Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy.

In this paper, we propose DNA-Genetic Encryption Technique (D-GET) in order to make the technique more secure and less predictable. In this technique, binaries any type of digital data and convert it to DNA sequencing, reshape, encrypt, crossover, mutate and then reshape. The main stages of D-GET are repeated three times or more. Transmit the encrypted data in text/image format file. In other side, the receiver uses the D-GET to decrypt the received data and reshape it to original format. This Technique also transforms the text into an image and vice versa to improve security and multiple key sequences to increase the degree of diffusion and confusion, which makes resulting cipher data difficult to decipher and makes to realize a perfect secrecy system. Experimental results demonstrate that proposed technique has multilayer protection stages against different attacks and higher level of security based on the multi-stages and genetic operations. Decrypted data are acceptable because of there is absolutely difference between it and secret data.

Chemoinformatics clustering algorithms are important issues for drug discovery process. So, there are many clustering algorithms that are available for analyzing large chemical data sets of medium and high dimensionality. The quality of these algorithms depends on the nature of data sets and the accuracy needed by the application. The applications of clustering algorithms in the drug discovery process are compound selection, virtual library generation, High-Throughput Screening (HTS), Quantitative Structure-Activity Relationship (QSAR) analysis and Absorption, Distribution, Metabolism, Elimination and Toxicity (ADMET) prediction. Based on Structure-Activity Relationship (SAR) model, compounds with similar structure have similar biological activities. So, clustering algorithms must group more similar compounds in one cluster. K-Means, bisecting K-Means and Ward clustering algorithms are the most popular clustering algorithms that have a wide range of applications in chemoinformatics. In this paper, a comparative study between these algorithms is presented. These algorithms are applied over homogeneous and heterogeneous chemical data sets. The results are compared to determine which algorithms are more suitable depending on the nature of data sets, computation time and accuracy of produced clusters. Accuracy is evaluated using standard deviation metric. Experimental results show that K-Means algorithm is preferable for small number of clusters for homogeneous and heterogeneous data sets in terms of time and standard deviation. Bisecting K-Means and Ward algorithms are preferable for large number of clusters for homogeneous and heterogeneous data sets in term of standard deviation, but bisecting K-Means algorithm is preferable in term of time.

Negotiation is a process of reaching an agreement on the terms of a transaction such as price, quantity, for two or more parties in multi-agent systems such as E-Commerce. It tries to maximize the benefits for all parties. Argumentation-based negotiation has been proposed as an alternative to proposalbased approaches such as game theory and heuristic. The main advantage is that it allows agents to exchange additional information rather than just simple proposals. This property of argumentation protocols can lead to beneficial agreements when used for complex multi agent negotiation. This paper presents an empirical comparison of argumentation-based negotiation to proposal-based negotiation in a strategic two-player scenario. It is implemented through a well established FIPA-complaint Agent Toolkit, JADE. Our experiments show that the argumentation-based approach outperforms the proposal-based approach with respect to the quality of the agreements found and the quantity of unsuccessful negotiations.

The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs' testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.