On Traffic Analysis Attacks to Encrypted VOIP Calls

The increasing popularity of VoIP telephony has brought a lot of attention and concern over security and privacy issues of VoIP communication. This thesis proposes a new class of traffic analysis attacks to encrypted VoIP calls. The goal of these attacks is to detect speaker or speech of encrypted VoIP calls. The proposed traffic analysis attacks exploit silent suppression, an essential feature of VoIP telephony. These attacks are based on application-level features so that the attacks can detect the same speech or the same speaker of different VoIP calls made with different VoIP codecs. We evaluate the proposed attacks by extensive experiments over different type of networks including commercialized anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speaker and speech of encrypted VoIP calls with a high detection rate which is a great improvement comparing with random guess. With the help of intersection attacks, the detection rate for speaker detection can be increased. In order to shield the detrimental effect of this proposed attacks, a countermeasure is proposed to mitigate the proposed traffic analysis attack

On Traffic Analysis Attacks to Encrypted VOIP Calls

The increasing popularity of VoIP telephony has brought a lot of attention and concern over security and privacy issues of VoIP communication. This thesis proposes a new class of traffic analysis attacks to encrypted VoIP calls. The goal of these attacks is to detect speaker or speech of encrypted VoIP calls. The proposed traffic analysis attacks exploit silent suppression, an essential feature of VoIP telephony. These attacks are based on application-level features so that the attacks can detect the same speech or the same speaker of different VoIP calls made with different VoIP codecs. We evaluate the proposed attacks by extensive experiments over different type of networks including commercialized anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speaker and speech of encrypted VoIP calls with a high detection rate which is a great improvement comparing with random guess. With the help of intersection attacks, the detection rate for speaker detection can be increased. In order to shield the detrimental effect of this proposed attacks, a countermeasure is proposed to mitigate the proposed traffic analysis attack

On Privacy of Encrypted Speech Communications

Silence suppression, an essential feature of speech communications over the Internet, saves bandwidth by disabling voice packet transmissions when silence is detected. However, silence suppression enables an adversary to recover talk patterns from packet timing. In this paper, we investigate privacy leakage through the silence suppression feature. More specifically, we propose a new class of traffic analysis attacks to encrypted speech communications with the goal of detecting speakers of encrypted speech communications. These attacks are based on packet timing information only and the attacks can detect speakers of speech communications made with different codecs. We evaluate the proposed attacks with extensive experiments over different type of networks including commercial anonymity networks and campus networks. The experiments show that the proposed traffic analysis attacks can detect speakers of encrypted speech communications with high accuracy based on traces of 15 minutes long on average

Sustainable high-strength alkali-activated slag concrete is achieved by recycling emulsified waste cooking oil

To mitigate the shrinkage of high-strength alkali-activated slag concrete (AASC), this paper introduces emulsified cooking oil (ECO) and emulsified waste cooking oil (EWCO) into the AASC system. The effects of admixing ECO and EWCO on the compressive strength, drying shrinkage, autogenous shrinkage, carbonation, and sulfuric acid resistance of the AASC are systematically explored. The optimization mechanism is also proposed based on the surface tension and microstructural analysis. The experimental results show that the admixing ECO and EWCO slightly reduce the compressive strength of the AASC by 7.8%. Interestingly, the admixing ECO and EWCO significantly reduce the drying shrinkage and autogenous shrinkage, simultaneously improving the resistance to carbonation and sulfuric acid of the AASC. Specifically, the introduction of 2 wt.% ECO and EWCO can reduce the autogenous shrinkage of the AASC by 66.7% and 41.0%, respectively. Microstructural observations reveal that the addition of ECO and EWCO can reduce the internal surface tension of the AASC, improve the transport and diffusion of the pore solution, and increase the absorbable free water of the slag, which in turn reduces the shrinkage of the composites. It also increases the ionic concentration in the pore solution, resulting in a more complete reaction of the AASC, which can optimize the pore structure and thus improve the durability of the AASC. This study proposes a promising way to develop sustainable alkali-activated slag concrete achieved by recycling waste materials

Induced cultivation pattern enhanced the phycoerythrin production in red alga Porphyridium purpureum.

Porphyridium purpureum is a rich source for producing phycoerythrin (PE); however, the PE content is greatly affected by culture conditions. Researchers have aimed to optimize the cultivation of P. purpureum for accumulation of PE. When traditional optimized culture conditions were used to cultivate P. purpureum, high PE contents were not usually achieved. In this study, an induced cultivation pattern was applied to P. purpureum for PE biosynthesis (i.e., an incremental approach by altering temperatures, light intensities, and nitrate concentrations). Results revealed that the induced pattern greatly improved the PE biosynthesis. The optimized PE content of 229 mg/L was achieved on the 12th cultivation day, which was a maximum PE content within one cultivation period and accounted for approximately 3.05% of the dry biomass. The induced cultivation pattern was highly suitable for PE synthesis in P. purpureum, which provided an important reference value to the large-scale production of PE

A Myb Transcription Factor of Phytophthora sojae, Regulated by MAP Kinase PsSAK1, Is Required for Zoospore Development

PsSAK1, a mitogen-activated protein (MAP) kinase from Phytophthora sojae, plays an important role in host infection and zoospore viability. However, the downstream mechanism of PsSAK1 remains unclear. In this study, the 3'-tag digital gene expression (DGE) profiling method was applied to sequence the global transcriptional sequence of PsSAK1-silenced mutants during the cysts stage and 1.5 h after inoculation onto susceptible soybean leaf tissues. Compared with the gene expression levels of the recipient P. sojae strain, several candidates of Myb family were differentially expressed (up or down) in response to the loss of PsSAK1, including of a R2R3-type Myb transcription factor, PsMYB1. qRT-PCR indicated that the transcriptional level of PsMYB1 decreased due to PsSAK1 silencing. The transcriptional level of PsMYB1 increased during sporulating hyphae, in germinated cysts, and early infection. Silencing of PsMYB1 results in three phenotypes: a) no cleavage of the cytoplasm into uninucleate zoospores or release of normal zoospores, b) direct germination of sporangia, and c) afunction in zoospore-mediated plant infection. Our data indicate that the PsMYB1 transcription factor functions downstream of MAP kinase PsSAK1 and is required for zoospore development of P. sojae

Multi-View Domain Adaptive Object Detection on Camera Networks

In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods