Biblio

Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for E-Health system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP.

In this work, novel metaheuristic algorithms are proposed to address the network coding (NC)-based routing and spectrum allocation (RSA) problem in elastic optical networks, aiming to increase the level of security against eavesdropping attacks for the network's confidential connections. A modified simulated annealing, a genetic algorithm, as well as a combination of the two techniques are examined in terms of confidentiality and spectrum utilization. Performance results demonstrate that using metaheuristic techniques can improve the performance of NC-based RSA algorithms and thus can be utilized in real-world network scenarios.

Security services and share information is provided by the computer network. The computer network is by default there is not security. The Attackers can use this provision to hack and steal private information. Confidentiality, creation, changes, and truthful of data is will be big problems in the network. Many types of research have given many methods regarding this, from these methods Generating Initial Chromosome Key called Generating Dynamic Chromosome Key (GDCK), which is a novel approach. With the help of the RSA (Rivest Shamir Adleman) algorithm, GICK and GDCK have created an initial key. The proposed method has produced new techniques using genetic fitness function for the sender and receiver. The outcome of GICK and GDCK has been verified by NIST (National Institute of Standards Technology) tools and analyzes randomness of auto-generated keys with various methods. The proposed system has involved three examines; it has been yield better P-Values 6.44, 7.05, and 8.05 while comparing existing methods.

In open network environments, since there is no centralized authority to monitor misbehaving entities, malicious entities can easily cause the degradation of the service quality. Trust has become an important factor to ensure network security, which can help entities to distinguish good partners from bad ones. In this paper, trust in open network environment is regarded as a self-organizing system, using self-organization principle of human social trust propagation, a genetic trust evaluation method with self-optimization and family attributes is proposed. In this method, factors of trust evaluation include time, IP, behavior feedback and intuitive trust. Data structure of access record table and trust record table are designed to store the relationship between ancestor nodes and descendant nodes. A genetic trust search algorithm is designed by simulating the biological evolution process. Based on trust information of the current node's ancestors, heuristics generate randomly chromosome populations, whose structure includes time, IP address, behavior feedback and intuitive trust. Then crossover and mutation strategy is used to make the population evolutionary searching. According to the genetic searching termination condition, the optimal trust chromosome in the population is selected, and trust value of the chromosome is computed, which is the node's genetic trust evaluation result. The simulation result shows that the genetic trust evaluation method is effective, and trust evaluation process of the current node can be regarded as the process of searching for optimal trust results from the ancestor nodes' information. With increasing of ancestor nodes' genetic trust information, the trust evaluation result from genetic algorithm searching is more accurate, which can effectively solve the joint fraud problem.

Most serious occurrence of modern malware is Botnet. Botnet is a rapidly evolving problem that is still not well understood and studied. One of the main goals for modern network security is to create adequate techniques for the detection and eventual termination of Botnet threats. The article presents an approach for implementing a host-based Intrusion Detection System for Botnet attack detection. The approach is based on a variation of a genetic algorithm to detect anomalies in a case of attacks. An implementation of the approach and experimental results are presented.

Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks.

In a modern software system, when a program fails, a crash report which contains an execution trace would be sent to the software vendor for diagnosis. A crash report which corresponds to a failure could be caused by multiple types of faults simultaneously. Many large companies such as Baidu organize a team to analyze these failures, and classify them into multiple labels (i.e., multiple types of faults). However, it would be time-consuming and difficult for developers to manually analyze these failures and come out with appropriate fault labels. In this paper, we automatically classify a failure into multiple types of faults, using a composite algorithm named MLL-GA, which combines various multi-label learning algorithms by leveraging genetic algorithm (GA). To evaluate the effectiveness of MLL-GA, we perform experiments on 6 open source programs and show that MLL-GA could achieve average F-measures of 0.6078 to 0.8665. We also compare our algorithm with Ml.KNN and show that on average across the 6 datasets, MLL-GA improves the average F-measure of MI.KNN by 14.43%.
 

Security is becoming a major concern in computing. New techniques are evolving every day; one of these techniques is Hash Visualization. Hash Visualization uses complex random generated images for security, these images can be used to hide data (watermarking). This proposed new technique improves hash visualization by using genetic algorithms. Genetic algorithms are a search optimization technique that is based on the evolution of living creatures. The proposed technique uses genetic algorithms to improve hash visualization. The used genetic algorithm was away faster than traditional previous ones, and it improved hash visualization by evolving the tree that was used to generate the images, in order to obtain a better and larger tree that will generate images with higher security. The security was satisfied by calculating the fitness value for each chromosome based on a specifically designed algorithm.
 

Mobile ad hoc network (MANET) is a self-created and self organized network of wireless mobile nodes. Due to special characteristics of these networks, security issue is a difficult task to achieve. Hence, applying current intrusion detection techniques developed for fixed networks is not sufficient for MANETs. In this paper, we proposed an approach based on genetic algorithm (GA) and artificial immune system (AIS), called GAAIS, for dynamic intrusion detection in AODV-based MANETs. GAAIS is able to adapting itself to network topology changes using two updating methods: partial and total. Each normal feature vector extracted from network traffic is represented by a hypersphere with fix radius. A set of spherical detector is generated using NicheMGA algorithm for covering the nonself space. Spherical detectors are used for detecting anomaly in network traffic. The performance of GAAIS is evaluated for detecting several types of routing attacks simulated using the NS2 simulator, such as Flooding, Blackhole, Neighbor, Rushing, and Wormhole. Experimental results show that GAAIS is more efficient in comparison with similar approaches.