Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Internet of Things (IoT) devices incorporate a large amount ofdata in several fields, including those of medicine, business, and engineering.User authentication is paramount in the IoT era to assure connecteddevices’ security. However, traditional authentication methods and conventionalbiometrics-based authentication approaches such as face recognition,fingerprints, and password are vulnerable to various attacks, including smudgeattacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics isintroduced by the powerful sensing capabilities of IoT devices such as smartwearables and smartphones, enabling continuous authentication. ArtificialIntelligence (AI)-based approaches introduce a bright future in refining largeamounts of homogeneous biometric data to provide innovative user authenticationsolutions. This paper presents a new continuous passive authenticationapproach capable of learning the signatures of IoT users utilizing smartphonesensors such as a gyroscope, magnetometer, and accelerometer to recognizeusers by their physical activities. This approach integrates the convolutionalneural network (CNN) and recurrent neural network (RNN) models to learnsignatures of human activities from different users. A series of experiments areconducted using the MotionSense dataset to validate the effectiveness of theproposed method. Our technique offers a competitive verification accuracyequal to 98.4%.We compared the proposed method with several conventionalmachine learning and CNN models and found that our proposed modelachieves higher identification accuracy than the recently developed verificationsystems. The high accuracy achieved by the proposed method proves itseffectiveness in recognizing IoT users passively through their physical activitypatterns.