A zero-day (also known as 0-day) vulnerability is a computer-software vulnerability that is unknown to those who would be interested in mitigating the vulnerability.
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.We suggested different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features . A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
Application of data mining based malicious code detection techniques for dete...UltraUploader
This document discusses using data mining techniques to detect spyware. It applies Naive Bayes algorithms used in previous work to detect viruses to a dataset of 312 benign and 614 spyware executables. Feature extraction examines byte sequences in files. Initial tests showed low accuracy, but removing Trojan programs from the dataset improved results, with a window size of 4 and without Trojans achieving 80% detection with a 4% false positive rate. Future work proposes testing against larger window sizes and obfuscated code.
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...Yandex
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.
AI approach to malware similarity analysis: Maping the malware genome with a...Priyanka Aash
In recent years, cyber defenders protecting enterprise networks have started incorporating malware code sharing identification tools into their workflows. These tools compare new malware samples to a large databases of known malware samples, in order to identify samples with shared code relationships. When unknown malware binaries are found to share code "fingerprints" with malware from known adversaries, they provides a key clue into which adversary is generating these new binaries, thus helping develop a general mitigation strategy against that family of threats. The efficacy of code sharing identification systems is demonstrated every day, as new family of threats are discovered, and countermeasures are rapidly developed for them. Unfortunately, these systems are hard to maintain, deploy, and adapt to evolving threats. First and foremost, these systems do not learn to adapt to new malware obfuscation strategies, meaning they will continuously fall out of date with adversary tradecraft, requiring, periodically, a manually intensive tuning in order to adjust the formulae used for similarity between malware. In addition, these systems require an up to date, well maintained database of recent threats in order to provide relevant results. Such a database is difficult to deploy, and hard and expensive to maintain for smaller organizations. In order to address these issues we developed a new malware similarity detection approach. This approach, not only significantly reduces the need for manual tuning of the similarity formulate, but also allows for significantly smaller deployment footprint and provides significant increase in accuracy. Our family/similarity detection system is the first to use deep neural networks for code sharing identification, automatically learning to see through adversary tradecraft, thereby staying up to date with adversary evolution. Using traditional string similarity features our approach increased accuracy by 10%, from 65% to 75%. Using an advanced set of features that we specifically designed for malware classification, our approach has 98% accuracy. In this presentation we describe how our method works, why it is able to significantly improve upon current approaches, and how this approach can be easily adapted and tuned to individual/organization needs of the attendees.
(Source: Black Hat USA 2016, Las Vegas)
Malware classification using Machine LearningJapneet Singh
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.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a
software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152
malware samples belonging to 16 families and 523 benign samples.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.We suggested different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features . A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
Application of data mining based malicious code detection techniques for dete...UltraUploader
This document discusses using data mining techniques to detect spyware. It applies Naive Bayes algorithms used in previous work to detect viruses to a dataset of 312 benign and 614 spyware executables. Feature extraction examines byte sequences in files. Initial tests showed low accuracy, but removing Trojan programs from the dataset improved results, with a window size of 4 and without Trojans achieving 80% detection with a 4% false positive rate. Future work proposes testing against larger window sizes and obfuscated code.
"Быстрое обнаружение вредоносного ПО для Android с помощью машинного обучения...Yandex
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.
AI approach to malware similarity analysis: Maping the malware genome with a...Priyanka Aash
In recent years, cyber defenders protecting enterprise networks have started incorporating malware code sharing identification tools into their workflows. These tools compare new malware samples to a large databases of known malware samples, in order to identify samples with shared code relationships. When unknown malware binaries are found to share code "fingerprints" with malware from known adversaries, they provides a key clue into which adversary is generating these new binaries, thus helping develop a general mitigation strategy against that family of threats. The efficacy of code sharing identification systems is demonstrated every day, as new family of threats are discovered, and countermeasures are rapidly developed for them. Unfortunately, these systems are hard to maintain, deploy, and adapt to evolving threats. First and foremost, these systems do not learn to adapt to new malware obfuscation strategies, meaning they will continuously fall out of date with adversary tradecraft, requiring, periodically, a manually intensive tuning in order to adjust the formulae used for similarity between malware. In addition, these systems require an up to date, well maintained database of recent threats in order to provide relevant results. Such a database is difficult to deploy, and hard and expensive to maintain for smaller organizations. In order to address these issues we developed a new malware similarity detection approach. This approach, not only significantly reduces the need for manual tuning of the similarity formulate, but also allows for significantly smaller deployment footprint and provides significant increase in accuracy. Our family/similarity detection system is the first to use deep neural networks for code sharing identification, automatically learning to see through adversary tradecraft, thereby staying up to date with adversary evolution. Using traditional string similarity features our approach increased accuracy by 10%, from 65% to 75%. Using an advanced set of features that we specifically designed for malware classification, our approach has 98% accuracy. In this presentation we describe how our method works, why it is able to significantly improve upon current approaches, and how this approach can be easily adapted and tuned to individual/organization needs of the attendees.
(Source: Black Hat USA 2016, Las Vegas)
Malware classification using Machine LearningJapneet Singh
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.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a
software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152
malware samples belonging to 16 families and 523 benign samples.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
Malware Detection Using Machine Learning TechniquesArshadRaja786
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...
Cognitive computing in security uses AI to help security analysts understand threats better. It can analyze large amounts of structured and unstructured security data to find patterns humans may miss. This helps address gaps in speed, accuracy, and intelligence for security teams overwhelmed by data. IBM's Watson for Cyber Security ingests security knowledge from sources like reports, blogs, and alerts. It builds a knowledge graph to help analysts investigate incidents faster, from minutes to hours instead of days to weeks. The cognitive system can reduce the skills gap and workload for analysts.
Integrated Feature Extraction Approach Towards Detection of Polymorphic Malwa...CSCJournals
Some malware are sophisticated with polymorphic techniques such as self-mutation and emulation based analysis evasion. Most anti-malware techniques are overwhelmed by the polymorphic malware threats that self-mutate with different variants at every attack. This research aims to contribute to the detection of malicious codes, especially polymorphic malware by utilizing advanced static and advanced dynamic analyses for extraction of more informative key features of a malware through code analysis, memory analysis and behavioral analysis. Correlation based feature selection algorithm will be used to transform features; i.e. filtering and selecting optimal and relevant features. A machine learning technique called K-Nearest Neighbor (K-NN) will be used for classification and detection of polymorphic malware. Evaluation of results will be based on the following measurement metrics-True Positive Rate (TPR), False Positive Rate (FPR) and the overall detection accuracy of experiments.
This document describes PIndroid, a novel Android malware detection system that uses permissions and intents extracted from app manifest files along with ensemble learning methods. PIndroid analyzes apps' use of permissions, which provide access to device resources, and intents, which enable communication. It extracts these features, preprocesses the data, and classifies apps using multiple machine learning classifiers. PIndroid achieves high detection accuracy while requiring less analysis time than other approaches. The system is effective at identifying malware families and could also detect colluding apps.
The document discusses different techniques for anomaly detection in intrusion detection systems. It describes four main components of a typical anomaly detection model: data collection, normal system profiling, anomaly detection, and response. It then discusses the advantages and limitations of anomaly detection. The rest of the document summarizes various statistical, rule-based, biological, and learning models used for anomaly detection, including their key principles and examples like Haystack, NIDES, EMERALD, Wisdom & Sense, and Network Security Monitor.
APPBACS: AN APPLICATION BEHAVIOR ANALYSIS AND CLASSIFICATION SYSTEMijcsit
Number and complicacy of malware attack has increased multiple folds in recent times. Informed Internet
users generally keep their computer protected but get confused when it comes to execute the untrusted
applications. In such cases users may fall prey to malicious applications. There are malware behavior
analyzers available but leave report analysis to the user. Common users are not trained to understand and
analyze these reports, and generally expect direct recommendation whether to execute this application on
their computer. This research paper tries to analyze behavior and help the common users and analysts to
quickly classify an application as safe or malicious.
IRJET- Android Malware Detection using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to detect Android malware. It aims to extract features from Android applications (APKs) and train machine learning models to classify APKs as malware or benign. The proposed approach extracts features from an APK's manifest file and decompiled code to identify permissions, URLs, API calls, and other indicators. Random forest classifiers are trained on a dataset of benign and malicious APKs to detect known malware families. The models can classify new APKs as either malware or benign, and if malware, identify the specific malware family. The approach aims to detect malware with high accuracy while reducing analysis time by processing multiple APKs in parallel.
The proposed solution uses dynamic analysis to identify behavioral patterns and sequences of malware samples. It extracts these patterns using a Cuckoo analysis environment and stores them in a repository using the MAEC language. The detection system then executes suspicious binaries and compares the observed behaviors in real-time to the known malware patterns using ANN classification. This allows detection of novel malware and helps defeat polymorphism, improving over static analysis approaches. The framework is evaluated using the public VXHeaven malware dataset to compare results with the anchor paper, overcoming some of its limitations like inability to decrypt all samples.
Automated classification and analysis of internet malwareUltraUploader
The document summarizes research on analyzing how existing anti-virus software classifies malware. It finds that anti-virus products provide labels for malware that are inconsistent across products, incomplete in covering all malware, and lack concise semantics. To address these limitations, the research proposes a new technique for classifying malware based on its behavior and system changes, and automatically grouping similar behaviors. It evaluates the approach using large and diverse malware datasets.
Applications of genetic algorithms to malware detection and creationUltraUploader
This document summarizes and analyzes previous research on applying genetic algorithms to malware detection and creation. Section 2 summarizes a paper that compared the performance of genetic algorithm-based classifiers to non-genetic classifiers for detecting malware. It found genetic algorithms performed comparably to other methods in classification accuracy but with lower processing overhead. Sections 3 and 4 summarize papers applying genetic algorithms to optimize parameters for real-time malware detection and to evolve malware signatures similar to antibodies. Section 5 discusses using genetic algorithms to evolve malware. The document analyzes the effectiveness of genetic algorithms for malware detection tasks and issues around using them to evolve malware.
Analysis of Malware Infected Systems & Classification with Gradient-boosted T...Darshan Gorasiya
Analysis of Malware Infected Systems with MapReduce, Pig, Hive, SparkSQL & Classification with Spark MLlib Gradient-boosted Tree on Big Data Platform (Hadoop)
IRJET- Android Malware Detection using Deep LearningIRJET Journal
This document summarizes a research paper that proposes a machine learning model to detect Android malware. It extracts permission data from a large dataset of benign and malicious Android apps. A deep learning model is trained on the permission data to classify unknown apps as benign or malicious. The model achieves 88% accuracy on the test data, which is higher than other techniques. However, it may be vulnerable to encryption techniques used by some malware to evade detection.
IRJET- Discovery of Fraud Apps Utilizing Sentiment AnalysisIRJET Journal
This document discusses a proposed system for detecting fraudulent mobile apps through sentiment analysis. It begins with an introduction describing the problem of ranking fraud in app markets and an overview of the proposed solution. It then reviews related work on ranking spam detection, online review spam detection, and mobile app recommendation. The proposed system is described as using natural language processing on app reviews, comments and data to identify ranking, rating and behavior patterns that could indicate fraud. The goals are outlined as detecting ranking fraud in mobile apps and improving fraud detection efficiency. Key components of the system include mining app historical ranking data to identify periods of abnormal high ranking that could be fraudulent. Multiple sources of evidence like ranking, rating and review-based patterns would be analyzed and aggregated to
Data mining techniques for malware detection.pptxAditya Deshmukh
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.
Classification of Malware based on Data Mining Approachijsrd.com
This document discusses a system called the Intelligent Malware Detection System (IMDS) that uses data mining techniques to classify malware. The IMDS uses a PE parser to extract API execution sequences from Windows portable executable files. It then applies an OOA mining algorithm called OOA_Fast_FP-Growth to generate association rules from the API sequences to classify files as malware or benign. Experimental results showed the IMDS outperformed other classification techniques and anti-virus software in detecting malware.
A feature selection and evaluation scheme for computer virus detectionUltraUploader
This document proposes a feature selection and evaluation scheme for computer virus detection using machine learning. It presents an exhaustive search method to identify generic n-gram features from virus code, selecting features that meet minimum support thresholds within and across virus families. A hierarchical feature selection process is described to obtain concise yet representative features. The evaluation method aims to simulate detecting new virus outbreaks by testing the classifier on previously unseen viruses from the same families not in the training set.
Malware is malicious software that can compromise computer functions, steal data, bypass access controls, or otherwise cause harm. It includes viruses, trojans, rootkits, worms, spyware, adware, and spam. Viruses infect files while worms self-replicate and spread. Antivirus software uses signature scanning, heuristic analysis, and integrity checking to detect malware, but must constantly update signatures to address new threats. Effective prevention requires keeping software and security updates current, using caution when clicking links or entering information online, and avoiding unsecured networks.
Machine Learning for Malware Classification and ClusteringAshwini Almad
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.
ANALYTIC HIERARCHY PROCESS-BASED FUZZY MEASUREMENT TO QUANTIFY VULNERABILITIE...IJCNCJournal
Much research has been conducted to detect vulnerabilities of Web Applications; however, these never proposed a methodology to measure the vulnerabilities either qualitatively or quantitatively. In this paper, a methodology is proposed to investigate the quantification of vulnerabilities in Web Applications. We applied the Goal Question Metrics (GQM) methodology to determine all possible security factors and subfactors of Web Applications in the Department of Transportation (DOT) as our proof of concept. Then we
introduced a Multi-layered Fuzzy Logic (MFL) approach based on the security sub-factors’ prioritization in the Analytic Hierarchy Process (AHP). Using AHP, we weighted each security sub-factor before the quantification process in the Fuzzy Logic to handle imprecise crisp number calculation.
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSijaia
This document presents a static malware detection system using data mining techniques. The system extracts raw features from Windows Portable Executable (PE) files including PE header information, DLLs, and API functions. It then selects important features using Information Gain and reduces dimensions using Principal Component Analysis. Three classifiers (SVM, J48, Naive Bayes) are trained on the transformed feature vectors to classify files as malicious or benign. When evaluated on a dataset of over 247,000 files, the system achieved a detection rate of 99.6%.
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
Malware Detection Using Machine Learning TechniquesArshadRaja786
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...
Cognitive computing in security uses AI to help security analysts understand threats better. It can analyze large amounts of structured and unstructured security data to find patterns humans may miss. This helps address gaps in speed, accuracy, and intelligence for security teams overwhelmed by data. IBM's Watson for Cyber Security ingests security knowledge from sources like reports, blogs, and alerts. It builds a knowledge graph to help analysts investigate incidents faster, from minutes to hours instead of days to weeks. The cognitive system can reduce the skills gap and workload for analysts.
Integrated Feature Extraction Approach Towards Detection of Polymorphic Malwa...CSCJournals
Some malware are sophisticated with polymorphic techniques such as self-mutation and emulation based analysis evasion. Most anti-malware techniques are overwhelmed by the polymorphic malware threats that self-mutate with different variants at every attack. This research aims to contribute to the detection of malicious codes, especially polymorphic malware by utilizing advanced static and advanced dynamic analyses for extraction of more informative key features of a malware through code analysis, memory analysis and behavioral analysis. Correlation based feature selection algorithm will be used to transform features; i.e. filtering and selecting optimal and relevant features. A machine learning technique called K-Nearest Neighbor (K-NN) will be used for classification and detection of polymorphic malware. Evaluation of results will be based on the following measurement metrics-True Positive Rate (TPR), False Positive Rate (FPR) and the overall detection accuracy of experiments.
This document describes PIndroid, a novel Android malware detection system that uses permissions and intents extracted from app manifest files along with ensemble learning methods. PIndroid analyzes apps' use of permissions, which provide access to device resources, and intents, which enable communication. It extracts these features, preprocesses the data, and classifies apps using multiple machine learning classifiers. PIndroid achieves high detection accuracy while requiring less analysis time than other approaches. The system is effective at identifying malware families and could also detect colluding apps.
The document discusses different techniques for anomaly detection in intrusion detection systems. It describes four main components of a typical anomaly detection model: data collection, normal system profiling, anomaly detection, and response. It then discusses the advantages and limitations of anomaly detection. The rest of the document summarizes various statistical, rule-based, biological, and learning models used for anomaly detection, including their key principles and examples like Haystack, NIDES, EMERALD, Wisdom & Sense, and Network Security Monitor.
APPBACS: AN APPLICATION BEHAVIOR ANALYSIS AND CLASSIFICATION SYSTEMijcsit
Number and complicacy of malware attack has increased multiple folds in recent times. Informed Internet
users generally keep their computer protected but get confused when it comes to execute the untrusted
applications. In such cases users may fall prey to malicious applications. There are malware behavior
analyzers available but leave report analysis to the user. Common users are not trained to understand and
analyze these reports, and generally expect direct recommendation whether to execute this application on
their computer. This research paper tries to analyze behavior and help the common users and analysts to
quickly classify an application as safe or malicious.
IRJET- Android Malware Detection using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to detect Android malware. It aims to extract features from Android applications (APKs) and train machine learning models to classify APKs as malware or benign. The proposed approach extracts features from an APK's manifest file and decompiled code to identify permissions, URLs, API calls, and other indicators. Random forest classifiers are trained on a dataset of benign and malicious APKs to detect known malware families. The models can classify new APKs as either malware or benign, and if malware, identify the specific malware family. The approach aims to detect malware with high accuracy while reducing analysis time by processing multiple APKs in parallel.
The proposed solution uses dynamic analysis to identify behavioral patterns and sequences of malware samples. It extracts these patterns using a Cuckoo analysis environment and stores them in a repository using the MAEC language. The detection system then executes suspicious binaries and compares the observed behaviors in real-time to the known malware patterns using ANN classification. This allows detection of novel malware and helps defeat polymorphism, improving over static analysis approaches. The framework is evaluated using the public VXHeaven malware dataset to compare results with the anchor paper, overcoming some of its limitations like inability to decrypt all samples.
Automated classification and analysis of internet malwareUltraUploader
The document summarizes research on analyzing how existing anti-virus software classifies malware. It finds that anti-virus products provide labels for malware that are inconsistent across products, incomplete in covering all malware, and lack concise semantics. To address these limitations, the research proposes a new technique for classifying malware based on its behavior and system changes, and automatically grouping similar behaviors. It evaluates the approach using large and diverse malware datasets.
Applications of genetic algorithms to malware detection and creationUltraUploader
This document summarizes and analyzes previous research on applying genetic algorithms to malware detection and creation. Section 2 summarizes a paper that compared the performance of genetic algorithm-based classifiers to non-genetic classifiers for detecting malware. It found genetic algorithms performed comparably to other methods in classification accuracy but with lower processing overhead. Sections 3 and 4 summarize papers applying genetic algorithms to optimize parameters for real-time malware detection and to evolve malware signatures similar to antibodies. Section 5 discusses using genetic algorithms to evolve malware. The document analyzes the effectiveness of genetic algorithms for malware detection tasks and issues around using them to evolve malware.
Analysis of Malware Infected Systems & Classification with Gradient-boosted T...Darshan Gorasiya
Analysis of Malware Infected Systems with MapReduce, Pig, Hive, SparkSQL & Classification with Spark MLlib Gradient-boosted Tree on Big Data Platform (Hadoop)
IRJET- Android Malware Detection using Deep LearningIRJET Journal
This document summarizes a research paper that proposes a machine learning model to detect Android malware. It extracts permission data from a large dataset of benign and malicious Android apps. A deep learning model is trained on the permission data to classify unknown apps as benign or malicious. The model achieves 88% accuracy on the test data, which is higher than other techniques. However, it may be vulnerable to encryption techniques used by some malware to evade detection.
IRJET- Discovery of Fraud Apps Utilizing Sentiment AnalysisIRJET Journal
This document discusses a proposed system for detecting fraudulent mobile apps through sentiment analysis. It begins with an introduction describing the problem of ranking fraud in app markets and an overview of the proposed solution. It then reviews related work on ranking spam detection, online review spam detection, and mobile app recommendation. The proposed system is described as using natural language processing on app reviews, comments and data to identify ranking, rating and behavior patterns that could indicate fraud. The goals are outlined as detecting ranking fraud in mobile apps and improving fraud detection efficiency. Key components of the system include mining app historical ranking data to identify periods of abnormal high ranking that could be fraudulent. Multiple sources of evidence like ranking, rating and review-based patterns would be analyzed and aggregated to
Data mining techniques for malware detection.pptxAditya Deshmukh
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.
Classification of Malware based on Data Mining Approachijsrd.com
This document discusses a system called the Intelligent Malware Detection System (IMDS) that uses data mining techniques to classify malware. The IMDS uses a PE parser to extract API execution sequences from Windows portable executable files. It then applies an OOA mining algorithm called OOA_Fast_FP-Growth to generate association rules from the API sequences to classify files as malware or benign. Experimental results showed the IMDS outperformed other classification techniques and anti-virus software in detecting malware.
A feature selection and evaluation scheme for computer virus detectionUltraUploader
This document proposes a feature selection and evaluation scheme for computer virus detection using machine learning. It presents an exhaustive search method to identify generic n-gram features from virus code, selecting features that meet minimum support thresholds within and across virus families. A hierarchical feature selection process is described to obtain concise yet representative features. The evaluation method aims to simulate detecting new virus outbreaks by testing the classifier on previously unseen viruses from the same families not in the training set.
Malware is malicious software that can compromise computer functions, steal data, bypass access controls, or otherwise cause harm. It includes viruses, trojans, rootkits, worms, spyware, adware, and spam. Viruses infect files while worms self-replicate and spread. Antivirus software uses signature scanning, heuristic analysis, and integrity checking to detect malware, but must constantly update signatures to address new threats. Effective prevention requires keeping software and security updates current, using caution when clicking links or entering information online, and avoiding unsecured networks.
Machine Learning for Malware Classification and ClusteringAshwini Almad
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.
ANALYTIC HIERARCHY PROCESS-BASED FUZZY MEASUREMENT TO QUANTIFY VULNERABILITIE...IJCNCJournal
Much research has been conducted to detect vulnerabilities of Web Applications; however, these never proposed a methodology to measure the vulnerabilities either qualitatively or quantitatively. In this paper, a methodology is proposed to investigate the quantification of vulnerabilities in Web Applications. We applied the Goal Question Metrics (GQM) methodology to determine all possible security factors and subfactors of Web Applications in the Department of Transportation (DOT) as our proof of concept. Then we
introduced a Multi-layered Fuzzy Logic (MFL) approach based on the security sub-factors’ prioritization in the Analytic Hierarchy Process (AHP). Using AHP, we weighted each security sub-factor before the quantification process in the Fuzzy Logic to handle imprecise crisp number calculation.
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSijaia
This document presents a static malware detection system using data mining techniques. The system extracts raw features from Windows Portable Executable (PE) files including PE header information, DLLs, and API functions. It then selects important features using Information Gain and reduces dimensions using Principal Component Analysis. Three classifiers (SVM, J48, Naive Bayes) are trained on the transformed feature vectors to classify files as malicious or benign. When evaluated on a dataset of over 247,000 files, the system achieved a detection rate of 99.6%.
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
Abstract: The exponential growth of the internet and new technology lead today's world in a hectic situation both positive as well as the negative module. Cybercriminals gamble in the dark net using numerous techniques. This leads to cybercrime. Cyber threats like Malware attempt to infiltrate the computer or mobile device offline or internet, chat(online), and anyone can be a potential target. Malware is also known as malicious software is often used by cybercriminals to achieve their goal by tracking internet activity, capturing sensitive information, or blocking computer access. Reverse engineering is one of the best ways to prevent and is a powerful tool to keep the fight against cyber attacks. Most people in the cyber world see it as a black hat—It is said as being used to steal data and intellectual property. But when it is in the hands of cybersecurity experts, reverse engineering dons the white hat of the hero. Looking at the program from the outside in –often by a third party that had no hand in writing the code. It allows those who practice it to understand how a given program or system works when no source code is available. Reverse engineering accomplishing several tasks related to cybersecurity: finding system vulnerabilities, researching malware &analyzing the complexity of restoring core software algorithms that can further protect against theft. It is hard to hack certain software.
Keywords: Malware, threat, vulnerablity, detection, reverse engineering, analysis.
Title: Malware analysis and detection using reverse Engineering
Author: B.Rashmitha, J. Alwina Beauty Angelin, E.R. Ramesh
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 10, Issue 2, Month: April 2022 - June 2022
Page: (1-4)
Published Date: 01-April-2022
Research Publish Journals
Available at: www.researchpublish.com
You can Direct download full research paper at given below link:
https://www.researchpublish.com/papers/malware-analysis-and-detection-using-reverse-engineering
Academia Link: https://www.academia.edu/76069664/Malware_analysis_and_detection_using_reverse_Engineering_Available_at_www_researchpublish_com_journal_name_International_Journal_of_Computer_Science_and_Information_Technology_Research
COMPARISON OF MALWARE CLASSIFICATION METHODS USING CONVOLUTIONAL NEURAL NETWO...IJNSA Journal
Malicious software is constantly being developed and improved, so detection and classification of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to detect new/unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the API (Application Program Interface) calls. This research uses a database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor, Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used a 1-Dimensional CNN by mapping API calls as categorical and term frequency-inverse document frequency (TF-IDF) vectors and compared the results to other classification techniques.The proposed 1-D CNN outperformed other classification techniques with 91% overall accuracy for both categorical and TF-IDF vectors.
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.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially overcome these deceits by observing the actual behaviour of the code execution. In this regard, various methods, techniques and tools have been proposed. However, because of the diverse concepts and strategies used in the implementation of these methods and tools, security researchers and malware analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s implementation strategy, analysis approach, system-wide analysis support and its overall handling of binaries, helping them to select a suitable and effective one for their study and analysis.
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...IJCI JOURNAL
With a text mining and bibliometrics approach, this study reviews the literature on the evolution
of malware classification using machine learning. This work takes literature from 2008 to 2022
on the subject of using machine learning for malware classification to understand the impact of
this technology on malware classification. Throughout this study, we seek to answer three main
research questions: RQ1: Is the application of machine learning for malware classification
growing? RQ2: What is the most common machine-learning application for malware
classification? RQ3: What are the outcomes of the most common machine learning
applications? The analysis of 2186 articles resulting from a data collection process from peerreviewed databases shows the trajectory of the application of this technology on malware
classification as well as trends in both the machine learning and malware classification fields of
study. This study performs quantitative and qualitative analysis using statistical and N-gram
analysis techniques and a formal literature review to answer the proposed research questions.
The research reveals methods such as support vector machines and random forests to be
standard machine learning methods for malware classification in efforts to detect maliciousness
or categorize malware by family. Machine learning is a highly researched technology with
many applications, from malware classification and beyond.
When dealing with over 300 hundred thousand of malware samples every day, we had to deploy the state-of-the-art techniques to combat cyberthreats. And among them - machine learning algorithms.
In this whitepaper, we start from describing the basic approaches and proceed to explaining the key applications of machine learning algorithms to automated malware detection. Learn more about how Kaspersky Lab protects businesses like yours => https://kas.pr/8dxv
MACHINE LEARNING APPROACH TO LEARN AND DETECT MALWARE IN ANDROIDIRJET Journal
This document discusses machine learning approaches for detecting malware in Android apps. It first classifies different types of malware like viruses, trojans, worms, spyware, adware, and ransomware. It then discusses important features for malware detection like n-grams, opcodes, strings, memory access, and API calls. The document reviews several papers on machine learning techniques for Android malware detection using methods like random forest, SVM, decision trees, and evaluating accuracy and efficiency. It proposes using ANN and SVM models to identify malicious and benign apps and providing a category-based machine learning approach to improve detection accuracy.
A trust system based on multi level virus detectionUltraUploader
This document summarizes a research paper that proposes a new multi-level virus detection system (MDS). The MDS uses three levels of protection: 1) A smart memory monitor that detects virus behavior in real-time, 2) A file checker that analyzes batch files for virus-like code, and 3) An integrity checker that stores file signatures to detect modifications where viruses typically infect. The system was tested and able to detect virus activity through monitoring, file analysis, and integrity checking at different levels simultaneously. The paper concludes the MDS approach provides improved virus detection over single-method systems.
Optimised Malware Detection in Digital Forensics IJNSA Journal
This summarizes a research paper that proposes developing a new framework to optimize malware detection in digital forensics investigations. The paper discusses challenges with existing detection methods, such as signature-based approaches requiring extensive manual analysis. Through a market research survey of forensics professionals, the paper finds weaknesses in current skills, tools, and accuracy rates. Most respondents agreed a new customized detection tool is needed that employs both dynamic and static analysis methods. The proposed framework aims to address these issues to more effectively detect and analyze malware.
Cyber Defense Forensic Analyst - Real World Hands-on ExamplesSandeep Kumar Seeram
The document discusses analyzing malware using static and dynamic analysis techniques. Static analysis involves examining a malware file's code and structure without executing it, using tools like disassemblers and string extractors. Dynamic analysis executes malware in a controlled environment to observe its behaviors and any changes it makes. The document then demonstrates analyzing the "Netflix Account Generator" malware using an isolated cloud sandbox, where it is observed starting child processes and making outbound network connections, suggesting it is a remote access trojan.
This document discusses machine learning approaches for Android malware detection. It begins with an abstract discussing signature-based and behavior-based malware detection techniques, and how machine learning can be used to detect unknown malware. The document then discusses related work on Android malware detection using machine learning algorithms. It describes detecting malware using file permissions and features extracted from Android applications. Various machine learning algorithms are trained on datasets of benign and malicious applications, and their performance is evaluated based on accuracy, classification reports, and confusion matrices.
Adware is a software that may be installed on the client machine for displaying advertisements for the
user of that machine with or without consideration of user. Adware can cause unrecoverable threat to the security
and privacy of computer users as there is an increase in number of malicious adware’s. The paper presents an
adware detection approach based on the application of data mining on disassembled code. This is an approach for
an accurate adware detection algorithm with adware data set and machine learning techniques. In this paper, we
disassemble binary files, generate instruction sequences and past his data through different data mining as well as
machine learning algorithms for feature extraction and feature reduction for detection of malicious adware.Then
system accurately detect both novel and known adware instances even though the binary difference between
adware and legitimate software is usually small.
Keywords — Data Mining; Adware Detection; Binary Classification; Static Analysis; Disassembly;
Instruction Sequences
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Malware Detection Using Data Mining Techniques Akash Karwande
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.
Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemIJRES Journal
With today’s world filled with information and data, it is very important for one to know which information or data is harmless and which is harmful. Right from cellular phones to big MNCs and Server companies require a security system that is as competent and adaptive as its ever-updating and evolving viruses or malware. The paper talks about the development and implementation of a new idea Adaptive anti-virus based on Anfis logic. An adaptive anti-virus system that will catch up to the speed at which the viruses update and evolve.
IRJET- Zombie - Venomous File: Analysis using Legitimate Signature for Securi...IRJET Journal
The document discusses a proposed method for detecting viruses and malware that evade existing antivirus software. It uses a combination of analyzing files with VirusTotal's database of known threats and applying natural language processing techniques like suffix trees and TF-IDF to identify malicious patterns in files. An evaluation shows the proposed method can detect viruses that existing antivirus and VirusTotal miss, achieving a 97% accuracy rate in testing.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Build the Next Generation of Apps with the Einstein 1 Platform.
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Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
2. What is a malware ?
Different malware analysis techniques.
What’s wrong with those techniques.
What’s this paper about ?
Proposed malware classification system.
Evaluation and validation.
Experimental result analysis.
Comparing accuracy of classifiers BFS and AFS.
Comparing of model building time BFS and AFS.
2
3. A software program that purposefully fulfils the harmful
intent of an attacker is usually known as malicious software
or malware.
3
5. The suspicious program is scanned with fully-automated
tools.
These tools are able to quickly assess what a malware is
capable of if it infiltrated the system.
Even though a fully-automated analysis does not provide as
much information as an analyst, it is still the fastest method
to sift through large quantities of malware.
5
6. The static properties include hashes, embedded strings,
embedded resources, and header information.The
properties should be able to show elementary indicators of
compromise.
6
7. To observe a malicious file, it might often times be put in an
isolated laboratory to see if it directly infects the
laboratory.
Analysts will frequently monitor these laboratories to see if
the malicious file tries to attach to any hosts.
With this information, the analyst will then be able to
replicate the situation.
7
8. Reversing the code of the malicious file can decode
encrypted data that was stored by the sample, and see
other capabilities of the file that did not show up during the
behavioral analysis.
In order to manually reverse the code, malware analysis
tools such as a debugger and disassembler are needed.
8
9. The main problem with these techniques are:
High false positive and false negative rates.
The process of building a classification model takes
time which hinders the early detection of malware.
9
10. This paper presents a system that addresses both the
issues mentioned before.
It uses an integration of both static and dynamic analysis
features of malware binaries incorporated with machine
learning process for detecting zero-day malware.
10
11. Due to pros and cons of the techniques mentioned before, it
is obvious that a relevant of features needs to be selected
so that the classification model can be built in less time
with high accuracy.
11
12. Feature selection is a method of identifying top ranked
features.
It detects the relevant features thus making it easy to
discard the irrelevant ones.
A perfect selection of features can improve the learning
speed as well as generalization capacity of the model.
12
14. A large corpus of malicious samples are collected and then
scanned using AVG AV to endorse their maliciousness.
The clean files used are collected manually from system
directories of successive versions of the respective
operating system.
14
15. All the collected specimen are then made to execute in an
automated analysis environment using a modified version
of Cuckoo sandbox.
The system is configured to generate the analysis reports
in JSON format after executing a specimen in it.
15
16. The JSON reports are then parsed to obtain the various
malware features including both static and dynamic
features.
The dataset so obtained contains very large number of
features and is not suitable for building the classification
model.
This data is prepared to have a reduced set of malware
attributes which can be used for building the classification
model.
16
17. Building a classification model from the training data is
time consuming task .
So, the top ranked features are selected from this reduced
data set using Information Gain (IG) method.
17
18. The selected features are then used to build the
classification model using ML algorithms.
These classifiers are used for distinguishing malicious files
from benign ones.
The model build time is observed while conducting the
experiments using both the datasets i.e. BFS and AFS.
18
20. The training data is required by the classification
algorithms to build the model while testing data is required
to test the models so built.
Validation is done by cross validation technique which is
used for evaluating the results generated by the
independent datasets.
The machine learning algorithms are evaluated by using
following performance measures
20
21. True positive rate (TPR): Rate of correctly identified
malicious files (also known as recall or sensitivity). It is a
measure of completeness or quality.
𝑇𝑃𝑅 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
21
29. [1] A. Moser, C. Kruegel, E. Kirda,“Exploring Multiple
Execution Paths for Malware Analysis,”Proc. of IEEE
Symposium on Security and Privacy, pp. 231-245. IEEE
Computer Society, USA, 2007, doi:10.1109/SP.2007.17.
[2] E. Gandotra, D. Bansal,S. Sofat,“Malware Analysis and
Classification: A Survey,” Journal of Information Security,
vol. 5, pp. 56-65, 2014.
[3] Internet Security Threat Report, Symantec,Volume 21,
April, 2016, [online]. Available:
https://www.symantec.com/content/dam/symantec/docs/
reports/istr21-2016-en.pdf
[4] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P.
Reutemann and I.Witten,“The WEKA Data Mining
Software: An Update,” ACM SIGKDD Explorations
Newsletter, vol. 11, no. 1 pp. 10-18, 2009.
[5] M. Schultz, E. Eskin, F. Zadok, and S. Stolfo,“Data
mining methods for detection of new malicious
executables,”Proc. of 2001 IEEE Symposium on Security
and Privacy, IEEE, Oakland, CA, 2001, pp. 38-49, Doi:
10.1109/SECPRI.2001.924286.
[6] J. Kolter, and M. Maloof,“Learning to detect malicious
executables in the wild,” Proc. of the 10th ACM SIGKDD
international conference on Knowledge discovery and
data mining, ACM NewYork, NY, USA, 2004, pp. 470–478,
doi: 10.1145/1014052.1014105.
[7] D. Kong and G.Yan,“Discriminant malware distance
learning on structural information for automated malware
classification,”Proc. of the ACM SIGMETRICS/
international conference on Measurement and modeling
of computer systems,ACM NewYork, USA, 2013, pp. 347-
348, doi: 10.1145/2465529.2465531.
[8] R.Tian, L. Batten, and S.Versteeg,“Function Length as a
Tool for Malware Classification,” Proc. of the 3rd
International Conference.
29