An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate

Distribution of Heterogeneous Traffic Resources for 5G Cognitive Radio Networks based on Cooperative Learning and QOE

In addition to offering faster speeds and lower latency, 5G networks provide enhanced availability, significantly higher capacity, improved stability, and superior connectivity. The Mean Opinion Score (MOS), which quantifies the subjective quality of the user experience, has emerged as a widely accepted metric for assessing the quality of various types of network traffic. As we move towards the 5G era, measuring end-user quality has become increasingly important in the evolution of wireless communications. This paper presents a resource allocation approach for 5G cognitive radio networks that leverages cooperative learning and prioritizes Quality of Experience (QoE) for integrated heterogeneous traffic. The solution is based on a distributed underlay Dynamic Spectrum Access (DSA) system that utilizes MOS as a foundational metric for managing resource allocation across real-time video and data traffic, each with distinct characteristics. The proposed technique is designed to meet strict interference limitations for primary users while simultaneously maximizing the overall MOS. This is accomplished by employing reinforcement learning in a system where primary users and secondary users share the same frequency band of interest, ensuring efficient coexistence. Importantly, MOS serves as a standardized measure that allows for training across nodes carrying various types of traffic without compromising performance.&nbsp

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Multi-objective predictive control optimization with varying term objectives : a wind farm case study

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park

Theoretical and implementation improvements for difference evaluation functions

Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each individual agent on the overall system performance must be ascertained. Difference evaluation functions aim to solve this credit assignment problem, by approximating the effect that each agent has on the system evaluation function. Difference evaluations have proven to produce superior learned policies in many multiagent settings. Although difference evaluations have produced excellent empirical results, there are still three key research questions that must be addressed regarding their usefulness in real-world systems. More specifically, the performance, theoretical advantages, and methodology for implementation must be addressed in order to demonstrate that difference evaluations are practical for use in real-world multiagent learning. These research questions are addressed in this dissertation. The first contribution of this dissertation is to demonstrate that difference evaluations may be extended and combined with other coordination mechanisms, resulting in superior learned performance. The second contribution of this dissertation is to derive conditions which guarantee that difference evaluations will outperform traditional coordination mechanisms. The third and final contribution of this dissertation is to demonstrate that difference evaluations may be approximated using only local knowledge, allowing for their implementation in any generic multiagent learning setting. By addressing the performance, theoretical foundation, and implementation concerns of difference evaluations, this dissertation provides a detailed analysis demonstrating the usefulness of difference evaluation functions in multiagent learning systems

A DECISION SUPPORT TOOL ON DERELICT BUILDINGS FOR URBAN REGENERATION

Abstract. We present a decision suppport tool for the comparison and selection of projects of integrated renovation of derelict buildings and areas for the purpose of urban regeneration. Each project is defined as a subset of derelict properties to renovate together with their respective designated use, and is scored by the decision support tool on two criteria: expected effort and estimated effectiveness in terms of improved urban capabilities in the urban area of interest. The expected effort is estimated as a global transformation cost, factoring in legal and management overhead costs as well as possible economies of scale. The effectiveness in evaluated in terms of extension of urban capabilities centred on walkable distances. We have implemented a bi-objective evolutionary search algorithm to address the computational complexity of the problem of search for efficient (non-dominated) projects over the two criteria. For the purpose of illustration, we present an example case-study application on the historical core of the city of Sassari, Italy.</p

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS