User behavior threat detection is important for the protection of network system security. Traditional supervised modeling methods and unbalanced sample data lead to a high false positive rate in user behavior detection. In addition, network user behaviors are complex, changeable, and difficult to predict, and existing detection methods are facing ever greater challenges. Effectively detecting user behavior remains a challenge. In this paper, we propose a user behavior threat detection method based on an Adaptive Sliding Window Generative Adversarial Network(ASW-GAN). This method designs an adaptive sliding window mechanism to process behavior data and uses the GAN model to detect threat behavior, finally uses the maximum interclass variance algorithm Otsu to optimize test detection result. Compared with other typical methods, the proposed method achieves a higher accuracy rate and a markedly lower false positive rate, and can effectively evaluate user threat behaviors.

Internet of Things (IoT) brought great convenience to people’s daily lives. Meanwhile, the IoT devices are facing severe attacks from hackers and malicious attackers. Hackers and malicious attackers use various methods to invade the Internet of Things system, causing the Internet of Things to face a large number of targeted, concealed, and penetrating potential threats, which makes the privacy problem of the Internet of Things suffers serious challenges. But the existing methods and technologies cannot fully identify the attacker’s attack process and protect the privacy of the Internet of Things. Alarm correlation method can construct a complete attack scenario and identify the attacker’s intention by alarming the alarm data which provides an effective protection for user privacy. However, the existing alarm correlation methods still have the disadvantages of low correlation accuracy, poor correlation efficiency, and strong dependence on the knowledge base. To address these issues, we propose an alarm correlation method based on Affinity Propagation (AP) clustering algorithm and causal relationship. Our method considers that the alarm data triggered by the same attack process has high similarity characteristics, adopts the AP algorithm to improve the correlation efficiency, and at the same time constructs a complete attack process based on the causal correlation idea. The new alarm correlation method has a high correlation effect and builds a complete attack process to help managers identify attack intentions and prevent attacks.

Multi-kernel learning machine (MKLM) has recently been introduced to the research of computer-aided dementia identification and pathology progress tracking. Despite its good performance especially in case of using heterogeneous data, such learning schema and its variants usually utilize a L-l norm constraint that promotes sparse solutions, which may cause loss of potentially important information. In this paper, we propose the non-sparse infinite-kernel learning machine (NS-IKLM) for automated identification of Alzheimer cases from normal controls. In our approach, a modified constraint is utilized to promotes non-sparse solutions and kernel parameters are automatically tuned during the learning process. The proposed algorithm has been evaluated on a set of FDG-PET images selected from the Alzheimer's disease neuroimaing initiative (ADNI) cohort. Our results demonstrate that the proposed non-sparse NS-IKLM is able to achieve satisfying dementia identification at a relatively low computational cost.

Association mining in finding relationships between items in a dataset has been demonstrated to be practical in business applications. Many companies are applying association mining on market data for analyzing consumers' purchase behavior. The Apriori algorithm is the most established algorithm for association mining in finding frequent itemsets. However, the time complexity of the Apriori algorithm is dominated by the size of candidate itemsets. Research to date has focused on the efficient discovery of itemsets in a large dataset. Those improvements include the optimizations of data structures, the partitioning of datasets, and the parallelism of data mining. In this paper, we propose a distributed association mining algorithm in finding frequent itemsets. The work is different from many existing distributed algorithms where most of existing algorithms center on the reduction of the size of the dataset. Our distributed algorithm focuses on the reduction of the size of candidate itemsets. The work of candidate k-itemsets generation is evenly distributed to the nodes for workload balancing among processors. The complexity analysis of the distributed algorithm is also presented.