Person identification systems based on fingerprint patterns called Automatic Fingerprint Identification Systems, AFIS,are some of the most widely used biometric methods since they provide a high degree of success. The accuracy ofAFIS is mainly due to some unique characteristics called minutiae, which are points where a curve track finishes,intersects with another curve track, or branches off. During past decades several efficient minutia-based fingerprintrecognition algorithms have been proposed which achieve false recognition rates close to 1%, however, theirrecognition rate may be still improved. To this end, this paper presents a fingerprint recognition method using acombination of the Fast Fourier Transform (FFT) with Gabor filters for image enhancement. Next, fingerprintrecognition is carried out using a novel recognition stage based on Local Features and Hu invariant moments forverification.

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Google’s Android is the most used Operating System in mobile devices but as its popularity has increased hackers have taken advantage of the momentum to plague Google Play (Android’s Application Store) with multipurpose Malware that is capable of stealing private information and give the hacker remote control of smartphone’s features in the worst cases. This work presents an innovative methodology that helps in the process of malware detection for Android Operating System, which addresses aforementioned problem from a different perspective that even popular Anti-Malware software has left aside. It is based on the analysis of a common characteristic to all different kinds of malware: the need of network communications, so the victim device can interact with the attacker. It is important to highlight that in order to improve the security level in Android, our methodology should be considered in the process of malware detection. As main characteristic, it does not need to install additional kernel modules or to root the Android device. And finally as additional characteristic, it is as simple as can be considered for non-experienced users.

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