The search for new authentication methods to replace passwords for modern mobile devices such as smartphones and tablets has attracted a substantial amount of research in recent years. As a result, several new behavioral biometric schemes have been proposed. Most of these schemes, however, are uni-modal. This paper presents a new, bi-modal behavioral biometric solution for user authentication. The proposed mechanism takes into account micro-movements of a phone and movements of the user's finger during writing or signing on the touchscreen. More specifically, it profiles a user based on how he holds the phone and based on the characteristics of the points being pressed on the touchscreen, and not the produced signature image. We have implemented and evaluated our scheme on commercially available smartphones. Using Multilayer Perceptron (MLP) 1-class verifier, we achieved approx. 95% True Acceptance Rate (TAR) with 3.1% False Acceptance Rate (FAR) on a dataset of 30 volunteers. Preliminary results on usability show a positive opinion about our system.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).

Smartphones are the most popular and widespread personal devices. Apart from their conventional use, that is, calling and texting, they have also been used to perform multiple security sensitive activities, such as online banking and shopping, social networking, taking pictures, and e-mailing. On a positive side, smartphones have improved the quality of life by providing multiple services that users desire, for example, anytime-anywhere computing. However, on the other side, they also pose security and privacy threats to the users’ stored data. User authentication is the first line of defense to prevent unauthorized access to the smartphone. Several authentication schemes have been proposed over the years; however, their presentation might be perplexing to the new researchers to this domain, under the shade of several buzzwords, for example, active, continuous, implicit, static, and transparent, being introduced in academic papers without comprehensive description. Moreover, most of the reported authentication solutions were evaluated mainly in terms of accuracy, overlooking a very important aspect—the usability. This paper surveys various types and ways of authentication, designed and developed primarily to secure the access to smartphones and attempts to clarify correlated buzzwords, with the motivation to assist new researchers in understanding the gist behind those concepts. We also present the assessment of existing user authentication schemes exhibiting their security and usability issues.