National Institute of Standards and Technology における超伝導研究及び生活

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

A low-rate distributed denial of service (DDoS) attack has significant ability of concealing its traffic because it is very much like normal traffic. It has the capacity to elude the current anomaly-based detection schemes. An information metric can quantify the differences of network traffic with various probability distributions. In this paper, we innovatively propose using two new information metrics such as the generalized entropy metric and the information distance metric to detect low-rate DDoS attacks by measuring the difference between legitimate traffic and attack traffic. The proposed generalized entropy metric can detect attacks several hops earlier (three hops earlier while the order α = 10 ) than the traditional Shannon metric. The proposed information distance metric outperforms (six hops earlier while the order α = 10) the popular Kullback-Leibler divergence approach as it can clearly enlarge the adjudication distance and then obtain the optimal detection sensitivity. The experimental results show that the proposed information metrics can effectively detect low-rate DDoS attacks and clearly reduce the false positive rate. Furthermore, the proposed IP traceback algorithm can find all attacks as well as attackers from their own local area networks (LANs) and discard attack traffic.

Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions.

https://ieeexplore.ieee.org/ielaam/5/8789751/8741060-aam.pdf

Edge Computing Security: State of the Art and Challenges

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Service Oriented Architecture Concepts Technology And Design

Radio Frequency Identification (RFID) is a technological revolution that is expected to soon replace barcode systems. One of the important features of an RFID system is its ability to search for a particular tag among a group of tags. This task is quite common where RFID systems play a vital role. To our knowledge not much work has been done in this secure search area of RFID. Also, most of the existing work do not comply with the C1G2 standards. Our work aims to fill that gap by proposing a protocol based on Quadratic Residues property that does not use the expensive hash functions or any complex encryption schemes but achieves total compliance with industry standards while meeting the security requirements.

A secure search protocol based on Quadratic Residues for EPC Class-1 Gen-2 UHF RFID tags

Emotion is a kind of psychological and physical state that people produce to objective things. Accurate recognition of emotions is very important in the field of human-machine interface. It is still challenging to extract discriminative features used for electroencephalogram (EEG) emotion recognition which contain the subtle spatial feature of EEG signal and the temporal representations of EEG signal. In order to overcome these challenges, the paper proposes a model based on the capsule network for multi-channel EEG emotion recognition, which combines the attention mechanism and the LSTM network. First, the channel-wise attention mechanism is used to adaptively assign different weights to each channel, then the CapsNet is used to extract the spatial features of the EEG channel, and LSTM is used to extract temporal features of the EEG sequences. The paper proposed method achieves average accuracy of 97.17%, 97.34% and 96.50% on the valence, arouse and dominance of the DEAP dataset, respectively. These results show that the proposed method is better than the current state-of-the-art method.

EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models

Opportunistic networks (OppNets) are an interesting topic that are seen to have a promising future. Many protocols have been developed to accommodate the features of OppNets such as frequent partitions, long delays, and no end-to-end path between the source and destination nodes. Embedding security into these protocols is challenging and has taken a lot of attention in research. One of the attacks that OppNets are exposed to is the packet dropping attack, where the malicious node attempts to drop some packets and forwards an incomplete number of packets which results in the distortion of the message. To increase the security levels in OppNets, this paper presents an algorithm developed to detect packet dropping attacks, and finds the malicious node that attempted the attack. The algorithm detects the attack by using an indicative field in the header section of each packet; the indicative field has 3 sub fields - the identification field, the flag field, and the offset field. These 3 fields are used to find if a node receives the complete original number of packets from the previous node. The algorithm will have the advantage of detecting packets dropped by each intermediate node, this helps solve the difficulties of finding malicious nodes by the destination node only.

Defense against packet dropping attacks in opportunistic networks

The security and privacy issues in large scale deployments of RFID/IOT systems is of critical concern to researchers and industry alike. However scalability is often a forgotten aspect of protocol design and is not well addressed. In this paper, we propose an extension to an existing RFID protocol to address the scalability issues required in an IoT context. We also present a review of work by other researchers in this context.

Adjusting Matryoshka Protocol to Address the Scalability Issue in IoT Environment

Several grouping proof protocols have been proposed over the years but they are either found to be vulnerable to certain attacks or do not comply with EPC Class-1 Gen-2 (C1G2) standard because they use hash functions or other complex encryption schemes. Also, synchronization of keys, forward security, proving simultaneity, creating dependence, detecting illegitimate tags, eliminating unwanted tag processing and denial-of-proof (DoP) attacks have not been fully addressed by many. Our protocol addresses these important gaps and is based on Quadratic Residues property where the tags are only required to use XOR, 128-bit Pseudo Random Number Generators (PRNG) and Modulo (MOD) operations which can be easily implemented on low-cost passive tags and hence achieves EPC C1G2 compliance.

Robin Doss

Papers

A secure search protocol based on Quadratic Residues for EPC Class-1 Gen-2 UHF RFID tags

EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models

Defense against packet dropping attacks in opportunistic networks

Adjusting Matryoshka Protocol to Address the Scalability Issue in IoT Environment

Offline grouping proof protocol for RFID systems