Due to massive growth in IoT devices in recent years, security of these devices is a major concern. IoT applications span across a number of fields including but not limited to smart cities, intelligent agriculture systems, and the innovative industry. Despite its benefits, cybersecurity challenges have increased significantly in IoT environments. The lack of resource capacity and sophisticated security measurement exposes IoT devices to large number of recent attacks. A strong intrusion detection system (IDS) is the best way to secure IoT devices. Various studies have shown that the current IDS fails to detect modern malware in IoT environments. Some datasets do not have complex scenarios of attack. There are limitations of current datasets, or heterogeneous data of the IoT environment, such as KDD99, NLS_KDD, and UNSW _NB15. In addition, these datasets do not include an operating system and network monitoring audits. This paper comprehensively compares five machine-learning models on the recent EDGE-IIoT dataset. We examine these machine learning methods on Binary and Multiclass class IDS and the security challenges in managing current and future attacks in an IoT environment.

MOTIVATION: Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. RESULTS: We first demonstrate that detecting such 'co-fit' gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast. AVAILABILITY AND IMPLEMENTATION: Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A combination of the sparse coding and transfer learning techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from different underlying distributions, i.e., belong to different domains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small number of them. In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.