The IFDS (Inter-procedural, Finite, Distributive, Subset) algorithms are popularly used to solve a wide range of analysis problems. In particular, many interesting problems are formulated as multi-solver IFDS problems which expect multiple interleaved IFDS solvers to work together. For instance, taint analysis requires two IFDS solvers, one forward solver to propagate tainted data-flow facts, and one backward solver to solve alias relations at the same time. For such problems, large amount of additional data-flow facts need to be introduced for flow-sensitivity. This often leads to poor performance and scalability, as evident in our experiments and previous work. In this paper, we propose a novel approach to reduce the number of introduced additional data-flow facts while preserving flow-sensitivity and soundness. We have developed a new taint analysis tool, SADROID, and evaluated it on 1,228 open-source Android APPs. Evaluation results show that SADROID significantly outperforms FLowDROID (the state-of-the-art multi-solver IFDS taint analysis tool) without affecting precision and soundness: the run time performance is sped up by up to 17.89X and memory usage is optimized by up to 9X.

At present, footprint image retrieval based on deep learning mainly focuses on complete footprint, but many of the footprints obtained in the field of public safety and criminal investigation are incomplete forms, Therefore, the feature analysis of incomplete footprint has important practical significance. Based on multiple scale features fusion, we proposed a method to solve incomplete footprints retrieval. On the basis of extracting the global feature of footprint, this method simultaneously extracts multiple local features from different stages of the backbone network to supplement the footprint feature information. The multi-scale feature orthogonal fusion module is used to reduce redundant features, improve the expression ability of footprint features, and solve the problem of incomplete footprint retrieval to a certain extent. The experiment shows that our method has certain effectiveness in retrieving problems on incomplete footprint, with a Top 1 accuracy of 87.08%, expanding the scope of footprint research.

Generic programming has found widespread application in object-oriented languages like Java. However, existing context-sensitive pointer analyses fail to leverage the benefits of generic programming. This paper introducesgeneric sensitivity, a new context customization scheme targeting generics. We design our context customization scheme in such a way that generic instantiation sites, i.e., locations instantiating generic classes/methods with concrete types, are always preserved as key context elements. This is realized by augmenting contexts with a type variable lookup map, which is efficiently generated in a context-sensitive manner throughout the analysis process. We have implemented various variants of generic-sensitive analysis in WALA and conducted extensive experiments to compare it with state-of-the-art approaches, including both traditional and selective context-sensitivity methods. The evaluation results demonstrate that generic sensitivity effectively enhances existing context-sensitivity approaches, striking a new balance between efficiency and precision. For instance, it enables a 1-object-sensitive analysis to achieve overall better precision compared to a 2-object-sensitive analysis, with an average speedup of 12.6 times (up to 62 times).

Generic programming has been extensively used in object-oriented programs such as Java. However, existing context-sensitive pointer analyses perform poorly in analyzing generics. This paper introduces generic sensitivity, a new context customization scheme targeting generics. We design our context customization scheme in such a way that generic instantiation sites, i.e., locations instantiating generic classes/methods with concrete types, are always preserved as key context elements. This is realized by augmenting contexts with a type variable lookup map, which is efficiently updated during the analysis in a context-sensitive manner.

The IFDS algorithm can be memory-intensive, requiring a memory budget of more than 100 GB of RAM for some applications. The large memory requirements significantly restrict the deployment of IFDS-based tools in practise. To improve this, we propose a disk-assisted solution that drastically reduces the memory requirements of traditional IFDS solvers. Our solution saves memory by 1) recomputing instead of memorizing intermediate analysis data, and 2) swapping in-memory data to disk when memory usages reach a threshold. We implement sophisticated scheduling schemes to swap data between memory and disks efficiently. We have developed a new taint analysis tool, DiskDroid, based on our disk-assisted IFDS solver. Compared to FlowDroid, a state-of-the-art IFDS-based taint analysis tool, for a set of 19 apps which take from 10 to 128 GB of RAM by FlowDroid, DiskDroid can analyze them with less than 10GB of RAM at a slight performance improvement of 8.6%. In addition, for 21 apps requiring more than 128GB of RAM by FlowDroid, DiskDroid can analyze each app in 3 hours, under the same memory budget of 10GB. This makes the tool deployable to normal desktop environments. We make the tool publicly available at https://github.com/HaofLi/DiskDroid.

People's dependence on search engines allows various computer viruses to spread faster and stronger. Most scholars have neglected the influence of search engines on virus propagation and immunity. It is impossible to immunize all users at the same time with a huge system like social networks. So the main problem is how to pick a fixed-scale node cluster as the source of immunity in the network, which can make other individuals immune and continue to spread (called immune seeds). The immune seeds are scattered on some web pages of search engines to reduce the network virus infection rate. We establish two models, one is the model of computer virus early propagation based on the search engine, and the other is the model of the virus propagation and immunization model. Then we propose an improved immunization strategy: Community Partition immunization strategy based on the target immunization strategy. And we use four real datasets and two simulated datasets to do the simulation experiments, which shows that search engine can promote the propagation of the virus and the immune seeds, and the efficiency of the Community Partition immunization strategy is slightly higher than the target immunization strategy based on degree under the same conditions.