This document proposes an effective malware detection approach based on machine learning using both behavior and data features. It extracts features from decompiled code including opcodes, system calls, and data types. Various classifiers are trained and evaluated on a large malware and benign dataset, achieving high detection rates. Experiments on fresh malware samples and obfuscated malware show the approach can detect previously unseen behaviors and has some resistance to obfuscation techniques.
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...Priyanka Aash
Machine learning algorithms are key to modern at-scale cyberdefense. Transfer learning is a state of the art ML paradigm that enables applying knowledge and algorithms developed from one field to another, resulting in innovative solutions. This talk presents transfer learning in action wherein techniques created from other areas are successfully re-purposed and applied to cybersecurity.
(Source: RSA Conference USA 2018)
Fast Automated Unpacking and Classification of MalwareSilvio Cesare
This document summarizes Silvio Cesare's research presentation on fast automated unpacking and classification of malware. The research aims to efficiently and effectively detect and classify malware using static analysis. It involves developing an automated unpacker using emulation and entropy analysis to unpack malware. It then extracts control flow graphs from unpacked malware and uses graph matching techniques to classify malware and identify variants by similarity. The techniques were evaluated on real malware samples and shown to accurately unpack and classify malware with low processing times suitable for real-time systems.
This presentation describes a intelligent IT monitoring solution that uses Nagios as source of information, Esper as the CEP engine and a PCA algorithm.
Dependability Benchmarking by Injecting Software BugsRoberto Natella
Benchmarks are an established practice for performance evaluation in the computer industry since decades. Examples of successful benchmarking initiatives are the TPC (Transaction Processing Performance Council) and the SPEC (Standard Performance Evaluation Corporation). More recently, the research community developed the notion of dependability benchmarking, which evaluates the quality of service (throughput, availability, etc.) of competing products in the presence of faults, by using fault injection. The idea of dependability benchmarking has been applied in several domains including transaction processing, telecom, automotive, etc.
Given that software faults (bugs) are a major cause of failures, it becomes important to assess dependability against these faults. However, emulating software faults in a controlled fault injection experiment is a difficult problem, since bugs originate from human error. This presentation discusses about the open challenges and the recent advances in the field of emulating software bugs in a representative way.
DevSecCon Talk: An experiment in agile Threat ModellingzeroXten
ThreatSpec aims to close the gap between development and security by bringing the threat modelling process further into the development process. This is achieved by having developers and security engineers write threat specifications alongside code, then dynamically generating reports and data-flow diagrams from the code.
The document discusses code-driven threat modeling using ThreatSpec. It describes how ThreatSpec can be used to specify threats and mitigations directly in code comments. Developers write ThreatSpec as they develop code and tests. Security reviews then analyze generated reports and data flow diagrams to ensure threats are properly mitigated. Code-driven threat modeling allows development and security teams to work together and keep the threat model and code in sync. While improvements are still needed, this approach has potential benefits over traditional threat modeling methods.
The document describes a proposed mini project on robust malware detection for IoT devices using deep eigen space learning. The proposed system aims to detect malware via a device's operational code sequence. It applies a deep eigen space learning approach to classify applications as malicious or benign based on their opcodes represented as vectors. The system is evaluated as achieving 98.37% accuracy and 98.59% precision in malware detection, and is shown to be robust against junk code insertion attacks. The document outlines the existing approaches, proposed system, system requirements, modules, architecture, results, and conclusion of the project.
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...Priyanka Aash
Machine learning algorithms are key to modern at-scale cyberdefense. Transfer learning is a state of the art ML paradigm that enables applying knowledge and algorithms developed from one field to another, resulting in innovative solutions. This talk presents transfer learning in action wherein techniques created from other areas are successfully re-purposed and applied to cybersecurity.
(Source: RSA Conference USA 2018)
Fast Automated Unpacking and Classification of MalwareSilvio Cesare
This document summarizes Silvio Cesare's research presentation on fast automated unpacking and classification of malware. The research aims to efficiently and effectively detect and classify malware using static analysis. It involves developing an automated unpacker using emulation and entropy analysis to unpack malware. It then extracts control flow graphs from unpacked malware and uses graph matching techniques to classify malware and identify variants by similarity. The techniques were evaluated on real malware samples and shown to accurately unpack and classify malware with low processing times suitable for real-time systems.
This presentation describes a intelligent IT monitoring solution that uses Nagios as source of information, Esper as the CEP engine and a PCA algorithm.
Dependability Benchmarking by Injecting Software BugsRoberto Natella
Benchmarks are an established practice for performance evaluation in the computer industry since decades. Examples of successful benchmarking initiatives are the TPC (Transaction Processing Performance Council) and the SPEC (Standard Performance Evaluation Corporation). More recently, the research community developed the notion of dependability benchmarking, which evaluates the quality of service (throughput, availability, etc.) of competing products in the presence of faults, by using fault injection. The idea of dependability benchmarking has been applied in several domains including transaction processing, telecom, automotive, etc.
Given that software faults (bugs) are a major cause of failures, it becomes important to assess dependability against these faults. However, emulating software faults in a controlled fault injection experiment is a difficult problem, since bugs originate from human error. This presentation discusses about the open challenges and the recent advances in the field of emulating software bugs in a representative way.
DevSecCon Talk: An experiment in agile Threat ModellingzeroXten
ThreatSpec aims to close the gap between development and security by bringing the threat modelling process further into the development process. This is achieved by having developers and security engineers write threat specifications alongside code, then dynamically generating reports and data-flow diagrams from the code.
The document discusses code-driven threat modeling using ThreatSpec. It describes how ThreatSpec can be used to specify threats and mitigations directly in code comments. Developers write ThreatSpec as they develop code and tests. Security reviews then analyze generated reports and data flow diagrams to ensure threats are properly mitigated. Code-driven threat modeling allows development and security teams to work together and keep the threat model and code in sync. While improvements are still needed, this approach has potential benefits over traditional threat modeling methods.
The document describes a proposed mini project on robust malware detection for IoT devices using deep eigen space learning. The proposed system aims to detect malware via a device's operational code sequence. It applies a deep eigen space learning approach to classify applications as malicious or benign based on their opcodes represented as vectors. The system is evaluated as achieving 98.37% accuracy and 98.59% precision in malware detection, and is shown to be robust against junk code insertion attacks. The document outlines the existing approaches, proposed system, system requirements, modules, architecture, results, and conclusion of the project.
Palestra do evento "Cybersecurity: a nova era em resposta a incidentes e auditoria de dados"
Jim Butterworth - Senior Cybersecurity Director Guidance Software Inc.
Brasília, 04 de agosto de 2010
Introduzione ai network penetration test secondo osstmmSimone Onofri
"Competent Analysts will require adequate networking knowledge,
diligent security testing skills, and critical thinking skills to
assure factual data collection creates factual results through
correlation and analysis." - OSSTMM v3
Il Network Penetration Test (NPT) ha lo scopo verificare la sicurezza
dei sistemi esposti sulla rete. Viene valutata la presenza di
controlli - e la loro corretta implementazione - che annullano o
limitano le minacce esistenti verso i beni dell'organizzazione.
L'attività valuta uno scenario specifico che varia secondo il
bersaglio, la posizione degli attaccanti e le informazioni in possesso
al personale coinvolto.
Un Penetration Test si esegue tramite varie attivtà spesso molto
delicate e importanti e, come ben specificato nell'Open Source
Security Testing Methodology Manual (OSSTMM), gli analisti non solo
devono avere delle competenze adeguate della rete e dei suoi
protocolli ma anche applicare un ragionamento critico per raccogliere
e correlare le informazioni in maniera corretta così da ottenere
risultati oggettivi.
Durante il seminario verrà introdotta la metodologia OSSTMM, con
particolare attenzione alle reti TCP/IP (Data Networks) e alle
operazioni tipiche per la ricerca degli host sulla rete e
l'identificazione dei servizi interattivi.
This document describes a proposed system for detecting cyber attacks using Bayesian inference. It begins with an introduction to the problem of credit/debit card theft and existing physical unclonable functions. It then discusses the disadvantages of existing cyber attack detection systems, such as performance issues and high false positive rates. The proposed system builds a directed acyclic graph to represent the probability distribution of variables related to cyber attacks. It will use modules for data collection, preprocessing, model training/testing, and attack detection. The system will be implemented in Python using frameworks like Django and evaluated using algorithms like random forest, artificial neural networks, and support vector machines.
A Fast Flowgraph Based Classification System for Packed and Polymorphic Malwa...Silvio Cesare
We propose an algorithm to identify malware variants by determining program similarity through estimating isomorphic control flow graphs. We implement this approach in a prototype system that demonstrates its ability to detect real malware variants with low false positives and logarithmic performance scalability, making it suitable for endhost adoption. Control flow graphs provide a more invariant characteristic than traditional static features like byte sequences for identifying polymorphic malware variants. Our system generates signatures for control flow graphs to efficiently compare programs and classify unknown samples.
Malware Classification Using Structured Control FlowSilvio Cesare
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.
Making Runtime Data Useful for Incident Diagnosis: An Experience ReportQAware GmbH
QuASD/PROFES 2018, Wolfsburg: Talk by Marcus Ciolkowski (@M_Ciolkowski, Principal IT Consultant at QAware) and Florian Lautenschlager (@flolaut, Senior Software Engineer)
=== Please download slides if blurred! ===
Abstract: Important and critical aspects of technical debt often surface at runtime only and are difficult to measure statically.
This is a particular challenge for cloud applications because of their highly distributed nature.
Fortunately, mature frameworks for collecting runtime data exist but need to be integrated.
In this paper, we report an experience from a project that implements a cloud application within Kubernetes on Azure.
To analyze the runtime data of this software system, we instrumented our services with Zipkin for distributed tracing; with Prometheus and Grafana for analyzing metrics; and with fluentd, Elasticsearch and Kibana for collecting, storing and exploring log files.
However, project team members did not utilize these runtime data until we created a unified and simple access using a chat bot.
We argue that even though your project collects runtime data, this is not sufficient to guarantee its usage: In order to be useful, a simple, unified access to different data sources is required that should be integrated into tools that are commonly used by team members.
Get the research paper: http://bitly.com/2QmSNwl
In the recent years, the traditional application monolith has broken down into a hefty chunk of micro-services thereby increasing the attack surface. We will look at how this increases the entry points into the complex modern day application ecosystem. The modern security tester needs various skills to pen-test such apps including the understanding of containers to successfully break or defend such applications.
When we tie this with the fast paced devOps life cycles for applications and explore the challenges when scaling security for such applications across the organization.
Hence, this webinar discusses traditional and relatively newer methods of Pen-testing web applications. Thereby illustrating how the changing business requirements and Agile life cycles for applications affect Security testing for modern applications.
Key Takeaways:
- what do the traditional Pen testing/Security testing Techniques entail?
- How is the landscape for Applications changing and how it affects security testing?
- What are the key essentials for testing modern applications?
- what can be done to scaling Security Assessments(Testing) for Modern & Agile life cycles?
Dr. Fengmin Gong, Co-Founder and Chief Strategy Officer, presents why an ecosystem-based approach is necessary to defend against modern malware threats. Discussion continues with what it takes to implement cybersecurity using this approach. He also presents a number of use cases where multi-vendor products interacting in a security ecosystem provide the most effective protection for enterprises.
SF Bay Area Splunk User Group Meeting October 5, 2022Becky Burwell
Andrew D'Auria, the Director of Sales Engineering at Anvilogic, gave a presentation on modernizing threat detection engineering. He discussed problems with the current detection engineering process, including that it is slow, results in noisy alerts, and lacks coordination across tools. D'Auria proposed using Anvilogic's platform to build detections based on MITRE ATT&CK techniques and scenarios, correlate events of interest without code, and measure detection program effectiveness to improve security operations. He provided examples of how Anvilogic helped a financial client improve detections and reduce alerts.
Real time intrusion detection in network traffic using adaptive and auto-scal...Gobinath Loganathan
This document proposes an adaptive and auto-scaling stream processor called Wisdom to enable real-time intrusion detection in network traffic. Wisdom can dynamically optimize complex event processing (CEP) rules using hybrid optimization algorithms like particle swarm optimization and bisection. Tests show Wisdom can detect attacks like HTTP slow header denial of service and port scans with over 99.95% accuracy. Wisdom also allows functionally auto-scaling deployments of CEP rules to optimize resource usage.
IRJET - Survey on Malware Detection using Deep Learning MethodsIRJET Journal
This document discusses various machine learning methods for malware detection, including support vector machines (SVM), random forests, and decision trees. It provides an overview of each method and related works that have applied these techniques. Specifically, it examines analyses that used linear SVM, random forests on Android apps, and an improved decision tree algorithm to classify malware families. The document concludes that machine learning methods have become important for malware detection as signatures alone cannot keep up with new malware variants.
This document evaluates the performance of three machine learning algorithms - Naive Bayes, Random Forest, and Stochastic Gradient Boosting - for detecting DDoS attacks. The algorithms were tested on a dataset from the Canadian Institute for Cybersecurity containing normal and attack network traffic. Stochastic Gradient Boosting achieved 100% accuracy, precision, recall and F1 score, outperforming the other algorithms. However, it had the longest execution time of 5.87 seconds compared to 1.55 seconds for Random Forest and 0.037 seconds for Naive Bayes. In conclusion, Stochastic Gradient Boosting was the most accurate for classifying DDoS attacks, but took significantly longer to run than the other models.
This document describes how a robot assessor can automate the process of vulnerability assessments by executing common security tools. The robot assessor uses heuristics to discover services on a target, determine which tools to run, execute those tools via APIs, and record the results. This allows vulnerability assessments to be initiated with a single command, freeing up analysts to focus on analysis rather than repetitive tasks. Several examples are provided of how the robot assessor would automate running tools like nmap, Nikto, sqlmap, and more.
Machine learning techniques applied to detect cyber attacks on web applicationsVenkat Projects
This document discusses using machine learning techniques to detect cyber attacks on web applications. It proposes using a graph-based approach and regular expressions to model normal HTTP request behavior during a learning phase. This would establish a baseline for detecting anomalies and potential attacks. Existing approaches are also reviewed that use algorithms like NSG, LSEG and F-Sign to generate signatures for detecting malware based on network traffic patterns or software code. Supervised machine learning methods have also been applied using features extracted from network data and classifiers like k-NN, Naive Bayes and neural networks. The proposed system would adapt this machine learning paradigm to specifically detect attacks on web applications.
Machine learning techniques applied to detect cyber attacks on web applicationsVenkat Projects
Machine learning techniques applied to detect cyber attacks on web applications
The increased usage of cloud services, growing number of web applications users, changes in network infrastructure that connects devices running mobile operating systems and constantly evolving network technology cause novel challenges for cyber security. As a result, to counter arising threats, network security mechanisms, sensors and protection schemes also have to evolve, to address the needs and problems of the users. In this article, we focus on countering emerging application layer cyber attacks since those are listed as top threats and the main challenge for network and cyber security. The major contribution of the article is the proposition of machine learning approach to model normal behaviour of application and to detect cyber attacks. The model consists of patterns (in form of Perl Compatible Regular Expressions (PCRE) regular expressions) that are obtained using graph-based segmentation technique and dynamic programming. The model is based on information obtained from HTTP requests generated by client to a web server. We have evaluated our method on CSIC 2010 HTTP Dataset achieving satisfactory results.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
Data flow analysis is a type of static code analysis that examines how values are propagated through a program. It is more effective than pattern matching or regular static analysis at finding defects related to interactions between methods and classes that may be difficult to uncover through testing alone. Static analysis tools using data flow analysis can simulate execution paths to detect potential issues without requiring the code to be compiled and run. Developers are encouraged to use static testing tools to catch defects early in development, as prevention of bugs is more efficient than finding and fixing them later.
[IITB BTP 2015 Dec] Dynamic detection of malware in android OS.pptxDeepanjanKundu2
Malware is increasingly becoming a serious threat and a nuisance in the information and network age. Human experts have to extract (involves complex analysis of encrypted and/or packed binaries) a signature (usually a text pattern) of the malware and deploy it, to protect against a malware. However, this approach does not work for polymorphic and metamorphic malware, which have the ability to change shape from attack to attack; also, metamorphic virus detection even assuming fixed length is NP-complete. There have been numerous static code checking methods which are trying to detect anomalies and report the presence of malware. But in the current time there are numerous code obfuscation techniques which help them get away undetected. What we need is a dynamic method which reads through the processes run by the apps and detects unwanted behavior and reports it Android Systems have …
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...Silvio Cesare
This document describes two web services called Simseer and Bugwise for software defect detection and similarity analysis. Simseer performs malware variant and plagiarism detection by generating control flow signatures and comparing similarities. Bugwise detects bugs like double frees through decompilation and data flow analysis. The services are implemented through a PHP frontend and C++ backend called Malwise that performs analysis through plugins. Initial results found the web services had minimal overhead compared to command line usage.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
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
Palestra do evento "Cybersecurity: a nova era em resposta a incidentes e auditoria de dados"
Jim Butterworth - Senior Cybersecurity Director Guidance Software Inc.
Brasília, 04 de agosto de 2010
Introduzione ai network penetration test secondo osstmmSimone Onofri
"Competent Analysts will require adequate networking knowledge,
diligent security testing skills, and critical thinking skills to
assure factual data collection creates factual results through
correlation and analysis." - OSSTMM v3
Il Network Penetration Test (NPT) ha lo scopo verificare la sicurezza
dei sistemi esposti sulla rete. Viene valutata la presenza di
controlli - e la loro corretta implementazione - che annullano o
limitano le minacce esistenti verso i beni dell'organizzazione.
L'attività valuta uno scenario specifico che varia secondo il
bersaglio, la posizione degli attaccanti e le informazioni in possesso
al personale coinvolto.
Un Penetration Test si esegue tramite varie attivtà spesso molto
delicate e importanti e, come ben specificato nell'Open Source
Security Testing Methodology Manual (OSSTMM), gli analisti non solo
devono avere delle competenze adeguate della rete e dei suoi
protocolli ma anche applicare un ragionamento critico per raccogliere
e correlare le informazioni in maniera corretta così da ottenere
risultati oggettivi.
Durante il seminario verrà introdotta la metodologia OSSTMM, con
particolare attenzione alle reti TCP/IP (Data Networks) e alle
operazioni tipiche per la ricerca degli host sulla rete e
l'identificazione dei servizi interattivi.
This document describes a proposed system for detecting cyber attacks using Bayesian inference. It begins with an introduction to the problem of credit/debit card theft and existing physical unclonable functions. It then discusses the disadvantages of existing cyber attack detection systems, such as performance issues and high false positive rates. The proposed system builds a directed acyclic graph to represent the probability distribution of variables related to cyber attacks. It will use modules for data collection, preprocessing, model training/testing, and attack detection. The system will be implemented in Python using frameworks like Django and evaluated using algorithms like random forest, artificial neural networks, and support vector machines.
A Fast Flowgraph Based Classification System for Packed and Polymorphic Malwa...Silvio Cesare
We propose an algorithm to identify malware variants by determining program similarity through estimating isomorphic control flow graphs. We implement this approach in a prototype system that demonstrates its ability to detect real malware variants with low false positives and logarithmic performance scalability, making it suitable for endhost adoption. Control flow graphs provide a more invariant characteristic than traditional static features like byte sequences for identifying polymorphic malware variants. Our system generates signatures for control flow graphs to efficiently compare programs and classify unknown samples.
Malware Classification Using Structured Control FlowSilvio Cesare
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.
Making Runtime Data Useful for Incident Diagnosis: An Experience ReportQAware GmbH
QuASD/PROFES 2018, Wolfsburg: Talk by Marcus Ciolkowski (@M_Ciolkowski, Principal IT Consultant at QAware) and Florian Lautenschlager (@flolaut, Senior Software Engineer)
=== Please download slides if blurred! ===
Abstract: Important and critical aspects of technical debt often surface at runtime only and are difficult to measure statically.
This is a particular challenge for cloud applications because of their highly distributed nature.
Fortunately, mature frameworks for collecting runtime data exist but need to be integrated.
In this paper, we report an experience from a project that implements a cloud application within Kubernetes on Azure.
To analyze the runtime data of this software system, we instrumented our services with Zipkin for distributed tracing; with Prometheus and Grafana for analyzing metrics; and with fluentd, Elasticsearch and Kibana for collecting, storing and exploring log files.
However, project team members did not utilize these runtime data until we created a unified and simple access using a chat bot.
We argue that even though your project collects runtime data, this is not sufficient to guarantee its usage: In order to be useful, a simple, unified access to different data sources is required that should be integrated into tools that are commonly used by team members.
Get the research paper: http://bitly.com/2QmSNwl
In the recent years, the traditional application monolith has broken down into a hefty chunk of micro-services thereby increasing the attack surface. We will look at how this increases the entry points into the complex modern day application ecosystem. The modern security tester needs various skills to pen-test such apps including the understanding of containers to successfully break or defend such applications.
When we tie this with the fast paced devOps life cycles for applications and explore the challenges when scaling security for such applications across the organization.
Hence, this webinar discusses traditional and relatively newer methods of Pen-testing web applications. Thereby illustrating how the changing business requirements and Agile life cycles for applications affect Security testing for modern applications.
Key Takeaways:
- what do the traditional Pen testing/Security testing Techniques entail?
- How is the landscape for Applications changing and how it affects security testing?
- What are the key essentials for testing modern applications?
- what can be done to scaling Security Assessments(Testing) for Modern & Agile life cycles?
Dr. Fengmin Gong, Co-Founder and Chief Strategy Officer, presents why an ecosystem-based approach is necessary to defend against modern malware threats. Discussion continues with what it takes to implement cybersecurity using this approach. He also presents a number of use cases where multi-vendor products interacting in a security ecosystem provide the most effective protection for enterprises.
SF Bay Area Splunk User Group Meeting October 5, 2022Becky Burwell
Andrew D'Auria, the Director of Sales Engineering at Anvilogic, gave a presentation on modernizing threat detection engineering. He discussed problems with the current detection engineering process, including that it is slow, results in noisy alerts, and lacks coordination across tools. D'Auria proposed using Anvilogic's platform to build detections based on MITRE ATT&CK techniques and scenarios, correlate events of interest without code, and measure detection program effectiveness to improve security operations. He provided examples of how Anvilogic helped a financial client improve detections and reduce alerts.
Real time intrusion detection in network traffic using adaptive and auto-scal...Gobinath Loganathan
This document proposes an adaptive and auto-scaling stream processor called Wisdom to enable real-time intrusion detection in network traffic. Wisdom can dynamically optimize complex event processing (CEP) rules using hybrid optimization algorithms like particle swarm optimization and bisection. Tests show Wisdom can detect attacks like HTTP slow header denial of service and port scans with over 99.95% accuracy. Wisdom also allows functionally auto-scaling deployments of CEP rules to optimize resource usage.
IRJET - Survey on Malware Detection using Deep Learning MethodsIRJET Journal
This document discusses various machine learning methods for malware detection, including support vector machines (SVM), random forests, and decision trees. It provides an overview of each method and related works that have applied these techniques. Specifically, it examines analyses that used linear SVM, random forests on Android apps, and an improved decision tree algorithm to classify malware families. The document concludes that machine learning methods have become important for malware detection as signatures alone cannot keep up with new malware variants.
This document evaluates the performance of three machine learning algorithms - Naive Bayes, Random Forest, and Stochastic Gradient Boosting - for detecting DDoS attacks. The algorithms were tested on a dataset from the Canadian Institute for Cybersecurity containing normal and attack network traffic. Stochastic Gradient Boosting achieved 100% accuracy, precision, recall and F1 score, outperforming the other algorithms. However, it had the longest execution time of 5.87 seconds compared to 1.55 seconds for Random Forest and 0.037 seconds for Naive Bayes. In conclusion, Stochastic Gradient Boosting was the most accurate for classifying DDoS attacks, but took significantly longer to run than the other models.
This document describes how a robot assessor can automate the process of vulnerability assessments by executing common security tools. The robot assessor uses heuristics to discover services on a target, determine which tools to run, execute those tools via APIs, and record the results. This allows vulnerability assessments to be initiated with a single command, freeing up analysts to focus on analysis rather than repetitive tasks. Several examples are provided of how the robot assessor would automate running tools like nmap, Nikto, sqlmap, and more.
Machine learning techniques applied to detect cyber attacks on web applicationsVenkat Projects
This document discusses using machine learning techniques to detect cyber attacks on web applications. It proposes using a graph-based approach and regular expressions to model normal HTTP request behavior during a learning phase. This would establish a baseline for detecting anomalies and potential attacks. Existing approaches are also reviewed that use algorithms like NSG, LSEG and F-Sign to generate signatures for detecting malware based on network traffic patterns or software code. Supervised machine learning methods have also been applied using features extracted from network data and classifiers like k-NN, Naive Bayes and neural networks. The proposed system would adapt this machine learning paradigm to specifically detect attacks on web applications.
Machine learning techniques applied to detect cyber attacks on web applicationsVenkat Projects
Machine learning techniques applied to detect cyber attacks on web applications
The increased usage of cloud services, growing number of web applications users, changes in network infrastructure that connects devices running mobile operating systems and constantly evolving network technology cause novel challenges for cyber security. As a result, to counter arising threats, network security mechanisms, sensors and protection schemes also have to evolve, to address the needs and problems of the users. In this article, we focus on countering emerging application layer cyber attacks since those are listed as top threats and the main challenge for network and cyber security. The major contribution of the article is the proposition of machine learning approach to model normal behaviour of application and to detect cyber attacks. The model consists of patterns (in form of Perl Compatible Regular Expressions (PCRE) regular expressions) that are obtained using graph-based segmentation technique and dynamic programming. The model is based on information obtained from HTTP requests generated by client to a web server. We have evaluated our method on CSIC 2010 HTTP Dataset achieving satisfactory results.
This document provides an overview of Continuum Analytics and Python for data science. It discusses how Continuum created two organizations, Anaconda and NumFOCUS, to support open source Python data science software. It then describes Continuum's Anaconda distribution, which brings together 200+ open source packages like NumPy, SciPy, Pandas, Scikit-learn, and Jupyter that are used for data science workflows involving data loading, analysis, modeling, and visualization. The document outlines how Continuum helps accelerate adoption of data science through Anaconda and provides examples of industries using Python for data science.
Data flow analysis is a type of static code analysis that examines how values are propagated through a program. It is more effective than pattern matching or regular static analysis at finding defects related to interactions between methods and classes that may be difficult to uncover through testing alone. Static analysis tools using data flow analysis can simulate execution paths to detect potential issues without requiring the code to be compiled and run. Developers are encouraged to use static testing tools to catch defects early in development, as prevention of bugs is more efficient than finding and fixing them later.
[IITB BTP 2015 Dec] Dynamic detection of malware in android OS.pptxDeepanjanKundu2
Malware is increasingly becoming a serious threat and a nuisance in the information and network age. Human experts have to extract (involves complex analysis of encrypted and/or packed binaries) a signature (usually a text pattern) of the malware and deploy it, to protect against a malware. However, this approach does not work for polymorphic and metamorphic malware, which have the ability to change shape from attack to attack; also, metamorphic virus detection even assuming fixed length is NP-complete. There have been numerous static code checking methods which are trying to detect anomalies and report the presence of malware. But in the current time there are numerous code obfuscation techniques which help them get away undetected. What we need is a dynamic method which reads through the processes run by the apps and detects unwanted behavior and reports it Android Systems have …
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...Silvio Cesare
This document describes two web services called Simseer and Bugwise for software defect detection and similarity analysis. Simseer performs malware variant and plagiarism detection by generating control flow signatures and comparing similarities. Bugwise detects bugs like double frees through decompilation and data flow analysis. The services are implemented through a PHP frontend and C++ backend called Malwise that performs analysis through plugins. Initial results found the web services had minimal overhead compared to command line usage.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
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
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Malware_SmartCom_2017
1. Effective Malware Detection based on
Behavior and Data Features
Zhiwu Xu, Cheng Wen, Shengchao Qin, and Zhong Ming
College of Computer Science and Software Engineering,
Shenzhen University, China
3. Malware
Malicious software:
Computer viruses, worms, Trojan
horses, ransomware, spyware, adware,
scareware, and other intrusive codes
Recent report from McAfee:
More than 650 million malware samples
detected in Q1, 2017, in which more than
30 million ones are new.
4. Signature-based method
To compare with the known signatures,
Comodo, McAfee, Kaspersky, Kingsoft, and
Symantec
Can be easily evaded by the evasion techniques
packing, variable-renaming, and
polymorphism.
5. Heuristic-based method
To identity malicious patterns though either
static analysis or dynamic analysis
However, heavy-weight or Inefficient
6. Machine learning approaches
Most of existing work focus on behaviour
features, without data information
binary codes, opcodes and API calls
Can be easily evaded
previously-unseen behaviors
obfuscate
8. Our approaches
Based on machine learning
Consider both the behaviour information and
the data information.
Consider the time-split samples and obfuscated
samples
13. Feature Extractor
Decompilation
Information Extraction
Feature Selection and representation
Selection:
Term Frequency and Inverse Document Frequency (TF-IDF)
Representation:
15. Classifier
Classifier Training
An executable 𝑒 can be represented as a vector 𝑥. 𝐷0
represent the available dataset with known categories. Our
training problem is to find a classifier 𝐶: 𝑋 → [0,1] such that
𝑚𝑖𝑛
𝑥,𝑐 ∈𝐷0
𝑑 𝐶 𝑥 − 𝑐
Malware Detection
Given an executable 𝑒 and its vector representation, the
goal of the detection is to find 𝑐 such that
min 𝑑 𝐶 𝑥 − 𝑐
21. Time-Split Experiment
We use some fresh malware samples, which
were collected dated from January 2017 to July
2017, from the DAS MALWERK website.
22. Obfuscation Experiments
Obfuscation tools:Obfuscator
Change code execution flow
Obfuscation tools:Unest
rewriting digital changes equivalently
confusing the output string
pushing the target code segment into the stack and jumping
to it to confuse the target code
obfuscating the static libraries
24. Conclusion
Machine learning methods based on the opcodes,
data types and system libraries.
Carried out some interesting experiments.
Capable of detecting some fresh malware
Has a resistance to some obfuscation techniques
Thank you for the Session Chair. I am very honor to have this opportunity to attend this conference. The topic of my paper is “Effective Malware Detection based on Behavior and Data Features”. I am the speaker Cheng Wen. This work is done with Zhiwu Xu, Shengchao Qin and Zhong Ming.
The outline of my talk as follows. In the first part I want to introduce the background of this research. The second part present our approach. Followed by experiments. Finally, a simple conclusion is given.
Well, let’s move on the first part of this topic.
Malware is a generic term that encompasses viruses, Worm, spywares and other intrusive codes. They are spreading all over the world and are increasing day by day, thus becoming a serious threat. According to the recent report from McAfee, there are more than 650 million malware samples detected in the first quarter, 2017, in which more than 30 million ones are new. So the detection of malware is of major concern to both the anti-malware industry and researchers.
To protect users from these threats, anti-malware software products from different companies provide the major defense against malware, such as Comodo, McAfee and so on, wherein the signature-based method is employed. However, this method can be easily evaded by malware writers through the evasion techniques.
To overcome the limitation of the signature-based method, heuristic-based method are proposed, which focuses on identifying the malicious behavior patterns, though either static analysis or dynamic analysis. But the
increasing number of malware samples makes this method no longer considered effective.
Recently, various machine learning approaches have been proposed for detecting malware. Although some approaches can get a high accuracy (for the stationary data sets), it is still not enough for malware detection. Most of existing work focus on the behaviour features such as binary codes, opcodes and API calls, leaving the data information out of consideration.
Also, It can be easily evaded. Malware evolves rapidly and it thus becomes hard to generalize learning models to reflect future, previously-unseen behaviors. And most of the work didn’t consider the resistance to obfuscation techniques.
Next, Let’s move to the second part.
In this paper, we propose an effective approach to detect malware based on machine learning. Different from most existing work, we take into account not only the behaviour information but also the data information. We also consider the time-split samples and obfuscated samples,
Generally, the behaviour information reflects which behaviours a software intends to do, while the data information indicates which data's a software intends to perform on or how data's are organized.
This Figure shows the framework of our approach, which consists of two components, namely the feature extractor and the malware classifier. The feature extractor extracts the feature information from the executables and represents them as vectors. While the malware classifier is first trained from an available dataset of executables, and then can be used to detect new, unseen executables. In the following, we describe both components in more detail.
Feature extractor is consists of the 3 steps, Decompilation, Information Extraction and Feature Selection and representation
An instruction or a data in an executable file can be represented as a series of binary codes, which are clearly not easy to read. So the first step is to transform the binary codes into a readable intermediate representation such as assembly codes by a decompilation tool.
Next, the extractor parses the asm files to extract the information, namely, opcodes, data types and system libraries. Generally, the opcodes used in an executable represent its intended
behaviours, while the data types indicate the structures of the datas it may perform on. In addition, the imported system libraries, which reflect the interaction between the executable and system. All these information describes the possible mission of an executable in some sense, and similar executables share the similar information.
We use the well-known scheme TF-IDF method to measure the statistical dependence. Next the extractor select the top k weight terms. Each executable can be represented as a vector. An example of vector is shown in the following.
Another component is malware classifier.
As mentioned before, we will first train our malware classifier from an available dataset of executables with known categories by a supervised machine-learning method, and then use it detect new, unseen executables.
Followed by the experiments
Our dataset consists of malwares and benigns. The malware dataset consists of the samples from BIG 2015 Challenge and from theZoo aka Malware DB, while the benign software are collected from 360 software company. We use various machine learning method to train a classifier and performed some experiments to test our approach’s ability.
To evaluate the performance of our approach, we conducted 10-fold cross validation experiments. The learning methods we used in our experiments are listed in the table. Concerning ROC curve, most classifiers can produce much better classification results.
Meanwhile, we counted the training times and the testing times in seconds for each cross validation experiment. The results are shown in this table. We also evaluated how the feature extractor perform. Both the decompilation time and the extracting time are acceptable.
Next, we also performed experiments based on each kind of feature to see their effectiveness. For that, we conducted the same experiments as above for each kind of feature. From the results we can see that all the features are effective to detect malware, and using all of the features together produced the best results. The opcode and library features have been used by lots of work in practice, so we believe that the type information can benefit to malware detection as well in practice.
In this section, to test our approach’s ability to detect genuinely new malware or new malware versions, we ran a time split experiment. First, we downloaded the malware samples, which were collected from January 2017 to July 2017. That is to say, all the malware samples are newer than the ones in our data set.
About 81% of the samples can be detected by our classifier, which estimates that our approach can detect some new malware samples or new versions of existing malware samples. However, the results also indicate that the classifier becomes ineffective as time passes. This suggests that malware classifiers should be updated often with new data or new features in order to maintain the classification accuracy.
One reason to make the malware detection difficult is that malware writers can use obfuscation techniques. In this section, we performed some experiments to test our approach’s ability to detect new malware samples that are obtained by obfuscating the existing ones.
We use two commercial tool, Obfuscator and Unest, to obfuscate some malware samples, which are randomly selected from our data set.
The results show that all the obfuscated malware samples can be detected by our classifier. That is to say, our classifier has a resistance to some obfuscation techniques.
At last, I conclude the talk.
In this work, we have proposed a malware detection approach using various machine learning methods based on the opcodes, data types and system libraries.
To evaluate the proposed approach, we have carried out some interesting experiments.
The experimental results have demonstrated that our classier is capable of detecting some fresh malware, and has a resistance to some obfuscation techniques.
We use static analysis. In malware detection, both static analysis and dynamic analysis have their own advantages and limitations.
In real application, we suggest using static analysis at first. If the file cannot be will-represented, then we can try dynamic analysis.