Monitoring and Detecting Abnormal Behavior in Mobile
Recently, several mobile services are changing to cloud-based mobile services with richer communications and higher flexibility. We present a new mobile cloud infrastructure that combines mobile devices and cloud services. This new infrastructure provides virtual mobile instances through cloud computing. To commercialize new services with this infrastructure, service providers should be aware of security issues. Here, we first define new mobile cloud services through mobile cloud infrastructure and discuss possible security threats through the use of several service scenarios. Then, we propose a methodology and architecture for detecting abnormal behavior through the monitoring of both host and network data. To validate our methodology, we injected malicious programs into our mobile cloud test bed and used a machine learning algorithm to detect the abnormal behavior that arose from these programs.
On such normal mobile devices, most current vaccine applications detect malware through a signature-based method. Signature-based methods can detect malware in a short space of time with high accuracy, but they cannot detect new malware whose signature is unknown or has been modified. If mobile cloud services are provided, much more malicious applications may appear including new and modified malware. Therefore vaccine applications cannot detect and prohibit them with only signature-based method in the future. Moreover, mobile cloud infrastructure supports a huge number of virtual mobile instances. When a malware is compromised on a virtual mobile instance, it can be delivered to other virtual mobile instances in the same mobile cloud infrastructure. Without monitoring the network behavior in mobile cloud infrastructure, the malware will spread over the entire infrastructure. Algorithm:
Random Forest Machine machine learning algorithm.
Here We focuses on the abnormal behavior detection in mobile cloud infrastructure. Although signature-based vaccine applications can target on virtual mobile instances to detect malware, it makes additional overhead on instances, and it is difficult for users to install vaccine software by force when those instances are provided as a service. Behavior-based abnormal detection can address those problems by observing activities in the cloud infrastructure. To achieve this, we design a monitoring architecture using both the host and network data. Using monitored data, abnormal behavior is detected by applying a machine learning algorithm. To validate our methodology, we built a test bed for mobile cloud infrastructure, intentionally installed malicious mobile programs onto several virtual mobile instances, and then successfully detected the abnormal behavior that arose from those malicious programs.
Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective.
The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.
1. USER MODULE :
In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first.
2. MOBILE CLOUD SERVICE :
Here new mobile cloud service through the virtualization of mobile devices...
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