DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
Predict Android ransomware using categorical classifiaction.pptxlaharisai03
This research introduces a novel method for predicting Android ransomware attacks using categorical classification techniques. By analyzing features extracted from Android applications, the model effectively identifies malicious behavior, providing a proactive defense against ransomware threats on mobile devices. Enhancing security and ensuring a safer mobile ecosystem.
A FRAMEWORK FOR THE DETECTION OF BANKING TROJANS IN ANDROIDIJNSA Journal
Android is the most widely used operating system today and occupies more than 70% share of the smartphone market. It is also a popular target for attackers looking to exploit mobile operating systems for personal gains. More and more malware are targeting android operating system like Android Banking Trojans (ABTs) which are widely being discovered. To detect such malware, we propose a prediction model for ABTs that is based on hybrid analysis. The feature sets used with the machine learning algorithms are permissions, API calls, hidden application icon and device administrator. Feature selection methods based on frequency and gain ratio are used to minimize the number of features as well as to eliminate the low-impact features. The proposed system is able to achieve significant performance with selected machine learning algorithms and achieves accuracy up to 98% using random forest classifier.
Machine Learning Based Ensemble Classifier for Android Malware DetectionIJCNCJournal
Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones, malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy are chosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications.
Machine Learning Based Ensemble Classifier for Android Malware DetectionIJCNCJournal
Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
Predict Android ransomware using categorical classifiaction.pptxlaharisai03
This research introduces a novel method for predicting Android ransomware attacks using categorical classification techniques. By analyzing features extracted from Android applications, the model effectively identifies malicious behavior, providing a proactive defense against ransomware threats on mobile devices. Enhancing security and ensuring a safer mobile ecosystem.
A FRAMEWORK FOR THE DETECTION OF BANKING TROJANS IN ANDROIDIJNSA Journal
Android is the most widely used operating system today and occupies more than 70% share of the smartphone market. It is also a popular target for attackers looking to exploit mobile operating systems for personal gains. More and more malware are targeting android operating system like Android Banking Trojans (ABTs) which are widely being discovered. To detect such malware, we propose a prediction model for ABTs that is based on hybrid analysis. The feature sets used with the machine learning algorithms are permissions, API calls, hidden application icon and device administrator. Feature selection methods based on frequency and gain ratio are used to minimize the number of features as well as to eliminate the low-impact features. The proposed system is able to achieve significant performance with selected machine learning algorithms and achieves accuracy up to 98% using random forest classifier.
Machine Learning Based Ensemble Classifier for Android Malware DetectionIJCNCJournal
Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones, malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy are chosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications.
Machine Learning Based Ensemble Classifier for Android Malware DetectionIJCNCJournal
Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications.
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.
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
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
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil
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.
COMPARATIVE REVIEW OF MALWARE ANALYSIS METHODOLOGIESIJNSA Journal
To fight against the evolution of malware and its development, the specific methodologies that are applied by the malware analysts are crucial. Yet, this is something often overlooked in the relevant bibliography or in the formal and informal training of the relevant professionals. There are only two generic and allencompassing structured methodologies for Malware Analysis (MA) – SAMA and MARE. The question is whether they are adequate and there is no need for another one or whether there is no such need at all. This paper will try to answer the above and it will contribute in the following ways: it will present, compare and dissect those two malware analysis methodologies, it will present their capacity for analysing modern malware by applying them on a random modern specimen and finally, it will conclude on whether there is a procedural optimization for malware analysis over the evolution of these two methodologies.
Android malware detection through online learningIJARIIT
Android malware constantly evolves so as to evade detection. The entire malware population to be nonstationary.
Contrary to this fact, most of the prior works on machine learning based android malware detection have
assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The
problem of malware population drift and propose a novel online learning based framework to detect malware, named
CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate
detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviours along with their context
information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific
advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant
features that led to an apps classification as being malicious or benign.
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.
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.
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
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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.
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
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
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil
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.
COMPARATIVE REVIEW OF MALWARE ANALYSIS METHODOLOGIESIJNSA Journal
To fight against the evolution of malware and its development, the specific methodologies that are applied by the malware analysts are crucial. Yet, this is something often overlooked in the relevant bibliography or in the formal and informal training of the relevant professionals. There are only two generic and allencompassing structured methodologies for Malware Analysis (MA) – SAMA and MARE. The question is whether they are adequate and there is no need for another one or whether there is no such need at all. This paper will try to answer the above and it will contribute in the following ways: it will present, compare and dissect those two malware analysis methodologies, it will present their capacity for analysing modern malware by applying them on a random modern specimen and finally, it will conclude on whether there is a procedural optimization for malware analysis over the evolution of these two methodologies.
Android malware detection through online learningIJARIIT
Android malware constantly evolves so as to evade detection. The entire malware population to be nonstationary.
Contrary to this fact, most of the prior works on machine learning based android malware detection have
assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The
problem of malware population drift and propose a novel online learning based framework to detect malware, named
CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate
detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviours along with their context
information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific
advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant
features that led to an apps classification as being malicious or benign.
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.
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.
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attri
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CO2 EMISSION RATING BY VEHICLES USING DATA SCIENCE
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Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evo...Shakas Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity.docx
1. Base paper Title: Automated Android Malware Detection Using Optimal Ensemble Learning
Approach for Cybersecurity
Modified Title: Cybersecurity: Automatic Android Malware Detection Through the Use of the
Optimal Ensemble Learning Approach
Abstract
Current technological advancement in computer systems has transformed the lives of
humans from real to virtual environments. Malware is unnecessary software that is often
utilized to launch cyberattacks. Malware variants are still evolving by using advanced packing
and obfuscation methods. These approaches make malware classification and detection more
challenging. New techniques that are different from conventional systems should be utilized
for effectively combating new malware variants. Machine learning (ML) methods are
ineffective in identifying all complex and new malware variants. The deep learning (DL)
method can be a promising solution to detect all malware variants. This paper presents an
Automated Android Malware Detection using Optimal Ensemble Learning Approach for
Cybersecurity (AAMD-OELAC) technique. The major aim of the AAMD-OELAC technique
lies in the automated classification and identification of Android malware. To achieve this, the
AAMD-OELAC technique performs data preprocessing at the preliminary stage. For the
Android malware detection process, the AAMD-OELAC technique follows an ensemble
learning process using three ML models, namely Least Square Support Vector Machine (LS-
SVM), kernel extreme learning machine (KELM), and Regularized random vector functional
link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is
exploited for the optimal parameter tuning of the three DL models, and it helps accomplish
improved malware detection results. To denote the supremacy of the AAMD-OELAC method,
a comprehensive experimental analysis is conducted. The simulation results portrayed the
supremacy of the AAMD-OELAC technique over other existing approaches.
Existing System
Cybersecurity is becoming a main area of immediate concern to network engineers and
computer scientists, so satisfying solutions to several problems are in order [1]. Consequently,
the fast technological developments and their inherent integrations in every aspect of lifestyles,
various malware apps, and targets become well-identified and studied [2]. Android malware is
2. the malware variety that gained significant interest in the web world. One common operating
system is Android, which dominates the operating system market [3]. Malware invasive
methods emerge for avoiding identification, as few malware applications have more than 50
parameters that make detection a difficult one [4]. Hence, it is essential to devise techniques
that deal with the continuous growth of Android malware to find it, deactivate or remove it
efficiently. All these difficulties engage scholars in the area and urge them to continue more
research to find malware and manage it properly [5]. Thus, researchers have developed three
mechanisms to find Android malware such as dynamic, static, and hybrid analysis methods.
Static analysis extracts the features that assist in identifying harmful performance for apps
without a demanding actual application deployment [6]. But this kind of analysis suffered from
code obfuscation methods which assist help malware author to avoid static methods. Dynamic
analysis can be used for determining the malware of apps in their runtime [7]. Commonly, the
static analysis feature offers the capability of locating the malware element using source code,
while the dynamic analysis feature offers the capability of finding the location of malware in a
runtime environment. Android developers and users can be exposed to unnecessary risks and
dangers with malware [8]. This study covers malware detection methods. The detection of
malware using the ML model includes Android Application Packages (APKs) for deriving an
appropriate set of features. Deep learning (DL) and machine learning (ML) approaches can be
used for recognizing malicious APKs [9]. Like malware detection, vulnerability detection in
software code has two stages: training ML on derived attributes to find vulnerable code
segments and feature generation utilizing code analysis [10].
Drawback in Existing System
Improved Accuracy:
Ensemble learning methods, such as Random Forests or Gradient Boosting, often
result in better accuracy compared to individual classifiers. This can enhance the
detection of Android malware by reducing false positives and false negatives.
Interpretability:
While ensemble methods generally provide high accuracy, they may lack
interpretability compared to simpler models. Understanding why a particular decision
3. was made might be challenging, which is a concern in the context of cybersecurity
where explain ability is crucial.
Dynamic Nature of Malware:
Malware is constantly evolving, and new variants emerge frequently. The ensemble
learning model might not adapt quickly enough to newly emerging threats without
regular updates and retraining.
Feature Importance:
Ensemble methods can provide insights into feature importance, helping cyber
security professionals understand which features contribute most to malware detection.
This information can be valuable for refining the feature set.
Proposed System
Data Collection:
Gather a diverse and representative dataset of Android applications, including both
benign and malicious samples.
Model Evaluation:
Evaluate the performance of the model on a separate test set, measuring metrics such
as accuracy, precision, recall, and F1 score. Assess the model's ability to handle false
positives and false negatives.
Integration with Android Security Framework:
Integrate the trained model into the Android security framework for real-time
detection. This may involve collaboration with the Android operating system or third-
party security applications.
Collaboration with Security Communities:
Engage with cyber security communities to stay informed about the latest threats and
collaborate on improving the detection capabilities of the system.
4. Algorithm
Data Preprocessing:
Normalization and Scaling:
Algorithms such as Min-Max scaling or StandardScaler can be applied to normalize
and scale features.
Imputation:
Handle missing data using imputation methods such as mean imputation.
Ensemble Learning Models:
Random Forest:
A popular ensemble learning algorithm that uses multiple decision trees.
Gradient Boosting:
Algorithms like XGBoost, AdaBoost, or LightGBM can be used for boosting-based
ensemble learning.
Feature Extraction:
Static Analysis:
Permission-based Features: Identify and extract Android app permissions.
Manifest Analysis: Extract information from the AndroidManifest.xml file.
Code Analysis: Extract features from the app's code using static analysis tools.
Dynamic Analysis:
API Call Monitoring: Use dynamic analysis to monitor API calls during app
execution.
Advantages
Improved Accuracy:
Ensemble learning combines multiple models, reducing the risk of overfitting and
improving overall accuracy. By aggregating the predictions of different models, the
system can achieve higher precision and recall in detecting both known and unknown
malware.
5. Feature Importance Analysis:
Ensemble learning algorithms can provide insights into the importance of different
features in the detection process. This analysis helps cybersecurity professionals
understand the characteristics of malware, contributing to the refinement of the feature
set and better interpretability of the system.
Adaptability to Evolving Threats:
The dynamic nature of malware requires continuous adaptation of detection models.
Ensemble learning systems can be easily updated with new data, allowing them to adapt
to emerging threats and maintain high detection rates over time.
Parallel Processing:
Some ensemble learning algorithms, such as Random Forest, support parallel
processing. This can lead to faster training times and real-time detection, making the
system more responsive to potential threats.
Software Specification
Processor : I3 core processor
Ram : 4 GB
Hard disk : 500 GB
Software Specification
Operating System : Windows 10 /11
Frond End : Python
Back End : Mysql Server
IDE Tools : Pycharm