5 research outputs found
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes
A honeynet is a promising active cyber defense mechanism. It reveals the
fundamental Indicators of Compromise (IoCs) by luring attackers to conduct
adversarial behaviors in a controlled and monitored environment. The active
interaction at the honeynet brings a high reward but also introduces high
implementation costs and risks of adversarial honeynet exploitation. In this
work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to
characterize a stochastic transition and sojourn time of attackers in the
honeynet and quantify the reward-risk trade-off. In particular, we design
adaptive long-term engagement policies shown to be risk-averse, cost-effective,
and time-efficient. Numerical results have demonstrated that our adaptive
engagement policies can quickly attract attackers to the target honeypot and
engage them for a sufficiently long period to obtain worthy threat information.
Meanwhile, the penetration probability is kept at a low level. The results show
that the expected utility is robust against attackers of a large range of
persistence and intelligence. Finally, we apply reinforcement learning to the
SMDP to solve the curse of modeling. Under a prudent choice of the learning
rate and exploration policy, we achieve a quick and robust convergence of the
optimal policy and value.Comment: The presentation can be found at https://youtu.be/GPKT3uJtXqk. arXiv
admin note: text overlap with arXiv:1907.0139
Dynamics of sputum conversion during effective tuberculosis treatment: A systematic review and meta-analysis
Common surgical procedures in pilonidal sinus disease: A meta-analysis, merged data analysis, and comprehensive study on recurrence
Abstract We systematically searched available databases. We reviewed 6,143 studies published from 1833 to 2017. Reports in English, French, German, Italian, and Spanish were considered, as were publications in other languages if definitive treatment and recurrence at specific follow-up times were described in an English abstract. We assessed data in the manner of a meta-analysis of RCTs; further we assessed non-RCTs in the manner of a merged data analysis. In the RCT analysis including 11,730 patients, Limberg & Dufourmentel operations were associated with low recurrence of 0.6% (95%CI 0.3–0.9%) 12 months and 1.8% (95%CI 1.1–2.4%) respectively 24 months postoperatively. Analysing 89,583 patients from RCTs and non-RCTs, the Karydakis & Bascom approaches were associated with recurrence of only 0.2% (95%CI 0.1–0.3%) 12 months and 0.6% (95%CI 0.5–0.8%) 24 months postoperatively. Primary midline closure exhibited long-term recurrence up to 67.9% (95%CI 53.3–82.4%) 240 months post-surgery. For most procedures, only a few RCTs without long term follow up data exist, but substitute data from numerous non-RCTs are available. Recurrence in PSD is highly dependent on surgical procedure and by follow-up time; both must be considered when drawing conclusions regarding the efficacy of a procedure