The Extended Berkeley Packet Filter (eBPF) is a sandboxed virtual machine that runs on operating systems with kernel privileges. Currently, eBPF programs are either translated from a subset of C, called Restricted C, or from bindings available for languages such as Rust or Python. This paper describes Honey Potion, a compiler that compiles Elixir to eBPF binaries. Translation is challenging, for it must not only preserve semantics, but also satisfy the eBPF verifier, which requires proofs of in-bounds memory accesses and termination. The translator relies heavily on this last constraint---ensured termination---to implement different optimizations: constant propagation, type specialization and partial evaluation. Honey Potion is publicly available, and has been used in the development of many eBPF applications, such as packet routers, process monitors and event loggers. To the best of our knowledge, Honey Potion is the first translator of a functional programming language to eBPF.

Digital Collectible Card Games (DCCG) rely on human expertise to create competitive card decks. The most effective decks are shared and become popular among players through online communities. Players then keep enhancing those decks by replacing cards and testing those new deck variations in online arenas. This fine-tuning process is time-consuming and most of the time leads to small improvements in the players win rate. This paper presents CreativeStone, a creativity booster for Hearthstone card decks. Our creativity-guided approach uses a genetic algorithm and the Regent-Dependent Creativity (RDC) metric to identify the core cards of an existing deck, and then improves it towards a more valuable and novel deck by adding cards that are synergic to the core cards, but also different from the ones in the original deck. Our experimental results show that CreativeStone can boost even legendary decks, outperforming handcrafted ones by 21% in win rate.

Algorithm classification consists in determining which algorithm a program implements, given a finite set of candidates. Classifiers are used in applications such malware identification and plagiarism detection. There exist many ways to implement classifiers. There are also many ways to implement evaders to deceive the classifiers. This paper analyzes the state-of-the-art classification and evasion techniques. To organize this analysis, this paper brings forward a system of four games that matches classifiers and evaders. Games vary according to the amount of information that is given to each player. This setup lets us analyze a space formed by the combination of nine program encodings; seven obfuscation passes; and six stochastic classification models. Observations from this study include: (i) we could not measure substantial advantages of recent vector-based program representations over simple histograms of opcodes; (ii) deep neural networks recently proposed for program classification are no better than random forests; (iii) program optimizations are almost as effective as classic obfuscation techniques to evade classifiers; (iv) off-the-shelf code optimizations can completely remove the evasion power of naïve obfuscators; (v) control-flow flattening and bogus-control flow tend to resist the normalizing power of code optimizations.