Sendong Zhao

Security flaws in two improved remote user authentication schemes using smart cards

SUMMARY n nUnderstanding security failures of cryptographic protocols is the key to both patching existing protocols and designing future schemes. In this paper, we analyze two recent proposals in the area of password-based remote user authentication using smart cards. First, we point out that the scheme of Chen et al. cannot achieve all the claimed security goals and report its following flaws: (i) it is vulnerable to offline password guessing attack under their nontamper resistance assumption of the smart cards; and (ii) it fails to provide forward secrecy. Then, we analyze an efficient dynamic ID-based scheme without public-key operations introduced by Wen and Li in 2012. This proposal attempts to overcome many of the well-known security and efficiency shortcomings of previous schemes and supports more functionalities than its counterparts. Nevertheless, Wen–Lis protocol is vulnerable to offline password guessing attack and denial of service attack, and fails to provide forward secrecy and to preserve user anonymity. Furthermore, with the security analysis of these two schemes and our previous protocol design experience, we put forward three general principles that are vital for designing secure smart-card-based password authentication schemes: (i) public-key techniques are indispensable to resist against offline password guessing attack and to preserve user anonymity under the nontamper resistance assumption of the smart card; (ii) there is an unavoidable trade-off when fulfilling the goals of local password update and resistance to smart card loss attack; and (iii) at least two exponentiation (respectively elliptic curve point multiplication) operations conducted on the server side are necessary for achieving forward secrecy. The cryptanalysis results discourage any practical use of the two investigated schemes and are important for security engineers to make their choices correctly, whereas the proposed three principles are valuable to protocol designers for advancing more robust schemes. Copyright

Secure Password-based Remote User Authentication Scheme Against Smart Card Security Breach

It is a challenge for password authentication protocols using non-tamper resistant smart cards to achieve user anonymity, forward secrecy, immunity to various attacks and high performance at the same time. In 2011, Li and Lee showed that both Hsiang-Shih’s password-based remote user authentication schemes are vulnerable to various attacks if the smart card is non-tamper resistant. Consequently, an improved scheme was developed to preclude the identified weaknesses and claimed that it is secure against smart card loss attacks. In this paper, however, we will show that Li-Lee’s scheme still cannot withstand offline password guessing attack under the non-tamper resistance assumption of the smart card. In addition, their scheme is also vulnerable to denial of service attack and fails to provide user anonymity and forward secrecy. As our main contribution, a robust scheme is presented to cope with the aforementioned defects, while keeping the merits of different password authentication schemes using smart cards. The analysis demonstrates that our scheme meets all the proposed criteria and eliminates several hard security threats that are difficult to be tackled at the same time in previous scholarship.

Event causality extraction based on connectives analysis

Causality is an important type of relation which is crucial in numerous tasks, such as predicting future events, generating scenario, question answering, textual entailment and discourse comprehension. Therefore, causality extraction is a fundamental task in text mining. Many efforts have been dedicated to extracting causality from texts utilizing patterns, constraints and machine learning techniques. This paper presents a new Restricted Hidden Naive Bayes model to extract causality from texts. Besides some commonly used features, such as contextual features, syntactic features, position features, we also utilize a new category feature of causal connectives. This new feature is obtained from the tree kernel similarity of sentences containing connectives. In previous studies, the features have been usually assumed to be independent, which is not the case in reality. The advantage of our model lies in its ability to cope with partial interactions among features so as to avoid over-fitting problem on Hidden Naive Bayes model, especially the interaction between the connective category and the syntactic structure of sentences. Evaluation on a public dataset shows that our method goes beyond all the baselines.

Constructing and Embedding Abstract Event Causality Networks from Text Snippets

In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with abstract event nodes represented by frequently co-occurring word pairs. To perform the embedding task, we design a dual cause-effect transition model. Therefore, the proposed method can obtain general, frequent, and simple causality patterns, meanwhile, simplify event matching. Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction. Experimental results demonstrate that 1) the abstract causality network is effective for discovering high-level causality rules behind specific causal events; 2) the embedding models perform better than state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement prediction.

Multi-modal microblog classification via multi-task learning

Recent years have witnessed the flourishing of social media platforms (SMPs), such as Twitter, Facebook, and Sina Weibo. The rapid development of these SMPs has resulted in increasingly large scale multimedia data, which has been proved with remarkable marketing values. It is in an urgent need to classify these social media data into a specified list of concerned entities, such as brands, products, and events, to analyze their sales, popularity or influences. But this is a rather challenging task due to the shortness, conversationality, the incompatibility between images and text, and the data diversity of microblogs. In this paper, we present a multi-modal microblog classification method in a multi-task learning framework. Firstly features of different modalities are extracted for each microblog. Specifically, we extract TF-IDF features for each microblog text and low-level visual features and high-level semantic features for each microblog image. Then multiple related classification tasks are learned simultaneously for each feature to increase the sample size for each task and improve the prediction performance. Finally the outputs of each feature are integrated by a Support Vector Machine that learns how to optimally combine and weight each feature. We evaluate the proposed method on Brand-Social-Net to classify the contained 100 brands. Experimental results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

A new scheme with secure cookie against SSLStrip attack

In 2009 Moxie Marlinspike proposed a new Man-in-the- Middle (MitM) attack on secure socket layer (SSL) called SSLStrip attack at Black Hat DC, which is a serious threat to Web users. Some solutions have been proposed in literature. However, until now there is no practical countermeasure to resist on such attack. In this paper, we propose a new scheme to defend against SSLStrip attack by improving the previous secure cookie protocols and using proxy pattern and reverse proxy pattern. It implements a secure LAN guaranteed proxy in client-side, a secure server guaranteed proxy in server-side and a cookie authentication mechanism to provide the following security services: source authentication, integrity control and defending SSLStrip attack.

Breaking a Robust Remote User Authentication Scheme Using Smart Cards

Understanding security failures of cryptographic protocols is the key to both patching existing protocols and designing future schemes. Recently, Yeh et al. showed that Hsiang and Shih’s password-based remote user authentication scheme is vulnerable to various attacks if the smart card is non-tamper resistant, and proposed an improved version which was claimed to be efficient and secure. In this study, however, we find that, although Yeh et al.’s scheme possesses many attractive features, it still cannot achieve the claimed security goals, and we report its following flaws: (1) It cannot withstand offline password guessing attack and key-compromise impersonation attack under their non-tamper resistance assumption of the smart card; (2) It fails to provide user anonymity and forward secrecy; (3) It has some other minor defects. The proposed cryptanalysis discourages any use of the scheme under investigation in practice. Remarkably, rationales for the security analysis of password-based authentication schemes using smart cards are discussed in detail.

Cookie-proxy: a scheme to prevent SSLStrip attack

A new Man-in-the-Middle (MitM) attack called SSLStrip poses a serious threat to the security of secure socket layer protocol. Although some researchers have presented some schemes to resist such attack, until now there is still no practical countermeasure. To withstand SSLStrip attack, in this paper we propose a scheme named Cookie-Proxy, including a secure cookie protocol and a new topology structure. The topology structure is composed of a proxy pattern and a reverse proxy pattern. Experiment results and formal security proof using SVO logic show that our scheme is effective to prevent SSLStrip attack. Besides, our scheme spends little extra time cost and little extra communication cost comparing with previous secure cookie protocols.

CausalTriad: Toward Pseudo Causal Relation Discovery and Hypotheses Generation from Medical Text Data

Deriving pseudo causal relations from medical text data lies at the heart of medical literature mining. Existing studies have utilized extraction models to find pseudo causal relation from single sentences, while the knowledge created by causation transitivity - often spanning multiple sentences - has not been considered. Furthermore, we observe that many pseudo causal relations follow the rule of causation transitivity, which makes it possible to discover unseen casual relations and generate new causal relation hypotheses. In this paper, we address these two issues by proposing a factor graph model to incorporate three clues to discover causation expressions in the text data. We propose four types of triad structures to represent the rules of causation transitivity among causal relations. Our proposed model, called CausalTriad, uses textual and structural knowledge to infer pseudo causal relations from the triad structures. Experimental results on two datasets demonstrate that (a) CausalTriad is effective for pseudo causal relation discovery within and across sentences; (b) CausalTriad is highly capable at recognizing implicit pseudo causal relations; (c) CausalTriad can infer missing/new pseudo causal relations from text data.

Mining Medical Causality for Diagnosis Assistance

In the medical context, causal knowledge usually refers to causal relations between diseases and symptoms, living habits and diseases, symptoms which get better and therapy, drugs and side-effects, etc [3]. All these causal relations are usually in medical literature, forum and clinical cases and compose the core part of medical diagnosis. Therefore, mining these causal knowledge to predict disease and recommend therapy is of great value for assisting patients and professionals. The task of mining these causal knowledge for diagnosis assistance can be decomposed into four constitutes: (1) mining medical causality from text; (2) medical treatment effectiveness measurement; (3) disease prediction and (4) explicable medical treatment recommendation. However, these tasks have never been systemically studied before. For my PhD thesis, I plan to formally define the problem of mining medical domain causality for diagnosis assistance and propose methods to solve this problem. 1. Ming these textual causalities can be very useful for discovering new knowledge and making decisions. Many studies have been done for causal extraction from the text [1, 4, 5]. However, all these studies are based on pattern or causal triggers, which greatly limit their power to extract causality and rarely consider the frequency of co-occurrence and contextual semantic features. Besides, none of them take the transitivity rules of causality leading to reject those causalities which can be easily get by simple inference. Therefore, we formally define the task of mining causality via frequency of event co-occurrence, semantic distance between event pairs and transitivity rules of causality, and present a factor graph to combine these three resources for causality mining. 2. Treatment effectiveness analysis is usually taken as a subset of causal analysis on observational data. For such real observational data, PSM and RCM are two dominant methods. On one hand, it is usually difficult for PSM to find the matched cases due to the sparsity of symptom. On the other hand, we should check every possible (symptom, treatment) pair by exploiting RCM, leading to make the characteristic of exploding up, especially when we want to check the causal relation between a combination of symptoms and a combination of drugs. Besides, the larger number of symptom or treatment in the combination the less number of patient case retrieved, which lead to the lack of statistical significance. Specifically, patients tend to take tens of herbs as the treatment each time in Traditional Chinese Medicine (TCM). Therefore, how to evaluate the effectiveness of herbs separately and jointly is really a big challenge. This is also a very fundamental research topic supporting many downstream applications. 3. Both hospitals and on-line forums have accumulated sheer amount of records, such as clinical text data and online diagnosis Q&A pairs. The availability of such data in large volume enables automatic disease prediction. There are some papers on disease prediction with electronic health record (EHR) [2], but the research on disease prediction with raw symptoms is still necessary and challenging. Therefore, we propose a general new idea of using the rich contextual information of diseases and symptoms to bridge the gap of disease candidates and symptoms, and detach it from the specific way of implementing the idea using network embedding. 4. Recommendation in medical domain is usually a decision-making issue, which requires the ability of explaining why. The ability of explaining why are basically from two paths. Consider the recommendation suggest you eat more vegetables. You probably do not believe it if there is nothing attached. But if the recommendation gives the literally reasons why eating more vegetables is good you might like to take this suggestion. Consider another scenario, if the recommendation gives you the data of the contrast which show that people who eat more vegetables are healthier than those eat less, it is certain that you also want to take this recommendation. Based on these two intuitions, we present a recommendation model based on proofs which are either literally reasons or difference from contrast. This work was supported by the 973 program (No. 2014CB340503) and the NSFC (No. 61133012 and No. 61472107).