This paper presents a method for tracking an object in a sequence of images given its location in the first frame. Recently, a class of techniques called discriminative methods has shown promising results. These methods are based on training a classifier to distinguish the object from surrounding background. However, discriminative methods do not explicitly model the object. Therefore, noisy samples are likely to interfere and cause visual drift. In this paper, 3D joint RGB histograms of the object and surrounding background are used to develop an object model. An Incremental color Learning scheme with a forgetting factor is applied to evolve the object model during tracking. It is shown the proposed method can handle visual drift effectively. Evaluated against five state of the art methods, experiments demonstrate superior results of the proposed tracking algorithm. Implemented in MATLAB, the algorithm runs at 17.2 frames per second, including image input/output time.

Android-based mobile devices are widely used due to their ease of use among users. Individuals perform various tasks on their mobile phones, such as banking activities, social networking, and diverse business systems, thereby exposing considerable personal information to risks due to the vulnerabilities of the Android operating system. The rapid development of Android malware has rendered many traditional malware detection methods less accurate over time. Research indicates that machine Learning is an effective approach for detecting malware. The rapid evolution of malware contributes to the degradation of accuracy in trained models over time. Moreover, the collection of malware-related data from Android devices jeopardizes users' privacy. To address these issue, this paper employs federated and Incremental Learning. Recently, federated Learning has been introduced for training machine Learning models on decentralized devices with the aim of preserving privacy. This study utilizes a Multi-Layer Perceptron (MLP) within the framework of federated Learning. Stacking, a type of ensemble Learning, is employed for Incremental Learning. The CICMalDroid 2020 dataset is utilized in this research, using static data to develop the final model. The outcome of this study is a model with an accuracy of 96.49%, demonstrating significant improvement in computational time complexity along with maintaining the quality of Learning and model accuracy compared to existing methods.

Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face by intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed intrusion detection model using correlation(IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.

Streaming data refers to data that is continuously generated in the form of fast streams with high volumes. This kind of data often runs into evolving environments where a change may affect the data distribution. Because of a wide range of real-world applications of data streams, performance improvement of streaming analytics has become a hot topic for researchers. The proposed method integrates Online ensemble Learning into extreme machine Learning to improve the data stream classification performance. The proposed Incremental method does not need to access the samples of previous blocks. Also, regarding the AdaBoost approach, it can react to concept drift by the component weighting mechanism and component update mechanism. The proposed method can adapt to the changes, and its performance is leveraged to retain high-accurate classifiers. The experiments have been done on benchmark datasets. The proposed method can achieve 0. 90% average specificity, 0. 69% average sensitivity, and 0. 87% average accuracy, indicating its superiority compared to two competing methods.