Biblio

Software-Defined Network (SDN) is a novel architecture created to address the issues of traditional and vertically integrated networks. To increase cost-effectiveness and enable logical control, SDN provides high programmability and centralized view of the network through separation of network traffic delivery (the "data plane") from network configuration (the "control plane"). SDN controllers and related protocols are rapidly evolving to address the demands for scaling in complex enterprise networks. Because of the evolution of modern SDN technologies, production networks employing SDN are prone to several security vulnerabilities. The rate at which SDN frameworks are evolving continues to overtake attempts to address their security issues. According to our study, existing defense mechanisms, particularly SDN-based firewalls, face new and SDN-specific challenges in successfully enforcing security policies in the underlying network. In this paper, we identify problems associated with SDN-based firewalls, such as ambiguous flow path calculations and poor scalability in large networks. We survey existing SDN-based firewall designs and their shortcomings in protecting a dynamically scaling network like a data center. We extend our study by evaluating one such SDN-specific security solution called FlowGuard, and identifying new attack vectors and vulnerabilities. We also present corresponding threat detection techniques and respective mitigation strategies.

Mobile and Internet-of-Things (IoT) devices, such as smartphones, tablets, wearables, smart home assistants (e.g., Google Home and Amazon Echo), and wall-mounted cameras, come equipped with various sensors, notably camera and microphone. These sensors can capture extremely sensitive and private information. There are several important scenarios where, for privacy reasons, a user might require assurance about the use (or non-use) of these sensors. For example, the owner of a home assistant might require assurance that the microphone on the device is not used during a given time of the day. Similarly, during a confidential meeting, the host needs assurance that attendees do not record any audio or video. Currently, there are no means to attain such assurance in modern mobile and IoT devices. To this end, this paper presents Ditio, a system approach for auditing sensor activities. Ditio records sensor activity logs that can be later inspected by an auditor and checked for compliance with a given policy. It is based on a hybrid security monitor architecture that leverages both ARM's virtualization hardware and TrustZone. Ditio includes an authentication protocol for establishing a logging session with a trusted server and a formally verified companion tool for log analysis. Ditio prototypes on ARM Juno development board and Nexus 5 smartphone show that it introduces negligible performance overhead for both the camera and microphone. However, it incurs up to 17% additional power consumption under heavy use for the Nexus 5 camera.

Vehicular ad hoc network is based on MANET all the vehicle to vehicle and vehicle roadside are connected to the wireless sensor network. In this paper mainly discuss on the security in the VANET in the lightweight cloud environment. Moving vehicle on the roadside connected through the sensor nodes and to provide communication between the vehicles and directly connected to the centralized environment. We propose a new approach to share the information in the VANET networks in secure manner through cloud.

For a secure communication over an insecure channel the Diffie-Hellman key exchange protocol (DHKEP) is treated as the de facto standard. However, it suffers form server-side compromisation, identity compromisation, man-in-the-middle, replay attacks, etc. Also, there are single point of vulnerability (SOV), single point of failure (SOF) and user privacy preservation issues. This work proposes an identity-based two-server DHKEP to address the aforesaid issues and alleviating the attacks. To preserve user identity from outside intruders, a k-anonymity based identity hiding principle has been adopted. Further, to ensure efficient utilization of channel bandwidth, the proposed scheme employs elliptic curve cryptography. The security analysis substantiate that our scheme is provably secure and successfully addressed the above-mentioned issues. The performance study contemplates that the overhead of the protocol is reasonable and comparable with other schemes.

Wearable devices for fitness tracking and health monitoring have gained considerable popularity and become one of the fastest growing smart devices market. More and more companies are offering integrated health and activity monitoring solutions for fitness trackers. Recently insurances are offering their customers better conditions for health and condition monitoring. However, the extensive sensitive information collected by tracking products and accessibility by third party service providers poses vital security and privacy challenges on the employed solutions. In this paper, we present our security analysis of a representative sample of current fitness tracking products on the market. In particular, we focus on malicious user setting that aims at injecting false data into the cloud-based services leading to erroneous data analytics. We show that none of these products can provide data integrity, authenticity and confidentiality.

The proliferation of Internet-of-Things (IoT) devices within homes raises many security and privacy concerns. Recent headlines highlight the lack of effective security mechanisms in IoT devices. Security threats in IoT arise not only from vulnerabilities in individual devices but also from the composition of devices in unanticipated ways and the ability of devices to interact through both cyber and physical channels. Existing approaches provide methods for monitoring cyber interactions between devices but fail to consider possible physical interactions. To overcome this challenge, it is essential that security assessments of IoT networks take a holistic view of the network and treat it as a "system of systems", in which security is defined, not solely by the individual systems, but also by the interactions and trust dependencies between systems. In this paper, we propose a way of modeling cyber and physical interactions between IoT devices of a given network. By verifying the cyber and physical interactions against user-defined policies, our model can identify unexpected chains of events that may be harmful. It can also be applied to determine the impact of the addition (or removal) of a device into an existing network with respect to dangerous device interactions. We demonstrate the viability of our approach by instantiating our model using Alloy, a language and tool for relational models. In our evaluation, we considered three realistic IoT use cases and demonstrate that our model is capable of identifying potentially dangerous device interactions. We also measure the performance of our approach with respect to the CPU runtime and memory consumption of the Alloy model finder, and show that it is acceptable for smart-home IoT networks.

Modern browsers implement different security policies such as the Content Security Policy (CSP), a mechanism designed to mitigate popular web vulnerabilities, and the Same Origin Policy (SOP), a mechanism that governs interactions between resources of web pages. In this work, we describe how CSP may be violated due to the SOP when a page contains an embedded iframe from the same origin. We analyse 1 million pages from 10,000 top Alexa sites and report that at least 31.1% of current CSP-enabled pages are potentially vulnerable to CSP violations. Further considering real-world situations where those pages are involved in same-origin nested browsing contexts, we found that in at least 23.5% of the cases, CSP violations are possible. During our study, we also identified a divergence among browsers implementations in the enforcement of CSP in srcdoc sandboxed iframes, which actually reveals a problem in Gecko-based browsers CSP implementation. To ameliorate the problematic conflicts of the security mechanisms, we discuss measures to avoid CSP violations.

We study a sensor network setting in which samples are encrypted individually using different keys and maintained on a cloud storage. For large systems, e.g. those that generate several millions of samples per day, fine-grained sharing of encrypted samples is challenging. Existing solutions, such as Attribute-Based Encryption (ABE) and Key Aggregation Cryptosystem (KAC), can be utilized to address the challenge, but only to a certain extent. They are often computationally expensive and thus unlikely to operate at scale. We propose an algorithmic enhancement and two heuristics to improve KAC's key reconstruction cost, while preserving its provable security. The improvement is particularly significant for range and down-sampling queries – accelerating the reconstruction cost from quadratic to linear running time. Experimental study shows that for queries of size 32k samples, the proposed fast reconstruction techniques speed-up the original KAC by at least 90 times on range and down-sampling queries, and by eight times on general (arbitrary) queries. It also shows that at the expense of splitting the query into 16 sub-queries and correspondingly issuing that number of different aggregated keys, reconstruction time can be reduced by 19 times. As such, the proposed techniques make KAC more applicable in practical scenarios such as sensor networks or the Internet of Things.

We study a sensor network setting in which samples are encrypted individually using different keys and maintained on a cloud storage. For large systems, e.g. those that generate several millions of samples per day, fine-grained sharing of encrypted samples is challenging. Existing solutions, such as Attribute-Based Encryption (ABE) and Key Aggregation Cryptosystem (KAC), can be utilized to address the challenge, but only to a certain extent. They are often computationally expensive and thus unlikely to operate at scale. We propose an algorithmic enhancement and two heuristics to improve KAC's key reconstruction cost, while preserving its provable security. The improvement is particularly significant for range and down-sampling queries – accelerating the reconstruction cost from quadratic to linear running time. Experimental study shows that for queries of size 32k samples, the proposed fast reconstruction techniques speed-up the original KAC by at least 90 times on range and down-sampling queries, and by eight times on general (arbitrary) queries. It also shows that at the expense of splitting the query into 16 sub-queries and correspondingly issuing that number of different aggregated keys, reconstruction time can be reduced by 19 times. As such, the proposed techniques make KAC more applicable in practical scenarios such as sensor networks or the Internet of Things.

The safety of information inside of cloud networks is of interest to the network administrators. In a new insider attack, inside attackers merge confidential information with videos using digital video steganography. The video can be uploaded to video websites, where the information can be distributed online, where it can cost firms millions in damages. Standard behavior based exfiltration detection does not always prevent these attacks. This form of steganography is almost invisible. Existing compressed video steganalysis only detects small-payload watermarks. We develop such a strategy using distributed algorithms and classify videos, then compare existing algorithms to new ones. We find our approach improves on behavior based exfiltration detection, and on the existing online video steganalysis.

While the rapid progress in smart city technologies are changing cities and the lifestyle of the people, there are increasingly enormous challenges in terms of the safety and security of smart cities. The potential vulnerabilities of e-government products and imminent attacks on smart city infrastructure and services will have catastrophic consequences on the governments and can cause substantial economic and noneconomic losses, even chaos, to the cities and their residents. This paper aims to explore alternative economic solutions ranging from incentive mechanisms to market-based solutions to motivate smart city product vendors, governments, and vulnerability researchers and finders to improve the cybersecurity of smart cities.

We study a sensor network setting in which samples are encrypted individually using different keys and maintained on a cloud storage. For large systems, e.g. those that generate several millions of samples per day, fine-grained sharing of encrypted samples is challenging. Existing solutions, such as Attribute-Based Encryption (ABE) and Key Aggregation Cryptosystem (KAC), can be utilized to address the challenge, but only to a certain extent. They are often computationally expensive and thus unlikely to operate at scale. We propose an algorithmic enhancement and two heuristics to improve KAC's key reconstruction cost, while preserving its provable security. The improvement is particularly significant for range and down-sampling queries – accelerating the reconstruction cost from quadratic to linear running time. Experimental study shows that for queries of size 32k samples, the proposed fast reconstruction techniques speed-up the original KAC by at least 90 times on range and down-sampling queries, and by eight times on general (arbitrary) queries. It also shows that at the expense of splitting the query into 16 sub-queries and correspondingly issuing that number of different aggregated keys, reconstruction time can be reduced by 19 times. As such, the proposed techniques make KAC more applicable in practical scenarios such as sensor networks or the Internet of Things.

Cameras have become nearly ubiquitous with the rise of smartphones and laptops. New wearable devices, such as Google Glass, focus directly on using live video data to enable augmented reality and contextually enabled services. However, granting applications full access to video data exposes more information than is necessary for their functionality, introducing privacy risks. We propose a privilege-separation architecture for visual recognizer applications that encourages modularization and least privilege–-separating the recognizer logic, sandboxing it to restrict filesystem and network access, and restricting what it can extract from the raw video data. We designed and implemented a prototype that separates the recognizer and application modules and evaluated our architecture on a set of 17 computer-vision applications. Our experiments show that our prototype incurs low overhead for each of these applications, reduces some of the privacy risks associated with these applications, and in some cases can actually increase the performance due to increased parallelism and concurrency.

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.

The Internet of Things (IoT) offers new opportunities, but alongside those come many challenges for security and privacy. Most IoT devices offer no choice to users of where data is published, which data is made available and what identities are used for both devices and users. The aim of this work is to explore new middleware models and techniques that can provide users with more choice as well as enhance privacy and security. This paper outlines a new model and a prototype of a middleware system that implements this model.

Mobile radio frequency identification (RFID) systems are being employed in many applications such as supply chain management. Since the communications between RFID-reader and server, RFID-tag and RFID-reader are all wireless, security and privacy attracts more attentions, reflected in the research on authentication protocols. But most of the existing authentications only care about the front end (reader to tag) and ignore the back end (reader to server), which could not satisfy the security demands in the mobile RFID systems. Moreover, the tags have to be grouped when the population is large enough, but the existing authentication protocols are inapplicable in this scenario. In this paper, we propose a mixed authentication protocol composed of hash-based authentication for readers and lightweight authentication for low-cost tags to fit the mobile RFID system with grouping tags. Analysis demonstrates that the proposed authentication protocol could efficiently counteract the impersonation attack, reply attack and tracking attack.