Biblio
We present ctrlTCP, a method to combine the congestion controls of multiple TCP connections. In contrast to the previous methods such as the Congestion Manager, ctrlTCP can couple all TCP flows that leave one sender, traverse a common bottleneck (e.g., a home user's thin uplink) and arrive at different destinations. Using ns-2 simulations and an implementation in the FreeBSD kernel, we show that our mechanism reduces queuing delay, packet loss, and short flow completion times while enabling precise allocation of the share of the available bandwidth between the connections according to the needs of the applications.
Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces use of a sieve-valve as a security primitive to obfuscate bioassay implementations; (2) it shows how sieve-valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) it presents design rules and security metrics to design and measure obfuscated biochips. We assess the cost-security trade-offs associated with this solution and demonstrate practical sieve-valve based obfuscation on real-life biochips.
Due to practical constraints in preventing phishing through public network or insecure communication channels, simple physical unclonable function (PDF)-based authentication protocol with unrestricted queries and transparent responses is vulnerable to modeling and replay attacks. In this paper, we present a PUF-based authentication method to mitigate the practical limitations in applications where a resource-rich server authenticates a device with no strong restriction imposed on the type of PUF designs or any additional protection on the binary channel used for the authentication. Our scheme uses an active deception protocol to prevent machine learning (ML) attacks on a device. The monolithic system makes collection of challenge response pairs (CRPs) easy for model building during enrollment but prohibitively time consuming upon device deployment. A genuine server can perform a mutual authentication with the device at any time with a combined fresh challenge contributed by both the server and the device. The message exchanged in clear does not expose the authentic CRPs. The false PUF multiplexing is fortified against prediction of waiting time by doubling the time penalty for every unsuccessful authentication.
Moving target defense (MTD) is a proactive defense mechanism of changing the attack surface to increase an attacker's confusion and/or uncertainty, which invalidates its intelligence gained through reconnaissance and/or network scanning attacks. In this work, we propose software-defined networking (SDN)-based MTD technique using the shuffling of IP addresses and port numbers aiming to obfuscate both network and transport layers' real identities of the host and the service for defending against the network reconnaissance and scanning attacks. We call our proposed MTD technique Random Host and Service Multiplexing, namely RHSM. RHSM allows each host to use random, multiple virtual IP addresses to be dynamically and periodically shuffled. In addition, it uses short-lived, multiple virtual port numbers for an active service running on the host. Our proposed RHSM is novel in that we employ multiplexing (or de-multiplexing) to dynamically change and remap from all the virtual IPs of the host to the real IP or the virtual ports of the services to the real port, respectively. Via extensive simulation experiments, we prove how effectively and efficiently RHSM outperforms a baseline counterpart (i.e., a static network without RHSM) in terms of the attack success probability and defense cost.
With the interconnection of services and customers, network attacks are capable of large amounts of damage. Flexible Random Virtual IP Multiplexing (FRVM) is a Moving Target Defence (MTD) technique that protects against reconnaissance and access with address mutation and multiplexing. Security techniques must be trusted, however, FRVM, along with past MTD techniques, have gaps in realistic evaluation and thorough analysis of security and performance. FRVM, and two comparison techniques, were deployed on a virtualised network to demonstrate FRVM's security and performance trade-offs. The key results include the security and performance trade-offs of address multiplexing and address mutation. The security benefit of IP address multiplexing is much greater than its performance overhead, deployed on top of address mutation. Frequent address mutation significantly increases an attackers' network scan durations as well as effectively obfuscating and hiding network configurations.
Conventional SDN-based MTD techniques have been mainly developed with a single SDN controller which exposes a single point of failure as well as raises a scalability issue for large-scale networks in achieving both security and performance. The use of multiple SDN controllers has been proposed to ensure both performance and security of SDN-based MTD systems for large-scale networks; however, the effect of using multiple SDN controllers has not been investigated in the state-of-the-art research. In this paper, we propose the SDN based MTD architecture using multiple SDN controllers and validate their security effect (i.e., attack success probability) by implementing an IP shuffling MTD in a testbed using ONOS SDN controllers.
The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.
Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.
The accessibility of on-chip embedded infrastructure for test, reconfiguration, or debug poses a serious security problem. Access mechanisms based on IEEE Std 1149.1 (JTAG), and especially reconfigurable scan networks (RSNs), as allowed by IEEE Std 1500, IEEE Std 1149.1-2013, and IEEE Std 1687 (IJTAG), require special care in the design and development. This work studies the threats to trustworthy data transmission in RSNs posed by untrusted components within the RSN and external interfaces. We propose a novel scan pattern generation method that finds trustworthy access sequences to prevent sniffing and spoofing of transmitted data in the RSN. For insecure RSNs, for which such accesses do not exist, we present an automated transformation that improves the security and trustworthiness while preserving the accessibility to attached instruments. The area overhead is reduced based on results from trustworthy access pattern generation. As a result, sensitive data is not exposed to untrusted components in the RSN, and compromised data cannot be injected during trustworthy accesses.
Joint transmission coordinated multi-point (CoMP) is a combination of constructive and destructive superposition of several to potentially many signal components, with the goal to maximize the desired receive-signal and at the same time to minimize mutual interference. Especially the destructive superposition requires accurate alignment of phases and amplitudes. Therefore, a 5G clean slate approach needs to incorporate the following enablers to overcome the challenging limitation for JT CoMP: accurate channel estimation of all relevant channel components, channel prediction for time-aligned precoder design, proper setup of cooperation areas corresponding to user grouping and to limit feedback overhead especially in FDD as well as treatment of out-of-cluster interference (interference floor shaping).
This paper proposes an efficient diagnosis-aware ATPG method that can quickly identify equivalent-fault pairs and generate diagnosis patterns for nonequivalent-fault pairs, where an (non)equivalent-fault pair contains two stuck-at faults that are (not) equivalent. A novel fault injection method is developed which allows one to embed all fault pairs undistinguished by the conventional test patterns into a circuit model with only one copy of the original circuit. Each pair of faults to be processed is transformed to a stuck-at fault and all fault pairs can be dealt with by invoking an ordinary ATPG tool for stuck-at faults just once. High efficiency of diagnosis pattern generation can be achieved due to 1) the circuit to be processed is read only once, 2) the data structure for ATPG process is constructed only once, 3) multiple fault pairs can be processed at a time, and 4) only one copy of the original circuit is needed. Experimental results show that this is the first reported work that can achieve 100% diagnosis resolutions for all ISCAS'89 and IWLS'05 benchmark circuits using an ordinary ATPG tool. Furthermore, we also find that the total number of patterns required to deal with all fault pairs in our method is smaller than that of the current state-of-the-art work.