A MACHINE LEARNING APPROACH TO THE DETECTION AND ANALYSIS OF ANDROID MALICIOUS APPS
It is always an open war between the attackers and defenders. The defenders will make use of new technologies to stop the attackers and the attackers will try their level best to bypass the wall created by the defenders. For example, when anti-virus makers came up with signature analysis to protect the platforms, the attackers started creating new/encrypted signatures to bypass that. This made the need for a new technique and that is what we are trying to implement in our system using the Machine Learning Technique considering Machine Learning is the future. Today we say Machine Learning as the future. The reason is that, if you search around, you will understand that there is a lot of data everywhere. Starting from text messages to Facebook, email, maps and the list goes on. So, it became very necessary to manage these data’s in an efficient way. If you consider humans, there is a limit for the amount of data a human can manage. So there is one way left and that is the Machines Learning. A machine learning is the ability of a machine to learn without being explicitly programmed. It’s like, if you tell the machine to do a task 2 times repeatedly, 3rd time the machine will do it automatically and the 4th time it will do better. If that good the machine learning is then the outcome we will be getting once we use this concept for developing a mobile android application that can be used to differentiate malicious apps and benign apps should be more efficient. That is what our aim is and here, we will be developing a mobile application for the purpose of detecting and analyzing malicious apps and it will be working on the basis of Machine Learning.
Code Shoppy
Most of the existing commercial anti-virus applications are based on the signature analysis. They will do the matching of extracted signature of an application with the already available signatures in the database. The problem with such applications is that they are vulnerable to zero-day exploits as nowadays the malware writers are capable of creating a new signature by their own to bypass the anti-virus software. Furthermore, they can encrypt or obfuscate the malicious code to make the signature analysis more difficult. There is a security check done by the play store to stop the uploading of malicious applications into it. But the truth is that there are a lot of malicious applications available in play store even after the security check. IV.PROPOSEDSOLUTION The existing signature-based analysis is based on the signature stored in the database of an anti-virus application and it is not all a tough job for malware writers to bypass it. This is because the malware writers can create a signature by their own or they can modify it during the runtime. My system implements ML methodology for detection and analysis of malicious applications. This approach helps us filter harmful applications more accurately and effectively as it contains both static and dynamic analysis. So the above issue is also addressed in the proposed system as it is based on the permissions in the manifest file rather than the signature in static analysis and based on the malicious activities the app will be triggering in the dynamic analysis. In this system, I will be creating a rule set by my own so that it can give maximum success rate. This system will have the functionality to combine both static and dynamic analysis result. This also allows us to adapt to the new attacking methodologies being implemented by cybercriminals constantly.
As the technology is opening so many new methods for the attackers, we also need to utilize the same technology to implement counter methods to safeguard our privacy from the attackers. When anti-virus software makers started using signature analysis to find a malware, the attackers started creating a new signature to bypass such solutions. This made such solutions as less reliable. So the need for introducing a different solution which is more reliable, secure and efficient is very high. That is where the Machine Learning technique comes into play. Machine Learning is the future and in this system, the Machine-learning technique will look for patterns in the program properties. This will be the base for differentiating between a malicious application and a benign application. But the problem will arise when malware writers will start developing new techniques to bypass the algorithms that we implemented using the machine learning. In other words, the future will be going to be an open war between the malware authors and the defenders. VIII.FUTUREDIRECTIONS Our proposed system can be implemented to expand into the cloud thereby enhancing the reach of security by providing protection even for low run devices. Also, our system can be modified in such a way that it will be able to prevent unauthorized access of devices, financial crimes carried out from mobile devices and mobile phone spoofing https://codeshoppy.com/android-app-ideas-for-students-college-project.html
It is always an open war between the attackers and defenders. The defenders will make use of new technologies to stop the attackers and the attackers will try their level best to bypass the wall created by the defenders. For example, when anti-virus makers came up with signature analysis to protect the platforms, the attackers started creating new/encrypted signatures to bypass that. This made the need for a new technique and that is what we are trying to implement in our system using the Machine Learning Technique considering Machine Learning is the future. Today we say Machine Learning as the future. The reason is that, if you search around, you will understand that there is a lot of data everywhere. Starting from text messages to Facebook, email, maps and the list goes on. So, it became very necessary to manage these data’s in an efficient way. If you consider humans, there is a limit for the amount of data a human can manage. So there is one way left and that is the Machines Learning. A machine learning is the ability of a machine to learn without being explicitly programmed. It’s like, if you tell the machine to do a task 2 times repeatedly, 3rd time the machine will do it automatically and the 4th time it will do better. If that good the machine learning is then the outcome we will be getting once we use this concept for developing a mobile android application that can be used to differentiate malicious apps and benign apps should be more efficient. That is what our aim is and here, we will be developing a mobile application for the purpose of detecting and analyzing malicious apps and it will be working on the basis of Machine Learning.
Code Shoppy
Most of the existing commercial anti-virus applications are based on the signature analysis. They will do the matching of extracted signature of an application with the already available signatures in the database. The problem with such applications is that they are vulnerable to zero-day exploits as nowadays the malware writers are capable of creating a new signature by their own to bypass the anti-virus software. Furthermore, they can encrypt or obfuscate the malicious code to make the signature analysis more difficult. There is a security check done by the play store to stop the uploading of malicious applications into it. But the truth is that there are a lot of malicious applications available in play store even after the security check. IV.PROPOSEDSOLUTION The existing signature-based analysis is based on the signature stored in the database of an anti-virus application and it is not all a tough job for malware writers to bypass it. This is because the malware writers can create a signature by their own or they can modify it during the runtime. My system implements ML methodology for detection and analysis of malicious applications. This approach helps us filter harmful applications more accurately and effectively as it contains both static and dynamic analysis. So the above issue is also addressed in the proposed system as it is based on the permissions in the manifest file rather than the signature in static analysis and based on the malicious activities the app will be triggering in the dynamic analysis. In this system, I will be creating a rule set by my own so that it can give maximum success rate. This system will have the functionality to combine both static and dynamic analysis result. This also allows us to adapt to the new attacking methodologies being implemented by cybercriminals constantly.
As the technology is opening so many new methods for the attackers, we also need to utilize the same technology to implement counter methods to safeguard our privacy from the attackers. When anti-virus software makers started using signature analysis to find a malware, the attackers started creating a new signature to bypass such solutions. This made such solutions as less reliable. So the need for introducing a different solution which is more reliable, secure and efficient is very high. That is where the Machine Learning technique comes into play. Machine Learning is the future and in this system, the Machine-learning technique will look for patterns in the program properties. This will be the base for differentiating between a malicious application and a benign application. But the problem will arise when malware writers will start developing new techniques to bypass the algorithms that we implemented using the machine learning. In other words, the future will be going to be an open war between the malware authors and the defenders. VIII.FUTUREDIRECTIONS Our proposed system can be implemented to expand into the cloud thereby enhancing the reach of security by providing protection even for low run devices. Also, our system can be modified in such a way that it will be able to prevent unauthorized access of devices, financial crimes carried out from mobile devices and mobile phone spoofing https://codeshoppy.com/android-app-ideas-for-students-college-project.html
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