Small targets are often submerged in the cluttered backgrounds of infrared images. In this paper, we propose an iterative feedback UNet++ for infrared small target detection, dubbed ifUNet++. Unlike most of existing methods, ifU-Net++ enables to concentrate on small targets while weakening the interference of clutter backgrounds. ifUNet++ contains two parts: a simplified UNet++ and an iterative feedback strategy. We reduce the unnecessary nodes of UNet++ and have the simplified UNet++ as our backbone network, avoiding the loss of infrared small targets. Based on the simplified network, we search the infrared small targets in an iterative feedback manner, avoiding the interference of cluttered backgrounds. Besides, to optimize the iterative results, we propose Contextual Multiple Attention (CMA) to enhance the features in each iteration. Experimental results exhibit the clear promotion of ifUNet++ over eight state-of-the-art methods, in terms of noise-robustness and detection accuracy.

The rise of crowdsourcing brings new types of malpractices in Internet advertising. One can easily hire web workers through malicious crowdsourcing platforms to attack other advertisers. Such human generated crowd frauds are hard to detect by conventional fraud detection methods. In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity. Then we propose an effective crowd fraud detection method for search engine advertising based on these patterns, which consists of a constructing stage, a clustering stage and a filtering stage. At the constructing stage, we remove irrelevant data and reorganize the click logs into a surfer-advertiser inverted list; At the clustering stage, we define the sync-similarity between surfers' click histories and transform the coalition detection to a clustering problem, solved by a nonparametric algorithm; and finally we build a dispersity filter to remove false alarm clusters. The nonparametric nature of our method ensures that we can find an unbounded number of coalitions with nearly no human interaction. We also provide a parallel solution to make the method scalable to Web data and conduct extensive experiments. The empirical results demonstrate that our method is accurate and scalable.

Nowadays, it becomes more and more important to construct high-level attack scenarios from low-level intrusion alerts reported by intrusion detection systems (IDSs). Some methods have been presented to resolve this problem. These methods have different strengths. However, they also have different limitations. In order to build complicated attack processes accurately, this paper uses cluster and correlation techniques to construct high-level attack scenarios. Fuzzy cluster algorithm based on the similarity of attack attributes is proposed to classify alerts generated by IDSs. And then in every alert class, alert correlation method based on prerequisites and consequences of attacks is used to construct attack scenarios. Finally, to get whole attack graphs, this paper hypothesizes and reasons about attacks possibly missed based on the equality constrain and casual relation between intrusion alerts. The experimental results on LLS DDOS2.0 prove that the method is useful and effective.