Biblio

Unsupervised cross-domain NER task aims to solve the issues when data in a new domain are fully-unlabeled. It leverages labeled data from source domain to predict entities in unlabeled target domain. Since training models on large domain corpus is time-consuming, in this paper, we consider an alternative way by introducing syntactic dependency structure. Such information is more accessible and can be shared between sentences from different domains. We propose a novel framework with dependency-aware GNN (DGNN) to learn these common structures from source domain and adapt them to target domain, alleviating the data scarcity issue and bridging the domain gap. Experimental results show that our method outperforms state-of-the-art methods.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

With the rapid development of the wireless network and smart mobile equipment, many lawbreakers employ mobile devices to destroy and steal important information and property from other persons. In order to fighting the criminal act efficiently, the public security organ need to collect the evidences from the crime tools and submit to the court. In the meantime, with development of internal storage technology, the law enforcement officials collect lots of information from the smart mobile equipment, for the sake of handling the huge amounts of data, we propose a framework that combine distributed clustering methods to analyze data sets, this model will split massive data into smaller pieces and use clustering method to analyze each smaller one on disparate machines to solve the problem of large amount of data, thus forensics investigation work will be more effectively.

The classical key distribution systems used for data transmission in networked microgrids (NMGs) rely on mathematical assumptions, which however can be broken by attacks from quantum computers. This paper addresses this quantum-era challenge by using quantum key distribution (QKD). Specifically, the novelty of this paper includes 1) a QKD-enabled communication architecture it devises for NMGs, 2) a real-time QKD- enabled NMGs testbed it builds in an RTDS environment, and 3) a novel two-level key pool sharing (TLKPS) strategy it designs to improve the system resilience against cyberattacks. Test results validate the effectiveness of the presented strategy, and provide insightful resources for building quantum-secure NMGs.

Internet of Things (IoT) and cloud computing are promising technologies that change the way people communicate and live. As the data collected through IoT devices often involve users' private information and the cloud is not completely trusted, users' private data are usually encrypted before being uploaded to cloud for security purposes. Searchable encryption, allowing users to search over the encrypted data, extends data flexibility on the premise of security. In this paper, to achieve the accurate and efficient ciphertext searching, we present an efficient multi-keyword ranked searchable encryption scheme supporting ciphertext-policy attribute-based encryption (CP-ABE) test (MRSET). For efficiency, numeric hierarchy supporting ranked search is introduced to reduce the dimensions of vectors and matrices. For practicality, CP-ABE is improved to support access right test, so that only documents that the user can decrypt are returned. The security analysis shows that our proposed scheme is secure, and the experimental result demonstrates that our scheme is efficient.

A hardware Trojan is a malicious circuit inserted into a device by a malicious designer or manufacturer in the circuit design or fabrication phase. With the globalization of semiconductor industry, more and more chips and devices are designed, integrated and fabricated by untrusted manufacturers, who can potentially insert hardware Trojans for launching attacks after the devices are deployed. Moreover, the most damaging attack in a smart grid is a large scale electricity failure, which can cause very serious consequences that are worse than any disaster. Unfortunately, this attack can be implemented very easily by synchronized hardware Trojans acting as a collective offline time bomb; the Trojans do not need to interact with one another and can affect a large fraction of nodes in a power grid. More sophisticatedly, this attack can also be realized by online hardware Trojans which keep listening to the communication channel and wait for a trigger event to trigger their malicious payloads; here, a broadcast message triggers all the Trojans at the same time. In this paper, we address the offline synchronized hardware Trojan attack, as it does not require the adversary to penetrate the power grid network for sending triggers. We classify two types of offline synchronized hardware Trojan attacks as type A and B: type B requires communication between different nodes, and type A does not. The hardware Trojans needed for type B turn out to be much more complex (and therefore larger in area size) than those for type A. In order to prevent type A attacks we suggest to enforce each power grid node to work in an unique time domain which has a random time offset to Universal Coordinated Time (UTC). This isolation principle can mitigate type A offline synchronized hardware Trojan attacks in a smart grid, such that even if hardware Trojans are implanted in functional units, e.g. Phasor Measurement Units (PMUs) and Remote Terminal Units (RTUs), they can only cause a minimal damage, i.e. sporadic single node failures. The proposed solution only needs a trusted Global Positioning System (GPS) module which provides the correct UTC together with small additional interface circuitry. This means that our solution can be used to protect the current power grid infrastructure against type A offline attacks without replacing any untrusted functional unit, which may already have embedded hardware Trojans.

How to debug large networks is always a challenging task. Software Defined Network (SDN) offers a centralized con- trol platform where operators can statically verify network policies, instead of checking configuration files device-by-device. While such a static verification is useful, it is still not enough: due to data plane faults, packets may not be forwarded according to control plane policies, resulting in network faults at runtime. To address this issue, we present VeriDP, a tool that can continuously monitor what we call control-data plane consistency, defined as the consistency between control plane policies and data plane forwarding behaviors. We prototype VeriDP with small modifications of both hardware and software SDN switches, and show that it can achieve a verification speed of 3 μs per packet, with a false negative rate as low as 0.1%, for the Stanford backbone and Internet2 topologies. In addition, when verification fails, VeriDP can localize faulty switches with a probability as high as 96% for fat tree topologies.