The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. However, the latter can further load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility of where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious -although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript downloading malware; worryingly, we find this propensity is greater among implicitly trusted JavaScripts.

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Purpose: Cloud computing systems represent one of the most complex computing systems currently in existence. Current applications of Cloud involve extensive use of distributed systems with varying degree of connectivity and usage. With a recent focus on large-scale proliferation of Cloud computing, identity management in Cloud based systems is a critical issue for the sustainability of any Cloud-based service. This area has also received considerable attention from the research community as well as the IT industry. Numerous Cloud Identity Management Systems (IDMSs) have been proposed so far; however, most of those systems are neither widely accepted nor considered highly reliable due to their constraints in terms of scope, applicability and security. In order to achieve reliability and effectiveness in IDMs for Cloud, further extensive research needs to be carried out to critically examine Cloud based IDMSs and their level of security. Methods: In this work, we have holistically analyzed Cloud IDMSs to better understand the general as well as the security aspects of this domain. From the security perspective, we present a comprehensive list of attacks that occur frequently in Cloud based IDMSs. In order to alleviate those attacks, we present a well-organized taxonomy tree covering the most desired features essential for any Cloud-based IDMSs. Additionally, we have specified various mechanisms of realization (such as access control polices, encryption, self-service) against each of the features of Cloud IDMSs. We have further used the proposed taxonomy as an assessment criterion for the evaluation of Cloud based IDMSs. Results: Our in-depth analysis of various Cloud based IDMSs reveals that most of the systems do not offer support to all the essential features of Cloud IDMS and the ones that do, have their own certain weaknesses. None of the discussed techniques heuristically covers all the security features; moreover, they lack compliance to international standards which, understandably, undermines their credibility. Conclusion: Presented work will help Cloud subscribers and providers in understanding the available solutions as well as the involved risks, allowing them to make more knowledgeable decisions while selecting potential Cloud IDMSs that best suits their functional and security requirements.

Abstract:We argue that touch-based gestures on touchscreen devices enable the threat of a form of persistent and ubiquitous tracking which we call touch-based tracking. Touch-based tracking goes beyond the tracking of virtual identities and has the potential for cross-device tracking as well as identifying multiple users using the same device. We demonstrate the likelihood of touchbased tracking by focusing on touch gestures widely used to interact with touch devices such as swipes and taps.. Our objective is to quantify and measure the information carried by touch-based gestures which may lead to tracking users. For this purpose, we develop an information theoretic method that measures the amount of information about users leaked by gestures when modelled as feature vectors. Our methodology allows us to evaluate the information leaked by individual features of gestures, samples of gestures, as well as samples of combinations of gestures. Through our purposebuilt app, called TouchTrack, we gather gesture samples from 89 users, and demonstrate that touch gestures contain sufficient information to uniquely identify and track users. Our results show that writing samples (on a touch pad) can reveal 73.7% of information (when measured in bits), and left swipes can reveal up to 68.6% of information. Combining different combinations of gestures results in higher uniqueness, with the combination of keystrokes, swipes and writing revealing up to 98.5% of information about users. We further show that, through our methodology, we can correctly re-identify returning users with a success rate of more than 90%.

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Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

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Users leave a trail of their personal data, interests, and intents while suring or sharing information on the Web. Web data could therefore reveal some private/sensitive information about users based on inference analysis. The possible identiication of information corresponding to a single individual by an inference attack holds true even if the user identiiers are encoded or removed in the Web data. Several works have been done on improving privacy of Web data through obfuscation methods [7,12,18,32]. However, these methods are neither comprehensive, generic to be applicable to any Web data, nor efective against adversarial attacks. To this end, we propose a privacy-aware obfuscation method for Web data addressing these identiied drawbacks of existing methods. We use probabilistic methods to predict privacy risk of Web data that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of Web data. The Web data with high predicted risk are then obfuscated by our method to minimize the privacy risk using semantically similar data. Our method is resistant against adversary who has knowledge about the datasets and model learned risk probabilities using diferential privacy-based noise addition. Experimental study conducted on two real Web datasets validates the signiicance and eicacy of our method. Our results indicate that the average privacy risk reaches to 100% with a minimum of 10 sensitive Web entries, while at most 0% privacy risk could be attained with our obfuscation method at the cost of average utility loss of 64.3%.