This paper describes the evolution of our research from video analytics to a global security system with focus on the video surveillance component. Indeed video surveillance has evolved from a commodity security tool up to the most efficient way of tracking perpetrators when terrorism hits our modern urban centers. As number of cameras soars, one could expect the system to leverage the huge amount of data carried through the video streams to provide fast access to video evidences, actionable intelligence for monitoring real-time events and enabling predictive capacities to assist operators in their surveillance tasks. This research explores a hybrid platform for video intelligence capture, automated data extraction, supervised Machine Learning for intelligently assisted urban video surveillance; Extension to other components of a global security system are discussed. Applying Knowledge Management principles in this research helps with deep problem understanding and facilitates the implementation of efficient information and experience sharing decision support systems providing assistance to people on the field as well as in operations centers. The originality of this work is also the creation of "common" human-machine and machine to machine language and a security ontology.
University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in `the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better `tapped in' can be more effective. In this paper we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation and analytics in an end-to-end pi…
The workshop will focus on the application of artificial intelligence to problems in cyber security. AICS 2020 emphasis will be on human-machine teaming within the context of cyber security problems and will specifically explore collaboration between human operators and AI technologies. The workshop will address applicable areas of AI, such as machine learning, game theory, natural language processing, knowledge representation, automated and assistive reasoning and human machine interactions. Further, cyber security application areas with a particular emphasis on the characterization and deplo…
Rochester Institute of Technology（罗切斯特理工学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；IBM Research（IBM研究院）
Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence to inform them of indicators of compromise, attack techniques, and preventative actions. Security analysts prepare threat analysis reports after investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Collectively known as cyber threat intelligence (CTI), the reports are typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing intrusion d…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Open-Source Projects and Libraries are being used in software development while also bearing multiple security vulnerabilities. This use of third party ecosystem creates a new kind of attack surface for a product in development. An intelligent attacker can attack a product by exploiting one of the vulnerabilities present in linked projects and libraries. In this paper, we mine threat intelligence about open source projects and libraries from bugs and issues reported on public code repositories. We also track library and project dependencies for installed software on a client machine. We repres…
University of California, Berkeley（加州大学伯克利分校）；Microsoft Corporation（微软公司）
To remain aware of the fast-evolving cyber threat landscape, open-source Cyber Threat Intelligence (OSCTI) has received growing attention from the community. Commonly, knowledge about threats is presented in a vast number of OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI gathering and management platforms, however, have primarily focused on isolated, low-level Indicators of Compromise. On the other hand, higher-level concepts (e.g., adversary tactics, techniques, and procedures) and their relationships have been overlooked, which contain essential knowledge abo…
This volume represents the proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop 2019, held on January 27, 2019 in Honolulu, Hawaii.
University of Maryland University College（马里兰大学学院分校）
Attacks to networks are becoming more complex and sophisticated every day. Beyond the so-called script-kiddies and hacking newbies, there is a myriad of professional attackers seeking to make serious profits infiltrating in corporate networks. Either hostile governments, big corporations or mafias are constantly increasing their resources and skills in cybercrime in order to spy, steal or cause damage more effectively. traditional approaches to Network Security seem to start hitting their limits and it is being recognized the need for a smarter approach to threat detections. This paper provide…
Seoul National University（首尔大学）；AI Institute (AIIS), Seoul National University（首尔大学人工智能研究院）；Dongguk University（东国大学）
We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly challenging task, and evaluation methods for adequately measuring and analyzing the progress of AI agent are lacking as well. In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents. We define a general format and procedure of the Video Turing Test and present a case study to confirm the effectiv…
General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many diffe…
University of Florida（佛罗里达大学）；Worcester Polytechnic Institute（伍斯特理工学院）
Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design; specifically the design of digital and analog integrated circuits~(ICs), where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time-to-market, and the growing number of ubiquitous interconne…
If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might …
RealAI（瑞莱智慧）；Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；China Nanhu Academy of Electronics and Information Technology（中国电子科技南湖研究院）；Xi’an Jiaotong University（西安交通大学）；Tencent YouTu Lab（腾讯优图实验室）；China Construction Bank Fintech（中国建设银行金融科技公司）；RippleInfo（涟漪信息科技）
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competi…
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Anci…
Artificial Intelligence enhanced Security Problems in Real-Time Scenario using Blowfish Algorithm
St. Peter's Engineering College (Autonomous), Hyderabad（圣彼得工程学院（自治），海得拉巴）；Lendi Institute of Engineering and Technology (A), JNTU GV, Vizianagaram, Andhra Pradesh, India（伦迪工程技术学院（A），JNTU GV，维齐亚纳加勒姆，安得拉邦，印度）
Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
Dept. of Information Studies, Trine University（信息研究系，特林大学）；Dept. of Business Information Systems, Central Michigan University（商业信息系统系，中央密歇根大学）；Dept. of CS, Maharishi International University（计算机科学系， Maharishi 国际大学）
MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Xi’an Jiaotong University（西安交通大学）；University of Hong Kong（香港大学）；Fudan University（复旦大学）；University of California, Los Angeles（加州大学洛杉矶分校）
The Cyber Security Cooperative Research Centre (CSCRC)（网络安全合作研究中心（CSCRC））；Penn State（宾夕法尼亚州立大学）；RMIT University（皇家墨尔本理工大学）；The University of Technology Sydney（悉尼科技大学）
The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversaria…
FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models
RWTH Aachen University（亚琛工业大学）；University of Applied Sciences Duesseldorf（杜塞尔多夫应用技术大学）
Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature …
Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security
Karolinska Institutet（卡罗林斯卡学院）；Democritus University of Thrace（色雷斯德谟克利特大学）；IntelliSolutions（智解公司）；HEDNO SA（HEDNO SA公司）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mayo Clinic（梅奥诊所）；Auburn University（奥本大学）
As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to red team this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to o…
Laboratory for Physical Sciences（物理科学实验室）；Booz Allen Hamilton（博思艾伦汉密尔顿）；National Security Agency（美国国家安全局）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Singapore Management University（新加坡管理大学）；Harvard University（哈佛大学）；Washington University in Saint Louis（圣路易斯华盛顿大学）
The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Additionally, adversaries continue to develop new attacks. Hence, AI methods are required to understand and protect the cyber domain. These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. In general, AI techniques are still not widely adopted in the real world. Reasons include: (1) a lack of certification of AI for s…
Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPF algorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.
Proof of Useful Intelligence (PoUI): Blockchain Consensus Beyond Energy Waste
School of Science and Technology（科学与技术学院）；Kwansei Gakuin University（片山大学）；School of Engineering & Computer Science（工程与计算机科学学院）；Victoria University of Wellington（威灵顿维多利亚大学）
Prince Sultan University（苏丹王子大学）；Benha University（本哈大学）
Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traff…
Studies suggest that one in three US adults use the Internet to diagnose or learn about a health concern. However, such access to health information online could exacerbate the disparities in health information availability and use. Health information seeking behavior (HISB) refers to the ways in which individuals seek information about their health, risks, illnesses, and health-protective behaviors. For patients engaging in searches for health information on digital media platforms, health literacy divides can be exacerbated both by their own lack of knowledge and by algorithmic recommendatio…
Exploring the Impact of Generative Artificial Intelligence on Software Development in the IT Sector: Preliminary Findings on Productivity, Efficiency and Job Security
VGI-Bench: Probing Visual Intelligence in Video Generation Models
University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Tsinghua University（清华大学）；University of Waterloo（滑铁卢大学）；Massachusetts Institute of Technology（麻省理工学院）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（矢量研究所）；Microsoft Research（微软研究院）；Etude AI
LICEF Research Center, Université TÉLUQ（魁北克大学远程教育大学LICEF研究中心）；LORIA, Université de Lorraine（洛林大学洛林计算机科学及其应用实验室）；Université de Sherbrooke（舍布鲁克大学）；Laplace Insights（拉普拉斯洞察公司）
This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, watching these videos to derive actionable insights could be challenging and complex. In this paper, we leverage Whisper, a speech-to-text model from OpenAI, to generate a text corpus of market coverage videos from Bloomberg and Yahoo Finance. We employ natural language processing to extract insights regarding language use from the market coverage. Moreover, we examine the prominent presence of trending topics and thei…
