JOURNAL OF COMPUTING AND SECURITY
Year:2022 | Volume:9 | Issue:1|Papers Count:6

In highly configurable information systems such as SaaS information systems, business process variability management is an important issue. The variability model, which is often called the configurable process model (CPM), can be reused to configure a family of processes each serving a separate purpose or customer. If not already present, these business process variability models have to be “extracted” based on event logs residing in the databases of the target enterprise(s). Such extraction is costly to carry out manually. In this study, inspired by Software Product Line Engineering concepts, we propose a novel automated process-mining-based method by extending the “Alpha” algorithm for process discovery as a preliminary solution. The proposed method takes a set of event logs as input,and in three phases, outputs a CPM in terms of a model called “BPFM”. To evaluate the method, we used the Goal-Question-Metric approach in a case study on 10 cases. For this purpose, input event logs were artificially extracted from the cases’ existing BPFM models and were fed as input to the proposed method. Then, we observed if the output models of the method were similar to the preliminary existing ones. The results showed that the method was promising in identifying the CPMs,since the extracted models involved activities that were 97. 5% identical to what was expected. Moreover, a structural precision of 98% and a structural recall of 97. 3% were obtained. The set of configurations derivable from the output models was 100% similar to and provided 100% coverage over the expected configurations.

The Internet of Things is a paradigm for connecting objects using a common set of network technologies. Implementing IoT by host-to-host communication models, such as IP, faces several challenges. It is because of the heterogeneous and constrained devices that connect temporarily to different networks, and different security domains. Also it requires communication capabilities, for both local network and on a global scale. This paper examines how NDN, a proposed architecture for future of the Internet, can respond to these challenges and provide a safer and more straightforward method for deploying IoT. The NDN content-centric communication model makes it possible to interact directly with the contents, and because of this, IoT networks can be more easily developed and configured. Also we show that using NDN would be more fruitful if it is combined with Fog Computing. So, we propose a four-layer model in order to achieve a better structure for IoT. In this model, NDN is used as the core of the network, and routing operations are performed based on contents' names. Also, Fog Computing is used as the mediator between low-level sensors of IoT, the core network, and at the highest level, the cloud which is a fundamental component in IoT networks. In order to evaluate this model in comparison with common models in IoT, we compare the NDN protocol with the MQTT protocol, one of the most commonly used IoT protocols, based on the amount of resource usage and delay.

In the simulated binary crossover, offspring are generated from parents with a coefficient of variation and uses a probability distribution function for the coefficient and there is a linear relationship between parents and offspring. Most existing methods of crossover operators generate offspring on the solution on the decision space during the search and so far, no suggestion has been proposed on making a regression model for generating the offspring on the objective space. In this paper, a Gaussian linear regression crossover has been proposed. The idea is to apply linear regression to model a relationship between parents and offspring in crossover operations through the Gaussian process. The reason for using this process is that the probability distribution of the simulated binary operator is based on the parent in the mating pool on decision space, while the probability distribution of the proposed method is on objective space in the mating pool. To optimize problems on the combinatorial sets, the proposed method is applied. The performance of the proposed algorithm was tested on Computational Expensive Optimization benchmark tests and indicates that the proposed operator is a competitive and promising approach.

The increase in the use of the Internet and web services and the advent of the fifth generation of cellular network technology (5G) along with ever-growing Internet of Things (IoT) data traffic will grow global internet usage. To ensure the security of future networks, machine learning-based intrusion detection and prevention systems (IDPS) must be implemented to detect new attacks, and big data parallel processing tools can be used to handle a huge collection of training data in these systems. In this paper Apache Spark, a general-purpose and fast cluster computing platform is used for processing and training a large volume of network traffic feature data. In this work, the most important features of the CSE-CIC-IDS2018 dataset are used for constructing machine learning models and then the most popular machine learning approaches, namely Logistic Regression, Support Vector Machine (SVM), three different Decision Tree Classifiers, and Naive Bayes algorithm are used to train the model using up to eight number of worker nodes. Our Spark cluster contains seven machines acting as worker nodes and one machine is configured as both a master and a worker. We use the CSE-CIC-IDS2018 dataset to evaluate the overall performance of these algorithms on Botnet attacks and distributed hyperparameter tuning is used to find the best single decision tree parameters. We have achieved up to 100% accuracy using selected features by the learning method in our experiments.

Combinatorial optimization is the procedure of optimizing an objective function over the discrete configuration space. A genetic algorithm (GA) has been applied successfully to solve various NP-complete combinatorial optimization problems. One of the most challenging problems in applying GA is selecting mutation operators and associated probabilities for each situation. GA uses just one type of mutation operator with a specified probability in the basic form. The mutation operator is often selected randomly in improved GAs that leverage several mutation operators. While an effective GA search occurs when the mutation type for each chromosome is selected according to mutant genes and the problem landscape. This paper proposes an adaptive genetic algorithm that uses Q-learning to learn the best mutation strategy for each chromosome. In the proposed method, the success history of the mutant in solving the problem is utilized for specifying the best mutation type. For evaluating adaptive genetic algorithm, we adopted the traveling salesman problem (TSP) as a well-known problem in the field of optimization. The results of the adaptive genetic algorithm on five datasets show that this algorithm performs better than single mutation GAs up to 14% for average cases. It is also indicated that the proposed algorithm converges faster than single mutation GAs.

Named data networking is one of the recommended architectures for the future of the Internet. In this communication architecture, the content name is used instead of the IP address. To achieve this purpose, a new data structure is added to the nodes of named data networking which is called the Pending Interest Table (PIT). Scalability, memory consumption, and integration are significant challenges in PIT design as it needs to be updated for each packet, and it saves the name of the packet. This paper introduces a new data structure for PIT called DiCuPIT. DiCuPIT is a distributed data structure for the PIT table, that works based on the Cuckoo filter and can cover the three features as above-mentioned. By implementing this PIT, the lookup time shows a 36% reduction compared to the methods based on the Bloom filter and 40% based on hash tables. Moreover, the memory consumption is reduced by 68% compared to the hash tables-based mechanisms and 31% compared to the methods based on the Bloom filter.