DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets (GRMMP) in SDN as detection feature. Then, the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks. After training, the model can be used to infer whether SDN is under DoS attacks, and a DeepFM-based detection method for DoS attacks against client host is implemented. Simulation results show that our method can effectively detect DoS attacks in SDN. Compared with the K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) models, Support Vector Machine (SVM) and Random Forest models, our proposed method outperforms in accuracy, precision and F1 values.     

Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is secure. In the traditional blockchain, the data layer uses a Merkle hash tree to store data; however, the Merkle hash tree cannot provide non-member proof, which makes it unable to resist the attacks of malicious nodes in networks. To solve this problem, this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and nonmember proof. Moreover, the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements. This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model. Finally, this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.     

A new fuzzy C-means method for magnetic resonance image brain segmentation

In this paper, we introduce a new fuzzy c-means (FCM) method in order to improve the magnetic resonance images’ (MRIs) segmentation. The proposed method combines the FCM and possiblistic c-means (PCM) functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility (typicality) function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.     

Optimizing the distributed generators integration in electrical distribution networks: efficient modified forensic-based investigation

Neural Computing and Applications - As a result of various loads, including critical installations (industries, nuclear facilities, etc.), electrical distribution networks (EDNs) must operate...