Many systems or applications have been developed for distributed environments with the goal of attaining multiple objectives in the face of environmental challenges such as high dynamics/hostility or severe resource constraints (e.g., energy or communications bandwidth). Often the multiple objectives are conflicting with each other, requiring optimal tradeoff analyses between the objectives. This paper is mainly concerned with how to model multiple objectives of a system and how to optimize their performance. We first conduct a comprehensive survey of the state-of-the-art modeling and solution techniques to solve multi-objective optimization problems. In addition, we discuss pros and cons of each modeling and optimization technique for in-depth understanding. Further, we classify existing approaches based on the types of objectives and investigate main problem domains, critical tradeoffs, and key techniques used in each class. We discuss the overall trends of the existing techniques in terms of application domains, objectives, and techniques. Further, we discuss challenging issues based on the inherent nature of multi-objective optimization problems. Finally, we suggest future work directions in terms of what critical design factors should be considered to design and analyze a system with multiple objectives.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

Centrality metrics have been studied in the network science research. They have been used in various networks, such as communication, social, biological, geographic, or contact networks under different disciplines. In particular, centrality metrics have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed from 1940s, only some centrality metrics (e.g., degree, betweenness, or cluster coefficient) have been commonly in use. This paper aims to introduce various existing centrality metrics and discusses their applicabilities in various networks. In addition, we conducted extensive simulation study in order to demonstrate and analyze the network resilience of targeted attacks using the surveyed centrality metrics under four real network topologies. We also discussed algorithmic complexity of centrality metrics surveyed in this work. Through the extensive experiments and discussions of the surveyed centrality metrics, we encourage their use in solving various computing and engineering problems in networks.

Defensive deception is a promising approach for cyber defense. Via defensive deception, a defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding some of its resources. Although defensive deception is garnering increasing research attention, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e.g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process. To support this research, we construct MOCHEG, a large-scale dataset consisting of 15,601 claims where each claim is annotated with a truthfulness label and a ruling statement, and 33,880 textual paragraphs and 12,112 images in total as evidence. To establish baseline performances on MOCHEG, we experiment with several state-of-the-art neural architectures on the three pipelined subtasks: multimodal evidence retrieval, claim verification, and explanation generation, and demonstrate that the performance of the state-of-the-art end-to-end multimodal fact-checking does not provide satisfactory outcomes. To the best of our knowledge, we are the first to build the benchmark dataset and solutions for end-to-end multimodal fact-checking and explanation generation. The dataset, source code and model checkpoints are available at https://github.com/VT-NLP/Mocheg.