification tests on 290 trials from 29 individuals achieved verification rates over 97% using two distance measures. The proposed method effectively extracts wavelet features for fingerprint recognition and verification.
Data Hiding in Audio Signals
Nowadays, several methods are used for communicating secret messages for defense purposes or in order to ensure the privacy of communication between two parties. So we go for hiding information in ways that prevent its detection.
Fast detection of Android malware: machine learning approach
This is a my presentation for YaC 2013 about machine learning based system for fast classification of Android applications. Covered themes: how to find malware around thousands of applications in Store.
Android malware analysis
Discusses how to perform malware analysis on Android devices. Initially presented at BSidesDE 2011 (in a much more fun format), the version here is as-presented at Rochester Security Summit 2011.
Malware analysis using volatility
Volatility is an open source memory forensics tool that analyzes RAM dumps to detect malware. It supports Windows, Linux, Mac and Android and uses plugins to parse memory images and extract useful information. Some key things Volatility can do include detecting injected code, unpacked files, hooks, and kernel driver modifications left by malware in memory. It allows analyzing ransomware locked systems, unpacking encrypted files, and dumping executable content of running processes for further reverse engineering.
Generic Solving Of Text Based Captcha
Here is a complete seminar report for B. Tech Students.
This Seminar is about an algorithm used for solving Text Based Captcha.
Data hiding in audio signals ppt
The document discusses data hiding in audio signals using watermarking techniques. It provides an introduction to watermarking and its applications. It describes the basic watermarking system as similar to a communication system. It also discusses speech processing, wavelet analysis, hiding techniques, advantages and disadvantages of audio watermarking. The document presents results from watermarking an audio signal and inserting a speech signal. It concludes that audio watermarking can be used for information tracing, tamper detection and other purposes beyond covert communication.
Экспресс-анализ вредоносов / Crowdsourced Malware Triage
Ведущие: Сергей Франкофф и Шон Уилсон
Сортировка вредоносного ПО представляет собой процесс быстрого анализа потенциально опасных файлов или URL. Любая тщательно продуманная система реагирования на инциденты безопасности обладает этой важной функцией. Но что, если у вас не установлена программа реагирования на инциденты? Как быть, если вы только начали ее настраивать? А вдруг у вас нет программных средств для проведения анализа? Грамотно выбранный бесплатный онлайн-инструмент, веб-браузер и блокнот — вот все, что вам пригодится. На мастер-классе участники самостоятельно будут заниматься сортировкой вредоносного ПО. Ведущий предоставит информацию о необходимых инструментах.
Выживший
Ведущий: Андрей Масалович
Целый арсенал средств информационного воздействия используется в корпоративном коммуникационном маркетинге и в астротурфинге. Докладчик расскажет о том, как подготавливаются информационные атаки, как распознавать их на ранних стадиях и противостоять им. Проанализирует особенности восприятия и распространения информации в социальных сетях с использованием троллей и ботов. Рассмотрит метод экспресс-анализа социальных портретов участников массовых обсуждений — рядовых пользователей, политиков, медийных персон, подростков, вербовщиков и их жертв.
Practical Malware Analysis: Ch 0: Malware Analysis Primer & 1: Basic Static T...
This document provides an overview of basic static malware analysis techniques. It discusses using antivirus scanners, hashing files, and finding strings to identify malware without executing it. It also covers analyzing the Portable Executable (PE) file format used in Windows executables, including examining the PE header, imported and exported functions, linked libraries, and sections like .text and .rsrc. The document demonstrates various tools for these static analysis tasks like HashCalc, strings, PEview, Dependency Walker, and Resource Hacker.
Introduction to Malware Analysis
This document provides an overview of malware analysis, including both static and dynamic analysis techniques. Static analysis involves examining a file's code and components without executing it, such as identifying file types, checking hashes, and viewing strings. Dynamic analysis involves executing the malware in a controlled environment and monitoring its behavior and any system changes. Dynamic analysis tools discussed include Process Explorer, Process Monitor, and Autoruns to track malware processes, files accessed, and persistence mechanisms. Both static and dynamic analysis are needed to fully understand malware behavior.
Viewers also liked (20)
Fast detection of Android malware: machine learning approach
Fast detection of Android malware: machine learning approach
Экспресс-анализ вредоносов / Crowdsourced Malware Triage
Экспресс-анализ вредоносов / Crowdsourced Malware Triage
Practical Malware Analysis: Ch 0: Malware Analysis Primer & 1: Basic Static T...
Practical Malware Analysis: Ch 0: Malware Analysis Primer & 1: Basic Static T...
Similar to Semantics aware malware detection ppt
Design and Development of an Efficient Malware Detection Using ML
Enormous growth and generation of data is happening in every day from various sources. The generated data is presented in various formats, i.e., in structured, unstructured, semi-structured, pdfs, docs, csvs, and raw file formats. All these files are not genuine or pure in all scenarios cause which is generated from identified and unidentified sources. The modern malware is designed with mutation characteristics, that means, it can change its behavior based on the properties of physical file. It is a contraction from malicious software. The tremendous growth of the data is very helpful to the malware designers to execute the malware files such as Virus, Trojans, and Ransomware in any file. The formation of modern malware poses a variety of challenges to the antivirus industries. In this paper, we are going to induce a system with a lightweight model to accurately detect the malware for industrial use with high accuracy. In this, we are identifying nine different types of malwares like Ramnit, Lollipop, Kelihos_ver3, Vundo, etc., on huge amount of data (0.5 TB) that is provided by Microsoft.
Antimalware
This session discuss malware detection techniques and obfuscation techniques used by malware writers with special focus on signature based detection.
Model-checking for efficient malware detection
The document discusses using model checking for efficient malware detection. It outlines some limitations of traditional anti-virus techniques like signature matching and code emulation. Model checking is proposed as an alternative approach that can check a program's behavior without executing it. Pushdown systems are identified as a suitable formalism for modeling programs since they can analyze a program's stack, which is important for malware detection. A temporal logic called CTPL is discussed for specifying malicious behaviors, but it is noted that CTPL cannot fully describe stacks. The solution proposed is to consider predicates over stacks when specifying malicious behaviors.
What Are The Types of Malware? Must Read
If you want to make your career in ethical hacking, Bytecode Security offers the best malware analysis course online offline with job placement assistance. Read more: https://www.bytec0de.com/malware-analysis-course-training-certification/
Malware Analysis
Malware is harmful software that acts against the requirements of computer users. There are many types including viruses, worms, Trojans, spyware, and ransomware. Malware analysis is the process of understanding how malware functions and its potential impact. It involves scanning malware with automated tools, analyzing static properties like file hashes, observing interactive behavior in an isolated lab, and manually reversing code to understand capabilities and logic.
Type checking
Type checking involves assigning type expressions to each part of a program according to a type system's logical rules. This allows compilers to determine if a program's types conform or catch errors. Type checking can take the form of type synthesis, which builds up an expression's type from its parts, or type inference, which determines a construct's type from its use. Resolving names is also important for type checking.
Achieving quality with tools case study
The document discusses various techniques for achieving quality in software development, including static analysis tools. It describes how bugs occur due to human errors and complex codebases, and that testing alone cannot find all bugs. It then explains static analysis tools like FindBugs and PMD that can automatically find bugs, the challenges of static analysis, distinguishing bugs from style issues, and how tools like Eclipse and EclEmma are used to fix bugs and measure test coverage. The overall goal is to reduce bugs escaping to production through efficient tooling and quality practices.
Anti-virus Mechanisms and Various Ways to Bypass Antivirus detection
The document provides an overview of how anti-virus software works and techniques used to bypass antivirus detection. It discusses how antiviruses use signature-based, heuristic-based, behavioral, and sandboxing techniques to detect malware. It also explains common techniques used to evade detection like packers, splitters, code obfuscation, and injection. The document concludes that while antivirus has improved, virus creators continually develop new methods to bypass protections and that additional security measures are still needed.
Automated malware invariant generation
The document proposes new methods for automatically generating malware invariants from binary code to detect and identify malware. Current signature-based malware detectors can be evaded through obfuscation, but malware invariants capture semantic properties that are more difficult to obfuscate. The method involves using formal methods and static analysis to extract invariants from binary code and represent them as semantic signatures, called malware invariants, that can be matched against suspicious code to detect malware families. Combining multiple static and dynamic analysis tools can help generate strong malware invariants that circumvent common obfuscation techniques used by malware writers.
Introduction to Malware analysis
Hey Guys , In This Slide I'll be Directing you guys to the stepping stones of getting into malware analysis. Just Make Sure to focus on the slides and you are ready to go
The Four Types of Threat Detection and Use Cases in Industrial Security
Dragos' Sergio Caltagirone and Robert M. Lee discuss the four types of threat detection methods for industrial control systems operations, while providing ICS-specific use cases, to help you determine which detection strategy is most effective for your organization.
The recorded webinar can be found here: hhttps://youtu.be/zqvDu0OaY8k
Aslo check out: Four Types of Threat Detection White Paper: https://dragos.com/blog/FourTypesOfTh...
Part of the Secrets of ICS Cybersecurity webinar series: https://dragos.com/blog/20181017Webin...
More info www.dragos.com
Follow us on LinkedIn: https://www.linkedin.com/company/drag....
Follow us on Twitter: https://twitter.com/dragosinc
Unveiling Metamorphism by Abstract Interpretation of Code Properties
Metamorphic code includes self-modifying semantics-preserving transformations to exploit code diversification. The impact of metamorphism is growing in security and code protection technologies, both for preventing malicious host attacks, e.g., in soft- ware diversification for IP and integrity protection, and in malicious software attacks, e.g., in metamorphic malware self-modifying their own code in order to foil detection systems based on signature matching. In this paper we consider the problem of automatically extracting metamorphic signatures from metamorphic code. We introduce a semantics for self-modifying code, later called phase semantics, and prove its correctness by showing that it is an abstract interpretation of the standard trace semantics. Phase semantics precisely models the metamorphic code behavior by providing a set of traces of programs which correspond to the possible evolutions of the metamorphic code during execution. We show that metamorphic signatures can be automatically extracted by abstract interpretation of the phase semantics. In particular, we introduce the notion of regular metamorphism, where the invariants of the phase semantics can be modeled as finite state automata representing the code structure of all possible meta-morphic change of a metamorphic code, and we provide a static signature extraction algorithm for metamorphic code where metamorphic signatures are approximated in regular metamorphism.
Metasploit
The document provides an overview of the Metasploit framework. It describes Metasploit as an open-source penetration testing software that contains exploits, payloads, and other tools to help identify vulnerabilities. Key points covered include Metasploit's architecture and modules for scanning, exploitation, and post-exploitation. Examples of tasks that can be performed include port scanning, vulnerability assessment, exploiting known issues, and gaining access to systems using payloads and meterpreter sessions. The document warns that Metasploit should only be used for legitimate security testing and cautions about the potential risks if misused.
detection and classification of malware.pptx
The document presents a technique for detecting and classifying self-deleting malware using Windows Prefetch files. It trains a KNN classifier to detect malware execution based on prefetch file features. It then applies a Jaccard similarity classifier to attribute the malware to a family. The approach extracts features from over 4,000 malware and benign prefetch files, applies normalization to select relevant features, trains the KNN detector, and uses minimum family features and Jaccard similarity to classify into 48 malware families. The technique can detect malware execution and attribute it to a family using only prefetch file evidence, without accessing the original malware sample.
CHAPTER 1 MALWARE ANALYSIS PRIMER.pdf
This document provides an overview of malware analysis. It discusses the goals of malware analysis as determining what happened on a network and ensuring all infected files are found. It also describes static and dynamic analysis techniques, from basic approaches like examining file contents up to advanced methods like reverse engineering code. The document outlines common types of malware like backdoors, botnets, and information stealing malware. Finally, it provides some general rules for malware analysis like focusing on key features and using different analysis approaches when getting stuck.
Tech Days 2015: Static Analysis CodePeer
Static analysis tools like CodePeer can detect bugs and verify code correctness. CodePeer performs advanced static analysis to find runtime errors, verify code meets requirements, and automate parts of certification. It analyzes Ada source code and generates annotations to help developers fix issues. CodePeer integrates with IDEs and provides dashboards to help with continuous integration and security analysis. It supports incremental re-analysis to speed feedback during development.
Mutation Testing and MuJava
about mutation testing and demonstration of muJava. muJava is automated tool for mutation testing of java programs. It tests the test cases. hence good to enhance and checking effectiveness of your test suites.
Introduction To Malware Analysis.pptx
Malware analysis involves studying malicious software to understand its capabilities and how it infects systems. There are several types of malware like trojans, ransomware, and remote access tools. Malware analysis examines samples through static, dynamic, code, and behavioral analysis to determine the malware's functions, how the infection occurred, and how to prevent similar attacks. The process provides details about the malware's registry entries, files, and network activity to generate detection signatures.
Introduction To Malware Analysis.pptx
Malware analysis involves studying malicious software to understand its capabilities and how it infects systems. There are several types of malware like trojans, ransomware, and remote access tools. Malware analysis examines samples through static, dynamic, code, and behavioral analysis to determine the malware's functions, how the infection occurred, and how to prevent similar attacks. The process provides details on the malware's communications, files, and registry entries to generate detection signatures.
Similar to Semantics aware malware detection ppt (20)
Design and Development of an Efficient Malware Detection Using ML
Design and Development of an Efficient Malware Detection Using ML
Anti-virus Mechanisms and Various Ways to Bypass Antivirus detection
Anti-virus Mechanisms and Various Ways to Bypass Antivirus detection
The Four Types of Threat Detection and Use Cases in Industrial Security
The Four Types of Threat Detection and Use Cases in Industrial Security
Unveiling Metamorphism by Abstract Interpretation of Code Properties
Unveiling Metamorphism by Abstract Interpretation of Code Properties
Recently uploaded
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
A Deep Dive into ScyllaDB's Architecture
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
Demystifying Knowledge Management through Storytelling
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Recently uploaded (20)
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
AppSec PNW: Android and iOS Application Security with MobSF
AppSec PNW: Android and iOS Application Security with MobSF
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
Demystifying Knowledge Management through Storytelling
Demystifying Knowledge Management through Storytelling
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Semantics aware malware detection ppt
- 1. Semantics-Aware Malware Detection By Manish Kumar Yadav presented
- 2. Contents • Introduction • Goals • Technique • Semantics of malware • Malicious code detector • Strengths and limitations • Related work • Conclusion
- 3. INTRODUCTION • A malware detector is a system that attempts to determine whether a program has malicious intent. • A malware instance is a program that has malicious intent. • Examples of malware instance viruses, • trojans, and worms.
- 4. Goals • The goal of a malware writer (hacker) is to modify their malware to avoid detection by a malware detector. • The goal of this paper is to design a malware detection algorithm that uses semantics of instructions
- 5. Technique Aware Malware Detection • A common technique used by malware writers to evade detection is program obfuscation • Polymorphism and metamorphism • A polymorphic virus obfuscates its decryption loop using several transformations • Metamorphic viruses attempt to evade detection by obfuscating the entire virus.
- 6. Tanslation-validation techniques • Translation-validation techniques determine whether the two programs are semantically equivalent. • We use the observation that certain malicious behaviors appear in all variants of a certain malware. • We use semantic algorithm to discover malicious program.
- 7. Semantics of malware detection • Specifying the malicious behavior. • Templates • Variables • symbolic constants
- 9. Formal semantics • A template T = (IT , VT ,CT ) is a 3-tuple, where IT is a sequence of instructions and VT and CT are the set of variables and symbolic constants. Two types of symbolic constants. • n-ary function F(n) and n-ary predicate P(n)
- 10. The Malicious Code Detector
- 11. Strengths and limitations • Code reordering • Register renaming • Garbage insertion • Equivalent instruction replacement • same form needed • the use of def-use chains for value preservation checking.
- 12. Related work • Malware detection • Translation validation • Software verification
- 13. Conclusion • We observe that certain malicious behaviors appear in all variants of a certain malware. • We also presented a malware-detection algorithm that is sound with respect to our semantics.
- 14. • Thanks For Your Attention