Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

/pdf/multiobjective-immune-algorithm-with-nondominated-neighbor-3m342iujs2.pdf

Multiobjective immune algorithm with nondominated neighbor-based selection

Low-voltage distribution networks are emerging as an increasingly important component of power system operations From a computational standpoint, the proactive utilization of these resources places daunting challenges due to their vast number and the non-linear physics that govern power flow in low-voltage networks For this reason, a hierarchical approach to the organization of distribution markets which can better cope with computational scalability may be desirable This paper models various alternatives to the coordination of transmission and distribution system operations, and investigates their relative performance on a small-scale network in terms of allocative efficiency, consistency with physical constraints, and pricing,

Coordination Schemes for the Integration of Transmission and Distribution System Operations

An evaluation model is designed to describe the relationship of security and QoS.A classical NSGA-II algorithm is revised to optimize the multi-objective model.A novel crossover approach performs information exchange based on each role.The found solutions simultaneously meet the requirements of security and QoS. Both network security and quality of service (QoS) consume computational resource of IT system and thus may evidently affect the application services. In the case of limited computational resource, it is important to model the mutual influence between network security and QoS, which can be concurrently optimized in order to provide a better performance under the available computational resource. In this paper, an evaluation model is accordingly presented to describe the mutual influence of network security and QoS, and then a multi-objective genetic algorithm NSGA-II is revised to optimize the multi-objective model. Using the intrinsic information from the target problem, a new crossover approach is designed to further enhance the optimization performance. Simulation results validate that our algorithm can find a set of Pareto-optimal security policies under different network workloads, which can be provided to the potential users as the differentiated security preferences. These obtained Pareto-optimal security policies not only meet the security requirement of the user, but also provide the optimal QoS under the available computational resource.

Optimizing security and quality of service in a Real-time database system using Multi-objective genetic algorithm

Optimizing security and quality of service in a real-time operating system using multi-objective Bat algorithm

Blockchain is a significant advancement in technology recently, transforming the traditional centralized system into a decentralized one. Smart contracts, as one of the best applications of blockchain, show great potential in various fields, such as finance, supply chain, and the Internet of Things (IoT). As the world’s first blockchain platform to support turing complete smart contracts, Ethereum has become the most critical infrastructure for the digital world. However, with the vigorous development of smart contracts, malicious attacks against smart contracts have frequently occurred in recent years. The issue of smart contract security has attracted widespread attention due to the huge financial losses caused by smart contract vulnerabilities. Although researchers have made some progress in detecting smart contract vulnerabilities through symbolic execution and fuzzing-based methods, existing methods mainly rely on expert knowledge and hand-crafted features, leading to many detection errors. Even worse, existing methods take tens of seconds or even minutes to detect each smart contract on average, which is extremely time-consuming. In this work, we present SVScanner, the new method combining two features of heterogeneous patterns to detect smart contract vulnerabilities in the blockchain. Specifically, we first extract global semantic features from the sequence of contract code tokens. Then we further use the attention mechanism to capture deep structural semantics from the Abstract Syntax Tree (AST) of smart contracts. Finally, we combine these two features from different patterns and use a text convolutional neural network (TextCNN) to detect contract bugs. Experimental results show that SVScanner has the ability to detect vulnerabilities effectively in real-world smart contract datasets. SVScanner achieves a 7.33% improvement in accuracy compared with other traditional methods. Moreover, our method requires significantly less detection time. 

SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction

Software vulnerabilities have always been an essential issue in cyberspace, for which many vulnerability detection techniques have been investigated. Among them, deep learning-based detection techniques have demonstrated promising detection results. However, due to the various programming patterns of developers, vulnerabilities are usually associated with the code context, such as Internet of Things (IoT) programs. Therefore, we propose a contextual embedding model to integrate three hybrid models, CLSTM, CBiLSTM (sequential structure), and CNN-BiLSTM (parallel structure), based on the code characteristics of IoT applications. To further improve the precision and robustness, we apply information augment by adding synthetic data to the real-world vulnerability data to address the severe data imbalance and facilitate neural models learning vulnerability patterns. The new method inherits the architecture of CodeBERT with multiheaded attention mechanisms and learns a richer set of vulnerable code patterns with long-range context dependencies when processing code sequence data. To assess the effectiveness of hybrid contextual embedding, we contrast the neural network models developed using the representations obtained from the three embedding methods, including CodeBERT, Word2Vec, and FastText. The experiments involve IoT applications and well-known open-source APIs. The results show that the hybrid model built based on the neural features outperforms the non-hybrid model in vulnerability detection. 

Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis

With the development of both economy and society,service always requires higher and higher security and quality.However, since security services always induce extra resource consumption, QoS may descend evidently. It is important and essential to find optimal solutions that can not only achieve enough level of security, but also meet as high QoS as system can. In this paper, we propose an entity, named QoS amending function, which is used to evaluate the end-to-end delay induced by security in communication network. Moreover, an adaptive immune algorithm is used to obtained optimal parameters on both security and QoS. Simulations show that the proposed model is effective to achieve optimal balance between security and QoS.

Impact of Security on QoS in Communication Network

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Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis

Impact of Security on QoS in Communication Network