Biblio

The notion of Zero Trust Architecture (ZTA) has been introduced as a fine-grained defense approach. It assumes that no entities outside and inside the protected system can be trusted and therefore requires articulated and high-coverage deployment of security controls. However, ZTA is a complex notion which does not have a single design solution; rather it consists of numerous interconnected concepts and processes that need to be assessed prior to deciding on a solution. In this paper, we outline a ZTA design methodology based on cyber risks and the identification of known high security risks. We then discuss challenges related to the design and deployment of ZTA and related solutions. We also discuss the role that service technology can play in ZTA.

Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.

User's digital identity information has privacy and security requirements. Privacy requirements include confidentiality of the identity information itself, anonymity of those who verify and consume a user's identity information and unlinkability of online transactions which involve a user's identity. Security requirements include correctness, ownership assurance and prevention of counterfeits of a user's identity information. Such privacy and security requirements, although conflicting, are critical for identity management systems enabling the exchange of users' identity information between different parties during the execution of online transactions. Addressing all such requirements, without a centralized party managing the identity exchange transactions, raises several challenges. This paper presents a decentralized protocol for privacy preserving exchange of users' identity information addressing such challenges. The proposed protocol leverages advances in blockchain and zero knowledge proof technologies, as the main building blocks. We provide prototype implementations of the main building blocks of the protocol and assess its performance and security.

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

Policy based Security Management in an accepted practice in the industry, and required to simplify the administrative overhead associated with security management in complex systems. However, the growing dynamicity, complexity and scale of modern systems makes it difficult to write the security policies manually. Using AI, we can generate policies automatically. Security policies generated automatically can reduce the manual burden introduced in defining policies, but their impact on the overall security of a system is unclear. In this paper, we discuss the security metrics that can be associated with a system using generative policies, and provide a simple model to determine the conditions under which generating security policies will be beneficial to improve the security of the system. We also show that for some types of security metrics, a system using generative policies can be considered as equivalent to a system using manually defined policies, and the security metrics of the generative policy based system can be mapped to the security metrics of the manual system and vice-versa.

Traditionally, a Certification Authority (CA) is required to sign, manage, verify and revoke public key certificates. Multiple CAs together form the CA-based Public Key Infrastructure (PKI). The use of a PKI forces one to place trust in the CAs, which have proven to be a single point-of-failure on multiple occasions. Blockchain has emerged as a transformational technology that replaces centralized trusted third parties with a decentralized, publicly verifiable, peer-to-peer data store which maintains data integrity among nodes through various consensus protocols. In this paper, we deploy three blockchain-based alternatives to the CA-based PKI for supporting IoT devices, based on Emercoin Name Value Service (NVS), smart contracts by Ethereum blockchain, and Ethereum Light Sync client. We compare these approaches with CA-based PKI and show that they are much more efficient in terms of computational and storage requirements in addition to providing a more robust and scalable PKI.

According to recent reports, the most common insider threats to systems are unauthorized access to or use of corporate information and exposure of sensitive data. While anomaly detection techniques have proved to be effective in the detection of early signs of data theft, these techniques are not able to detect sophisticated data misuse scenarios in which malicious insiders seek to aggregate knowledge by executing and combining the results of several queries. We thus need techniques that are able to track users' actions across time to detect correlated ones that collectively flag anomalies. In this paper, we propose such techniques for the detection of anomalous accesses to relational databases. Our approach is to monitor users' queries, sequences of queries and sessions of database connection to detect queries that retrieve amounts of data larger than the normal. Our evaluation of the proposed techniques indicates that they are very effective in the detection of anomalies.

In context-aware applications, user's access privileges rely on both user's identity and context. Access control rules are usually statically defined while contexts and the system state can change dynamically. Changes in contexts can result in service disruptions. To address this issue, this poster proposes a reactive access control system that associates contingency plans with access control rules. Risk scores are also associated with actions part of the contingency plans. Such risks are estimated by using fuzzy inference. Our approach is cast into the XACML reference architecture.

Despite decades of research on the Internet security, we constantly hear about mega data breaches and malware infections affecting hundreds of millions of hosts. The key reason is that the current threat model of the Internet relies on two assumptions that no longer hold true: (1) Web servers, hosting the content, are secure, (2) each Internet connection starts from the original content provider and terminates at the content consumer. Internet security is today merely patched on top of the TCP/IP protocol stack. In order to achieve comprehensive security for the Internet, we believe that a clean-slate approach must be adopted where a content based security model is employed. Named Data Networking (NDN) is a step in this direction which is envisioned to be the next generation Internet architecture based on a content centric communication model. NDN is currently being designed with security as a key requirement, and thus to support content integrity, authenticity, confidentiality and privacy. However, in order to meet such a requirement, one needs to overcome several challenges, especially in either large operational environments or resource constrained networks. In this paper, we explore the security challenges in achieving comprehensive content security in NDN and propose a research agenda to address some of the challenges.

Access control for information has primarily focused on access statically granted to subjects by administrators usually in the context of a specific system. Even if mechanisms are available for access revocation, revocations must still be executed manually by an administrator. However, as physical devices become increasingly embedded and interconnected, access control needs to become an integral part of the resource being protected and be generated dynamically by resources depending on the context in which the resource is being used. In this paper, we discuss a set of scenarios for access control needed in current and future systems and use that to argue that an approach for resources to generate and manage their access control policies dynamically on their own is needed. We discuss some approaches for generating such access control policies that may address the requirements of the scenarios.

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.

At present, Bluetooth Low Energy (BLE) is dominantly used in commercially available Internet of Things (IoT) devices – such as smart watches, fitness trackers, and smart appliances. Compared to classic Bluetooth, BLE has been simplified in many ways that include its connection establishment, data exchange, and encryption processes. Unfortunately, this simplification comes at a cost. For example, only a star topology is supported in BLE environments and a peripheral (an IoT device) can communicate with only one gateway (e.g. a smartphone, or a BLE hub) at a set time. When a peripheral goes out of range, it loses connectivity to a gateway, and cannot connect and seamlessly communicate with another gateway without user interventions. In other words, BLE connections do not get automatically migrated or handed-off to another gateway. In this paper, we propose a system which brings seamless connectivity to BLE-capable mobile IoT devices in an environment that consists of a network of gateways. Our framework ensures that unmodified, commercial off-the-shelf BLE devices seamlessly and securely connect to a nearby gateway without any user intervention.

Many enterprises are transitioning towards data-driven business processes. There are numerous situations where multiple parties would like to share data towards a common goal if it were possible to simultaneously protect the privacy and security of the individuals and organizations described in the data. Existing solutions for multi-party analytics that follow the so called Data Lake paradigm have parties transfer their raw data to a trusted third-party (i.e., mediator), which then performs the desired analysis on the global data, and shares the results with the parties. However, such a solution does not fit many applications such as Healthcare, Finance, and the Internet-of-Things, where privacy is a strong concern. Motivated by the increasing demands for data privacy, we study the problem of privacy-preserving multi-party data analytics, where the goal is to enable analytics on multi-party data without compromising the data privacy of each individual party. In this paper, we first propose a secure sum protocol with strong security guarantees. The proposed secure sum protocol is resistant to collusion attacks even with N-2 parties colluding, where N denotes the total number of collaborating parties. We then use this protocol to propose two secure gradient descent algorithms, one for horizontally partitioned data, and the other for vertically partitioned data. The proposed framework is generic and applies to a wide class of machine learning problems. We demonstrate our solution for two popular use-cases, regression and classification, and evaluate the performance of the proposed solution in terms of the obtained model accuracy, latency and communication cost. In addition, we perform a scalability analysis to evaluate the performance of the proposed solution as the data size and the number of parties increase.

In this paper, we present an architecture and implementation of a secure, automated, proximity-based access control that we refer to as Context-Aware System to Secure Enterprise Content (CASSEC). Using the pervasive WiFi and Bluetooth wireless devices as components in our underlying positioning infrastructure, CASSEC addresses two proximity-based scenarios often encountered in enterprise environments: Separation of Duty and Absence of Other Users. The first scenario is achieved by using Bluetooth MAC addresses of nearby occupants as authentication tokens. The second scenario exploits the interference of WiFi received signal strength when an occupant crosses the line of sight (LOS). Regardless of the scenario, information about the occupancy of a particular location is periodically extracted to support continuous authentication. To the best of our knowledge, our approach is the first to incorporate WiFi signal interference caused by occupants as part of proximity-based access control system. Our results demonstrate that it is feasible to achieve great accuracy in localization of occupants in a monitored room.

There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.

Several solutions have recently been proposed to securely estimate sensor positions even when there is malicious location information which distorts the estimate. Some of those solutions are based on the Minimum Mean Square Estimation (MMSE) methods which efficiently estimate sensor positions. Although such solutions can filter out most of malicious information, if an attacker knows the position of a target sensor, the attacker can significantly alter the position information. In this paper, we introduce such a new attack, called Inside-Attack, and a technique that is able to detect and filter out malicious location information. Based on this technique, we propose an algorithm to effectively estimate sensor positions. We illustrate the impact of inside attacks on the existing algorithms and report simulation results concerning our algorithm.

The Internet of Things (IoT) is the latest Internet evolution that incorporates a diverse range of things such as sensors, actuators, and services deployed by different organizations and individuals to support a variety of applications. The information captured by IoT present an unprecedented opportunity to solve large-scale problems in those application domains to deliver services; example applications include precision agriculture, environment monitoring, smart health, smart manufacturing, and smart cities. Like all other Internet based services in the past, IoT-based services are also being developed and deployed without security consideration. By nature, IoT devices and services are vulnerable to malicious cyber threats as they cannot be given the same protection that is received by enterprise services within an enterprise perimeter. While IoT services will play an important role in our daily life resulting in improved productivity and quality of life, the trend has also “encouraged” cyber-exploitation and evolution and diversification of malicious cyber threats. Hence, there is a need for coordinated efforts from the research community to address resulting concerns, such as those presented in this special section. Several potential research topics are also identified in this special section.