40,941 research outputs found
Leveraging Intelligent Building Infrastructure for Event Response
The number of attacks against occupied commercial buildings has increased dramatically around the world and in the United States. These attacks have included active shooters, kinetic devices, and hazardous (i.e., chemical or biological) agents. Typical commercial buildings have no systems to deal with these threats. Even iconic buildings rely primarily on restricted access to provide security. Video cameras are often present but are used primarily for forensics after an event has occurred. However, advanced systems that enable intelligent and connected buildings can be leveraged along with sensor technology to enable detection, notification and response to attacks. These same systems can also be used to enhance response to other emergency events such as grid outages, extreme weather, and earthquakes. This paper will present current research showing how Intelligent Building Technology can be leveraged to provide automated response and situational awareness to attacks and other emergency event situations. Results from whole-building modeling and full-scale testing will be presented for one type of threat. Specifically, CONTAM model results validated with experimental data from a well-instrumented 50,000 sqft testbed building will be presented to demonstrate the capability of using intelligent building infrastructure to affect dispersion of a chemical or biological agent inside a commercial office building
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
Action Stories for Counter Terrorism (extended abstract)
Due to the raised terrorist threat worldwide, there is an urgent need to research that assists security and police services to protect the public and key assets and to prevent attacks from taking place. Successful protection and prevention may require potential and known suspects to be monitored or arrested. These operations are high risk because inappropriate surveillance, interview or arrest may have damaging political, public relations and intelligence effects. In addition to better tracking information on which to base suspicions, the security and police services need to have confidence that operations will yield evidence that can demonstrate conclusively that a deceptive activity such as a terrorist attack was in the process of being planned or executed before an operation takes place
An Implementation of Intrusion Detection System Using Genetic Algorithm
Nowadays it is very important to maintain a high level security to ensure
safe and trusted communication of information between various organizations.
But secured data communication over internet and any other network is always
under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are
various approaches being utilized in intrusion detections, but unfortunately
any of the systems so far is not completely flawless. So, the quest of
betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently
detect various types of network intrusions. Parameters and evolution processes
for GA are discussed in details and implemented. This approach uses evolution
theory to information evolution in order to filter the traffic data and thus
reduce the complexity. To implement and measure the performance of our system
we used the KDD99 benchmark dataset and obtained reasonable detection rate
Autonomic computing architecture for SCADA cyber security
Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator
Cryptocurrency with a Conscience: Using Artificial Intelligence to Develop Money that Advances Human Ethical Values
Cryptocurrencies like Bitcoin are offering new avenues for economic empowerment
to individuals around the world. However, they also provide a powerful tool that
facilitates criminal activities such as human trafficking and illegal weapons sales
that cause great harm to individuals and communities. Cryptocurrency advocates
have argued that the ethical dimensions of cryptocurrency are not qualitatively new,
insofar as money has always been understood as a passive instrument that lacks
ethical values and can be used for good or ill purposes. In this paper, we challenge
such a presumption that money must be ‘value-neutral.’ Building on advances in
artificial intelligence, cryptography, and machine ethics, we argue that it is possible
to design artificially intelligent cryptocurrencies that are not ethically neutral but
which autonomously regulate their own use in a way that reflects the ethical values
of particular human beings – or even entire human societies. We propose a technological framework for such cryptocurrencies and then analyse the legal, ethical, and
economic implications of their use. Finally, we suggest that the development of
cryptocurrencies possessing ethical as well as monetary value can provide human
beings with a new economic means of positively influencing the ethos and values
of their societies
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