Since cyberattacks have become sophisticated in the form of advanced persistent threats (APTs), predicting and defending the APT attacks have drawn lots of attention. Although there have been related studies such as attack graphs, Hidden Markov Models, and Bayesian networks, they have four representative limitations; (i) non-standard attack modeling, (ii) lack of data-driven approaches, (iii) absence of real-world APT dataset, and (iv) high system dependability. In this paper, we propose Bayesian ATT&CK Network (BAN) which is based on system-independent data-driven approach. Specifically, BAN is based on Bayesian network, which adopts structure learning and parameter learning to model APT attackers with the MITRE ATT&CK® framework. The trained BAN aims to predict upcoming attack techniques and derives corresponding countermeasures. In addition, we prepare datasets via both automatic and manual labeling to overcome the data insufficiency issues of APT prediction. Experimental results show that BAN successfully contributes to handling APT attacks, given the best parameters extracted from extensive evaluations.

Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. The concolic execution fuzzers also have limitations in scalability. Recently, the gradient-based fuzzers that use a gradient to mutate inputs have been introduced. Gradient-based fuzzers can be applied to real-world programs and achieve high code coverage. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. In this paper, we propose a new type of gradient-based fuzzer, MaxAFL, to overcome the limitations of existing gradient-based fuzzers. Our approach constructs an objective function through fine-grained static analysis. After constructing a well-made objective function, we can apply the gradient-based optimization algorithm. We use a modified gradient-descent algorithm to minimize our objective function and propose some probabilistic techniques to escape local optimum. We introduce an adaptive objective function which aims to explore various paths in the program. We implemented MaxAFL based on the original AFL. MaxAFL achieved increase of code coverage per time compared with three other fuzzers in six open-source Linux binaries. We also measured cumulative code coverage per total execution, and MaxAFL outperformed the other fuzzers in this metric. Finally, MaxAFL can also find more bugs than the other fuzzers.

Purpose: The purpose of this study was to investigate the effect of context interference in attentional focus on motor learning in beginners. Methods: Badminton novice college students(n=30) were randomly assigned to 3 groups(IFA: a constant-practice group with repeated internal attention instruction, EFA: a constant-practice group with repeated external attention instruction, R: a random-practice group with internal and external attention instructions randomly presented). The experimental task was to perform badminton high-clear skill. This experiment consisted of pre-test, acquisition stage, delayed retention test and transfer test. The absolute error, the variable error and the error were calculated for all trials. Results: The IFA group and the R group showed a significant delayed retention effect in terms of accuracy and although no statistically significant difference was shown, the R group showed a better delayed retention effect. In terms of consistency, no significant learning effect was shown and in terms of achievement, the only delayed retention effect showed in the R group. The transfer effect was not shown in all aspects. Conclusion: This results showed that randomly presenting internal/external attention instructions can be more effective than repeatedly presenting a single attention instruction in beginner's badminton high-clear learning.