Collaborating Authors


Information Security and Privacy


Mobile Web/Mobile apps (for work) Cookies Search engines - everything you search is tracked Google mapping - location tracking malicious links and scams Bluetooth and wireless security and hot spots anti-virus software Security threats in collaborative activity - sharing features Social Media Blogging & personal web sites that are tied to work Using 3rd party applications Business Continuity Planning Responding to an emergency/mishap (virus attack/stolen laptop) Information classification (company-specific?) / Data Classification Policy Business Identity Theft Advertisements (check for searching competency) Equipping yourself for Data Recovery (backups/best practices) FTP/Network protocol/network security Organizational Independence Hard Drive/USBs

Using AI to Reduce IoT Vulnerability


This article considers the use of artificial intelligence to help security professionals protect IoT systems. The Internet of Things (IoT) is still in its infancy, but threats to IoT systems and their potential for harm have become quite sophisticated. There are two reasons for this: the value of data and systems that IoT vulnerabilities can give access to; and the high number of potential attack vectors – discrete elements of IoT networks that are vulnerable to foul play. Artificial intelligence (AI) software and algorithms help security professionals to wrest control of this technological battleground back from hackers and protect the IoT as it reaches maturity. Only introduced in 2008, the Internet of Things and IoT systems are still fairly nebulous concepts, subjects of numerous and sometimes conflicting definitions.

Video Intelligence as a component of a Global Security system Artificial Intelligence

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.

Automated Security Assessment for the Internet of Things Artificial Intelligence

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and potential vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

IoT Security Challenges and Risk Mitigation Strategies


The Internet of Things plays a key role in digital transformation. However, in many cases, organizations realize that they already have a large fleet of legacy IoT devices that have been gradually deployed over the years. Many of these devices may not have been designed with security in mind. One of the biggest concerns of IoT is managing the risks associated with a growing number of IoT devices. Information security and privacy issues related to IoT devices have attracted global attention; these devices have the ability to interact with the physical world.

Machine Learning in IoT Security: Current Solutions and Future Challenges Machine Learning

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.

Artificial Intelligence: The Key Cybersecurity Weapon in the IoT Era


As businesses struggle to combat increasingly sophisticated cybersecurity attacks, the severity of which is exacerbated by both the vanishing IT perimeters in today's mobile and IoT era, coupled with an acute shortage of skilled security professionals, IT security teams need both a new approach and powerful new tools to protect data and other high-value assets. Increasingly, they are looking to artificial intelligence (AI) as a key weapon to win the battle against stealthy threats inside their IT infrastructures, according to a new global research study conducted by the Ponemon Institute on behalf of Aruba, a Hewlett Packard Enterprise company. The Ponemon Institute study, entitled "Closing the IT Security Gap with Automation & AI in the Era of IoT," surveyed 4,000 security and IT professionals across the Americas, Europe and Asia to understand what makes security deficiencies so hard to fix, and what types of technologies and processes are needed to stay a step ahead of bad actors within the new threat landscape. The research revealed that in the quest to protect data and other high-value assets, security systems incorporating machine learning and other AI-based technologies are essential for detecting and stopping attacks that target users and IoT devices. Twenty four percent of Indian respondents said they currently use some form of machine-learning or other AI-based security solution, with another 29 percent stating they plan on deploying these types of products within the next 12 months.

A Self-Adaptive Network Protection System Artificial Intelligence

In this treatise we aim to build a hybrid network automated (self-adaptive) security threats discovery and prevention system; by using unconventional techniques and methods, including fuzzy logic and biological inspired algorithms under the context of soft computing.