Biblio
To combat blind in-window attacks against TCP, changes proposed in RFC 5961 have been implemented by Linux since late 2012. While successfully eliminating the old vulnerabilities, the new TCP implementation was reported in August 2016 to have introduced a subtle yet serious security flaw. Assigned CVE-2016-5696, the flaw exploits the challenge ACK rate limiting feature that could allow an off-path attacker to infer the presence/absence of a TCP connection between two arbitrary hosts, terminate such a connection, and even inject malicious payload. In this work, we perform a comprehensive measurement of the impact of the new vulnerability. This includes (1) tracking the vulnerable Internet servers, (2) monitoring the patch behavior over time, (3) picturing the overall security status of TCP stacks at scale. Towards this goal, we design a scalable measurement methodology to scan the Alexa top 1 million websites for almost 6 months. We also present how notifications impact the patching behavior, and compare the result with the Heartbleed and the Debian PRNG vulnerability. The measurement represents a valuable data point in understanding how Internet servers react to serious security flaws in the operating system kernel.
A practically feasible low-overhead hardware design that provides strong defenses against memory bus side channel remains elusive. This paper observes that smart memory, memory with compute capability and a packetized interface, can dramatically simplify this problem. InvisiMem expands the trust base to include the logic layer in the smart memory to implement cryptographic primitives, which aid in addressing several memory bus side channel vulnerabilities efficiently. This allows the secure host processor to send encrypted addresses over the untrusted memory bus, and thereby eliminates the need for expensive address obfuscation techniques based on Oblivious RAM (ORAM). In addition, smart memory enables efficient solutions for ensuring freshness without using expensive Merkle trees, and mitigates memory bus timing channel using constant heart-beat packets. We demonstrate that InvisiMem designs have one to two orders of magnitude of lower overheads for performance, space, energy, and memory bandwidth, compared to prior solutions.
Internet of things (IoT) is internetworking of various physical devices to provide a range of services and applications. IoT is a rapidly growing field, on an account of this; the security measurements for IoT should be at first concern. In the modern day world, the most emerging cyber-attack threat for IoT is ransomware attack. Ransomware is a kind of malware with the aim of rendering a victim's computer unusable or inaccessible, and then asking the user to pay a ransom to revert the destruction. In this paper we are evaluating ransomware attacks statistics for the past 2 years and the present year to estimate growth rate of the most emerging ransomware families from the last 3 years to evaluate most threatening ransomware attacks for IoT. Growth rate results shows that the number of attacks for Cryptowall and locky ransomware are notably increasing therefore, these ransomware families are potential threat to IoT. Moreover, we present a Cryptowall ransomware attack detection model based on the communication and behavioral study of Cryptowall for IoT environment. The proposed model observes incoming TCP/IP traffic through web proxy server then extracts TCP/IP header and uses command and control (C&C) server black listing to detect ransomware attacks.
Blockchain, the underlying technology of cryptocurrency networks like Bitcoin, can prove to be essential towards realizing the vision of a decentralized, secure, and open Internet of Things (IoT) revolution. There is a growing interest in many research groups towards leveraging blockchains to provide IoT data privacy without the need for a centralized data access model. This paper aims to propose a decentralized access model for IoT data, using a network architecture that we call a modular consortium architecture for IoT and blockchains. The proposed architecture facilitates IoT communications on top of a software stack of blockchains and peer-to-peer data storage mechanisms. The architecture is aimed to have privacy built into it, and to be adaptable for various IoT use cases. To understand the feasibility and deployment considerations for implementing the proposed architecture, we conduct performance analysis of existing blockchain development platforms, Ethereum and Monax.
Internet of Things devices (IoT-D) have limited resource capacity. But these devices can share resources. Hence, they are being used in variety of applications in various fields including smart city, smart energy, healthcare etc. Traditional practice of medicine and healthcare is mostly heuristic driven. There exist big gaps in our understanding of human body, disease and health. We can use upcoming digital revolution to turn healthcare upside down with data-driven medical science. Various healthcare companies now provide remote healthcare services. Healthcare professionals are also adapting remote healthcare monitoring practices so as to monitor patients who are either hospitalized or executing their normal lifestyle activities at remote locations. Wearable devices available in the market calculate different health parameters and corresponding applications pass the information to server through their proprietary platforms. However, these devices or applications cannot directly communicate or share the data. So, there needs an API to access health and wellness data from different wearable medical devices and applications. This paper proposes and demonstrates an API to connect different wearable healthcare devices and transfer patient personal information securely to the doctor or health provider.
Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65-nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.
As we know, we are already facing IoT threat and under IoT attacks. However, there are only a few discussions on, how to analyze this kind of cyber threat and malwares. In this paper, we propose IoT sandbox which can support different type of CPU architecture. It can be used to analyze IoT malwares, collect network packets, identify spread method and record malwares behaviors. To make sure our IoT sandbox can be functional, we implement it and use the Zollard botnet for experiment. According to our experimental data, we found that at least 71,148 IP have been compromised. Some of them are IoT devices (DVR, Web Camera, Router WiFi Disk, Set-top box) and others are ICS devices (Heat pump and ICS data acquisition server). Based on our IoT sandbox technology, we can discover an IoT malware in an early stage. This could help IT manager or security experts to analysis and determine IDS rules. We hope this research can prevent IoT threat and enhance IoT Security in the near future.
In the context of the emerging Internet of Things (IoT), a proliferation of wireless connectivity can be expected. That ubiquitous wireless communication will be hard to centrally manage and control, and can be expected to be opaque to end users. As a result, owners and users of physical space are threatened to lose control over their digital environments. In this work, we propose the idea of an IoTScanner. The IoTScanner integrates a range of radios to allow local reconnaissance of existing wireless infrastructure and participating nodes. It enumerates such devices, identifies connection patterns, and provides valuable insights for technical support and home users alike. Using our IoTScanner, we investigate metrics that could be used to classify devices and identify privacy threats in an IoT neighborhood.
Wireless Sensor Networks (WSN) are widely used to monitor and control physical environments. An efficient energy management system is needed to be able to deploy these networks in lossy environments while maintaining reliable communication. The IPv6 Routing Protocol for Low-Power and Lossy networks is a routing protocol designed to properly manage energy without compromising reliability. This protocol has currently been implemented in Contiki OS, TinyOS, and OMNeT++ Castalia. But these applications also simulate all operation mechanics of a specified hardware model instead of just simulating the protocol only, thus adding unnecessary overhead and slowing down simulations on RPL. In light of this, we have implemented a working ns-3 implementation of RPL with support for multiple RPL instances with the use of a global repair mechanism. The behavior and output of our simulator was compared to Cooja for verification, and the results are similar with a minor difference in rank computation.
Information security can benefit from real-time cyber threat indicator sharing, in which companies and government agencies share their knowledge of emerging cyberattacks to benefit their sector and society at large. As attacks become increasingly sophisticated by exploiting behavioral dimensions of human computer operators, there is an increased risk to systems that store personal information. In addition, risk increases as individuals blur the boundaries between workplace and home computing (e.g., using workplace computers for personal reasons). This paper describes an architecture to leverage individual perceptions of privacy risk to compute privacy risk scores over cyber threat indicator data. Unlike security risk, which is a risk to a particular system, privacy risk concerns an individual's personal information being accessed and exploited. The architecture integrates tools to extract information entities from textual threat reports expressed in the STIX format and privacy risk estimates computed using factorial vignettes to survey individual risk perceptions. The architecture aims to optimize for scalability and adaptability to achieve real-time risk scoring.
We present in this paper a security analysis of electronic devices which considers the lifecycle properties of embedded systems. We first define a generic model of electronic devices lifecycle showing the complex interactions between the numerous assets and the actors. The method is illustrated through a case study: a connected insulin pump. The lifecycle induced vulnerabilities are analyzed using the EBIOS methodology. An analysis of associated countermeasures points out the lack of consideration of the life cycle in order to provide an acceptable security level of each assets of the device.
Analytics in big data is maturing and moving towards mass adoption. The emergence of analytics increases the need for innovative tools and methodologies to protect data against privacy violation. Many data anonymization methods were proposed to provide some degree of privacy protection by applying data suppression and other distortion techniques. However, currently available methods suffer from poor scalability, performance and lack of framework standardization. Current anonymization methods are unable to cope with the massive size of data processing. Some of these methods were especially proposed for MapReduce framework to operate in Big Data. However, they still operate in conventional data management approaches. Therefore, there were no remarkable gains in the performance. We introduce a framework that can operate in MapReduce environment to benefit from its advantages, as well as from those in Hadoop ecosystems. Our framework provides a granular user's access that can be tuned to different authorization levels. The proposed solution provides a fine-grained alteration based on the user's authorization level to access MapReduce domain for analytics. Using well-developed role-based access control approaches, this framework is capable of assigning roles to users and map them to relevant data attributes.
Currently, when companies conduct risk analysis of own networks and systems, it is common to outsource risk analysis to third-party experts. At that time, the company passes the information used for risk analysis including confidential information such as network configuration to third-party expert. It raises the risk of leakage and abuse of confidential information. Therefore, a method of risk analysis by using secure computation without passing confidential information of company has been proposed. Although Liu's method have firstly achieved secure risk analysis method using multiparty computation and attack tree analysis, it has several problems to be practical. In this paper, improvement of secure risk analysis method is proposed. It can dynamically reduce compilation time, enhance scale of target network and system without increasing execution time. Experimental work is carried out by prototype implementation. As a result, we achieved improved performance in compile time and enhance scale of target with equivalent performance on execution time.
While most organizations continue to invest in traditional network defences, a formidable security challenge has been brewing within their own boundaries. Malicious insiders with privileged access in the guise of a trusted source have carried out many attacks causing far reaching damage to financial stability, national security and brand reputation for both public and private sector organizations. Growing exposure and impact of the whistleblower community and concerns about job security with changing organizational dynamics has further aggravated this situation. The unpredictability of malicious attackers, as well as the complexity of malicious actions, necessitates the careful analysis of network, system and user parameters correlated with insider threat problem. Thus it creates a high dimensional, heterogeneous data analysis problem in isolating suspicious users. This research work proposes an insider threat detection framework, which utilizes the attributed graph clustering techniques and outlier ranking mechanism for enterprise users. Empirical results also confirm the effectiveness of the method by achieving the best area under curve value of 0.7648 for the receiver operating characteristic curve.
The Sensor Web is evolving into a complex information space, where large volumes of sensor observation data are often consumed by complex applications. Provenance has become an important issue in the Sensor Web, since it allows applications to answer “what”, “when”, “where”, “who”, “why”, and “how” queries related to observations and consumption processes, which helps determine the usability and reliability of data products. This paper investigates characteristics and requirements of provenance in the Sensor Web and proposes an interoperable approach to building a provenance model for the Sensor Web. Our provenance model extends the W3C PROV Data Model with Sensor Web domain vocabularies. It is developed using Semantic Web technologies and thus allows provenance information of sensor observations to be exposed in the Web of Data using the Linked Data approach. A use case illustrates the applicability of the approach.
The use of self organized wireless technologies called as Mobile Ad Hoc Networks (MANETs) has increased and these wireless devices can be deployed anywhere without any infrastructural support or without any base station, hence securing these networks and preventing from Intrusions is necessary. This paper describes a method for securing the MANETs using Hybrid cryptographic technique which uses RSA and AES algorithm along with SHA 256 Hashing technique. This hybrid cryptographic technique provides authentication to the data. To check whether there is any malicious node present, an Intrusion Detection system (IDS) technique called Enhanced Adaptive Acknowledgement (EAACK) is used, which checks for the acknowledgement packets to detect any malicious node present in the system. The routing of packets is done through two protocols AODV and ZRP and both the results are compared. The ZRP protocol when used for routing provides better performance as compared to AODV.
Access control is one of the most challenging issues in Cloud environment, it must ensure data confidentiality through enforced and flexible access policies. The revocation is an important task of the access control process, generally it consists on banishing some roles from the users. Attribute-based encryption is a promising cryptographic method which provides the fine-grained access, which makes it very useful in case of group sharing applications. This solution has initially been developed on a central authority model. Later, it has been extended to a multi-authority model which is more convenient and more reliable. However, the revocation problem is still the major challenge of this approach. There have been few proposed revocation solutions for the Multi-authority scheme and these solutions suffer from the lack of efficiency. In this paper, we propose an access control mechanism on a multi-authority architecture with an immediate and efficient attributes' or users' revocation. The proposed scheme uses decentralized CP-ABE to provide flexible and fine-grained access. Our solution provides collusion resistance, prevents security degradations, supports scalability and does not require keys' redistribution.
Collaborative Filtering (CF) is a successful technique that has been implemented in recommender systems and Privacy Preserving Collaborative Filtering (PPCF) aroused increasing concerns of the society. Current solutions mainly focus on cryptographic methods, obfuscation methods, perturbation methods and differential privacy methods. But these methods have some shortcomings, such as unnecessary computational cost, lower data quality and hard to calibrate the magnitude of noise. This paper proposes a (k, p, I)-anonymity method that improves the existing k-anonymity method in PPCF. The method works as follows: First, it applies Latent Factor Model (LFM) to reduce matrix sparsity. Then it improves Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase homogeneity among records in each group which can retain better data quality and (p, I)-diversity model where p is attacker's prior knowledge about users' ratings and I is the diversity among users in each group to improve the level of privacy preserving. Theoretical and experimental analyses show that our approach ensures a higher level of privacy preserving based on lower information loss.
To appear. Available at \url{https://goo.gl/b3nnqG}
Distribution system security region (DSSR) has been widely used to analyze the distribution system operation security. This paper innovatively defines the scale of DSSR, namely the number of boundary constraints and variables of all operational constraints, analyzes and puts forward the corresponding evaluation method. Firstly, the influence of the number of security boundary constraints and variables on the scale of DSSR is analyzed. The factors that mainly influence the scale are found, such as the number of transformers, feeders, as well as sectionalizing switches, and feeder contacts modes between transformers. Secondly, a matrix representing the relations among transformers in distribution system is defined to reflect the characteristics of network's structure, while an algorithm of the scale of DSSR based on transformers connection relationship matrix is proposed, which avoids the trouble of listing security region constraints. Finally, the proposed method is applied in a test system to confirm the effectiveness of the concepts and methods. It provides the necessary foundation for DSSR theory as well as safety analysis.