With the increasing adoption of end-to-end encryption in industrial systems, the risk of distributing hidden malware by exploiting encrypted channels gradually turns to a major concern. Due to encryption, the stateof-the-art, signature-based mechanisms might fail to detect malware sufficiently, thus new approaches are required. In this work, a method for malware detection in encrypted traffic based on Machine Learning is presented. A supervised learning approach is adopted and the efficiency of the solution is demonstrated by a set of exhaustive simulations. Further considerations for incorporating the proposed method in a reference industrial network are also discussed.
Imagine that in a few years from now a full-scale, practical quantum computer hits the headlines. In this apocalyptic scenario, the world of cryptography would be in a state of shock, since almost everything that forms the foundations of current security would collapse. Indeed, the presence of a quantum computer would render state-of-the-art, public-key cryptography useless. All the underlying assumptions about the intractability of mathematical problems that offer confident levels of security today would no longer apply in the presence of a quantum computer. But are we really doomed? Is cryptography dead? Well, luckily no. This paper examines the technologies that will enable crypto to survive the post-apocalyptic world of quantum computing. There are many things yet to be done to offer an environment as safe as current crypto does, but the tools are there. It is now a game of engineering, pro-active standardization and ingenious mathematics, while following a careful development approach to pave a safe way through the qubits inferno.
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