In recent years, the increase in cyber threats has raised many concerns about security and privacy in the digital world. However, new attack methods are often limited to a few core techniques. Here, in order to detect new threat patterns, the authors use an attack graph structure to model unprecedented network traffic. This graph for the unknown attack is matched to a pre‐known threat database, which contains attack graphs related to each known threat. The main challenge is to associate unknown traffics to a family of known threats. For this, the authors utilise random walks and pattern theorem. The authors utilise the pattern theorem and apply it to a set of proposed algorithms for detecting new generations of malicious traffics. Under the assumption of having a proper threat database, the authors argue that for each unknown threat, which belongs to a family of threats, it is possible to find at least one matching pattern with high matching rate and sensitivity.

The increase of the capacity of processing units and the growth of distributed computing make easy to collect and process information of Internet traffic flows. This information, however, can be used to perform attacks in anonymous communications that could compromise privacy. With the aim of preventing these attacks, we propose a scheme that implements a multimodal behavior using the random walk theory and crypto‐types. The random walk mechanism is responsible for generating network patterns, and the cryptotype performs the micro‐encryption tasks using series of quantum‐resistant cryptography methods through the anonymous channel. The result shows that using this technique, we can prevent network analysis attacks by means of the generation of a different pattern in each execution for the same set of data. Namely, the experiments we have developed indicate that the average rate of true detections of application behaviors made by intruders does not exceed 24%. Thus, this multimodal pattern gives a high level of immunity against data analysis attacks because the intruders could consider the generated patterns as the typical patterns. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, the main idea is to establish IoT structure for ubiquitous communications. This structure has been made by data type modelling. Type modeling is useful to construct homogeneous computational pattern. A design cycle has been introduced here. This cycle is a wizard to cluster and insert computational nodes. For IoT intentions, data that should be transmitted to the higher protocol layer, must be processed at first. So computational nodes are responsible for these innetwork computations. The problem is response time and time wasting. The experimental results show that this problem can be eliminated by limited case scenario. The amount of transmitted data has been restricted by a higher bound. This type model is efficient and also reliable for ubiquitous communications. The result says that the same resources can be responsible for new computational demands according to the same type operations. In this way computational nodes can be accessible for all nodes.