Biblio

Over the past decade, smart grids have been widely implemented. Real-time pricing can better address demand-side management in smart grids. Real-time pricing requires managers to interact more with consumers at the data level, which raises many privacy threats. Thus, we introduce differential privacy into the Real-time pricing for privacy protection. However, differential privacy leaves more space for an adversary to compromise the robustness of the system, which has not been well addressed in the literature. In this paper, we propose a novel active attack detection scheme against stealthy attacks, and then give the proof of correctness and effectiveness of the proposed scheme. Further, we conduct extensive experiments with real datasets from CER to verify the detection performance of the proposed scheme.

In recent years, a new type of DDoS attacks against backbone routing links have appeared. They paralyze the communication network of a large area by directly congesting the key routing links concerning the network accessibility of the area. This new type of DDoS attacks make it difficult for traditional countermeasures to take effect. This paper proposes and implements an attack detection method based on non-cooperative active measurement. Experiments show that our detection method can efficiently perceive changes of network link performance and assist in identifying such new DDoS attacks. In our testbed, the network anomaly detection accuracy can reach 93.7%.

Physical layer authentication scheme for node authentication using the time-reversal (TR) process and the location-specific key feature of the channel impulse response (CIR) in an underwater time-varying multipath environment is proposed. TR is a well-known signal focusing technique in signal processing; this focusing effect is used by the database maintaining node to authenticate the sensor node by convolving the estimated CIR from a probe signal with its database of CIRs. Maximum time-reversal resonating strength (MTRRS) is calculated to make an authentication decision. This work considers a static underwater acoustic sensor network (UASN) under the “Alice- Bob-Eve” scenario. The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA).

In this paper, we propose the first traffic monitoring and labeling system for iOS devices, named iMonitor, which not just captures mobile network traffic in .pcap files, but also provides comprehensive APP-related and user-related information of captured packets. Through further analysis, one can obtain the exact APP or device where each packet comes from. The labeled traffic can be used in many research areas for mobile security, such as privacy leakage detection and user profiling. Given the implementation methodology of NetworkExtension framework of iOS 9+, APP labels of iMonitor are reliable enough so that labeled traffic can be regarded as training data for any traffic classification methods. Evaluations on real iPhones demonstrate that iMonitor has no notable impact upon user experience even with slight packet latency. Also, the experiment result supports our motivation that mobile traffic monitoring for iOS is absolutely necessary, as traffic generated by different OSes like Android and iOS are different and unreplaceable in researches.

Recent studies have shown that co-resident attacks have aroused great security threat in cloud. Since hardware is shared among different tenants, malicious tenants can launch various co-resident attacks, such as side channel attacks, covert channel attacks and resource interference attacks. Existing countermeasures have their limitations and can not provide comprehensive defense against co-resident attacks. This paper combines the advantages of various countermeasures and proposes a complete co-resident threat defense solution which consists of co-resident-resistant VM allocation (CRRVA), analytic hierarchy process-based threat score mechanism (AHPTSM) and attack-aware VM reallocation (AAVR). CRRVA securely allocates VMs and also takes load balance and power consumption into consideration to make the allocation policy more practical. According to the intrinsic characteristics of co-resident attacks, AHPTSM evaluates VM's threat score which denotes the probability that a VM is suffering or conducting co-resident attacks based on analytic hierarchy process. And AAVR further migrates VMs with extremely high threat scores and separates VM pairs which are likely to be malicious to each other. Extensive experiments in CloudSim have shown that CRRVA can greatly reduce the allocation co-resident threat as well as balancing the load for both CSPs and tenants with little impact on power consumption. In addition, guided by threat score distribution, AAVR can effectively guarantee runtime co-resident security by migrating high threat score VMs with less migration cost.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Facial recognition and detection is a traditional problem of computer vision, and the problem of facial key points detection is one of the most important branches of it. As the development of deep learning, more and more methods based on it are proposed for solving related issues, which bring numerous revolutionary changes. In our experiment, we propose a simplified method based on Convolution Neural Network for solving the problem of detecting 5 human facial key points. The method mainly consists of 2 sections. The first one is determining the facial area in an image, the output of which is a matrix representing coordinates of the facial area. The second one detects the relative position within the facial area and fine-tune it repeatedly. In the two sections, we design the structure of Neural Network for it, which size of hidden layers is small but stay efficient. We use Caffe, a popular open source framework of deep learning, to build our neural network and get the satisfactory result.

Deep packet inspection (DPI) is widely used in content-aware network applications to detect string features. It is of vital importance to improve the DPI performance due to the ever-increasing link speed. In this demo, we propose a novel DPI architecture with a hierarchy memory structure and parallel matching engines based on memory-centric FPGA. The implemented DPI prototype is able to provide up to 60Gbps full-text string matching throughput and fast rules update speed.