Jesus Pacheco

IoT Security Framework for Smart Cyber Infrastructures

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.

Anomaly behavior analysis for IoT sensors

The Internet of Things (IoT) will not only connect computers and mobile devices but also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real time and require large storage and computational power. The integration of IoT with fog and cloud computing not only brings the computational requirements but also enables IoT services to be pervasive, cost-effective, and accessible from anywhere and at anytime. In any IoT application, sensors are indispensable to bring the physical world into the digital world that can be implemented by leveraging fog computing. However, IoT sensors will introduce major security challenges as they contribute to a significant increase in the IoT attack surface. In this paper, we present a methodology to develop an intrusion detection system on the basis of anomaly behavior analysis to detect when a sensor has been compromised and used to provide misinformation. Our preliminary experimental results show that our approach can accurately authenticate sensors on the basis of their behavior and can detect known and unknown sensor attacks with high detection rate and low false alarms.

IoT Security Development Framework for building trustworthy Smart car services

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect Smart cars, buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, with the introduction of IoT, we will be experiencing grand challenges to secure and protect its advanced information services due to the significant increase of the attack surface, complexity, heterogeneity and number of interconnected resources. In this paper, we present an IoT Security Development Framework (ISDF) to build trustworthy and highly secure applications and services. The ISDF enables developers to consider security issues at all IoT layers and integrate security algorithms with the functions and services offered in each layer rather than considering security in an ad-hoc and after thought manner. We also show how this framework can be used to develop highly secure and trustworthy Vehicle Information and management Portal (VIMP) services and how to apply our Anomaly Behavior Analysis (ABA) methodology to secure and protect these services against any type of attacks.

Secure and Resilient Cloud Services for Enhanced Living Environments

It is critical to provide enhanced living environments (ELEs) to people with special needs (such as the elderly and individuals with disabilities) that offer 24/7 continuous monitoring and control of the environment and access to care services when needed. Recently, there has been a strong interest in building ELEs using implantable and wearable sensors, and wireless sensor networks that are supported by cloud computing. However, ELE technologies and information are vulnerable to cyberattacks and exploitations that can lead to life-threatening scenarios such as incorrect medical diagnoses. This article presents a platform that offers secure and resilient services for ELEs. The main components of the platform are the ELE end nodes, secure gateway, and a secure and resilient cloud computing system. End nodes collect ELE variables and human body signals that are stored securely in the cloud using a secure gateway. The secure gateway manages communication between the end nodes and the cloud services using biocyber metrics for authentication. In addition, the cloud architecture provides the required ELE services at any time and from anywhere in a resilient manner.

IoT framework for smart buildings with cloud computing

The application domain of Urban Internet of Things systems are designed for the objective of the Smart City: to take advantage of the latest communication technologies in order to provide optimal services. One important element of Smart Cities is the Smart Building. Smart Building (SB) is a new concept where the building benefits from the development of automation and communication technologies to create smart environments which are more efficient in the use of the available resources and much more secure. Such smart systems are complex, dynamic and heterogeneous. Thus, SB need more and more computational power which can be obtained by using cloud computing. In this paper we present a framework used to provide the needed computational power to the SB by using Cloud Computing. Our idea is to have all the computational power as well as control and monitor capabilities in the cloud. We have evaluated and validated our approach by applying our framework in Sonora Tecnología Científica, a facility which is intended to be a Smart Building integrated to the Internet of Things.

Anomaly Behavior Analysis System for ZigBee in smart buildings

Smart Building (SB) exploits advances in information and communication technologies in order to provide the next generation of information and automation services that will significantly reduce operational costs and improve performance and efficiency. SB elements are typically interconnected using short range wireless communication technologies such as ZigBee, which is the most used wireless communication protocol for SBs. However, ZigBee protocol has multiple vulnerabilities that can be exploited by cyberattacks. In this paper, we present an Anomaly Behavior Analysis System (ABAS) for ZigBee protocol to be used in SBs. Our ABAS can detect both known and unknown ZigBee attacks with a high detection rate and low false alarms. Additionally, after detection, our system classifies the attack based on the impact, origin, and destination. We evaluate our approach by launching many attack scenarios such as DoS, Flooding, and Pulse DoS attacks, and then we compare our results with other intrusion detection systems such as secure HAN, signature IDS, and specification IDS.

Autonomic Identity Framework for the Internet of Things

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.

Anomaly behavior analysis for building automation systems

Advanced networking technology and increasing information services have led to extensive interconnection between Building Automation Systems (BAS) communication protocols and Internet, which makes Fog computing service a potential solution for automation of building end devices. However, the connection to Internet and public networks increases significantly the risk of the BAS networks being attacked due mainly to the significant increase in the attack surface. In this paper, we present an anomaly based Intrusion Detection System (IDS) that combines context awareness and Cyber DNA techniques to detect network misbehavior from security and functionality perspectives. We developed runtime models for service interactions and functionality patterns by modeling the information that is continuously acquired from building assets into two novel data structures: Protocol Context Aware and sensor-DNA. Our IDS uses Anomaly Behavior Analysis techniques to accurately detect anomalous events triggered by cyber-attacks or any failure. A classification of detected attacks allow our IDS to automatically launch protective countermeasures. We evaluate our approach in the Smart Building testbed developed at the University of Arizona Center for Cloud and Autonomic Computing, by launching several cyber-attacks that exploit the generic vulnerabilities of BAS.

Autoinfotainment Security Development Framework (ASDF) for Smart Cars

The Autoinfotainment system will not only provide information systems and entertainment to car components, but it will also connect to the Internet and a wide range of multimedia and mobile devices. However, with the introduction of many smart devices and a variety of wireless communications through Wi-Fi, Bluetooth, DSRC, and cellular, we are experiencing major challenges to secure and protect vehicular advanced information and entertainment services due to the significant increase of the attack surface, complexity, heterogeneity and number of interconnected resources. In this paper, we present an Auto Security Development Framework (ASDF) to build trustworthy and highly secure auto information and entertainment services. The ASDF enables developers to consider security issues at all the auto car communications layers and integrate security algorithms with the functions and services offered in each layer rather than considering security in an ad-hoc and after thought manner. We also show how this framework can be used to develop anomaly behavior analysis algorithm to detect wireless attacks against the QUALCOMM DragonBoard Autoinfotainment system.

Enabling Risk Management for Smart Infrastructures with an Anomaly Behavior Analysis Intrusion Detection System

The Internet of Things (IoT) connects not only computers and mobile devices, but it also interconnects smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, IoT applications introduce grand security challenges due to the increase in the attack surface. Current security approaches do not handle cybersecurity from a holistic point of view; hence a systematic cybersecurity mechanism needs to be adopted when designing IoTbased applications. In this work, we present a risk management framework to deploy secure IoT-based applications for Smart Infrastructures at the design time and the runtime. At the design time, we propose a risk management method that is appropriate for smart infrastructures. At the design time, our framework relies on the Anomaly Behavior Analysis (ABA) methodology enabled by the Autonomic Computing paradigm and an intrusion detection system to detect any threat that can compromise IoT infrastructures by. Our preliminary experimental results show that our framework can be used to detect threats and protect IoT premises and services.