Covert channels are used for the secret transfer of information. Encryption only protects communication from being decoded by unauthorised parties, whereas covert channels aim to hide the very existence of the communication. Initially, covert channels were identified as a security threat on monolithic systems i.e. mainframes. More recently focus has shifted towards covert channels in computer network protocols. The huge amount of data and vast number of different protocols in the Internet seems ideal as a high-bandwidth vehicle for covert communication. This article is a survey of the existing techniques for creating covert channels in widely deployed network and application protocols. We also give an overview of common methods for their detection, elimination, and capacity limitation, required to improve security in future computer networks.

https://researchbank.swinburne.edu.au/file/5de97ea6-fcdf-43e5-98ae-0567cdd495f6/1/PDF (Published version).pdf

A survey of covert channels and countermeasures in computer network protocols

https://cyberleninka.org/article/n/854933.pdf

Social media analytics – Challenges in topic discovery, data collection, and data preparation

Private information retrieval (PIR) schemes allow a user to retrieve the ith bit of an n-bit data string x, replicated in k?2 databases (in the information-theoretic setting) or in k?1 databases (in the computational setting), while keeping the value of i private. The main cost measure for such a scheme is its communication complexity. In this paper we introduce a model of symmetrically-private information retrieval (SPIR), where the privacy of the data, as well as the privacy of the user, is guaranteed. That is, in every invocation of a SPIR protocol, the user learns only a single physical bit of x and no other information about the data. Previously known PIR schemes severely fail to meet this goal. We show how to transform PIR schemes into SPIR schemes (with information-theoretic privacy), paying a constant factor in communication complexity. To this end, we introduce and utilize a new cryptographic primitive, called conditional disclosure of secrets, which we believe may be a useful building block for the design of other cryptographic protocols. In particular, we get a k-database SPIR scheme of complexity O(n1/(2k?1)) for every constant k?2 and an O(logn)-database SPIR scheme of complexity O(log2n·loglogn). All our schemes require only a single round of interaction, and are resilient to any dishonest behavior of the user. These results also yield the first implementation of a distributed version of (n1)-OT (1-out-of-n oblivious transfer) with information-theoretic security and sublinear communication complexity.

Protecting Data Privacy in Private Information Retrieval Schemes.

The website fingerprinting attack aims to identify the content (i.e., a webpage accessed by a client) of encrypted and anonymized connections by observing patterns of data flows such as packet size and direction. This attack can be performed by a local passive eavesdropper – one of the weakest adversaries in the attacker model of anonymization networks such as Tor. In this paper, we present a novel website fingerprinting attack. Based on a simple and comprehensible idea, our approach outperforms all state-of-the-art methods in terms of classification accuracy while being computationally dramatically more efficient. In order to evaluate the severity of the website fingerprinting attack in reality, we collected the most representative dataset that has ever been built, where we avoid simplified assumptions made in the related work regarding selection and type of webpages and the size of the universe. Using this data, we explore the practical limits of website fingerprinting at Internet scale. Although our novel approach is by orders of magnitude computationally more efficient and superior in terms of detection accuracy, for the first time we show that no existing method – including our own – scales when applied in realistic settings. With our analysis, we explore neglected aspects of the attack and investigate the realistic probability of success for different strategies a real-world adversary may follow.

/pdf/website-fingerprinting-at-internet-scale-2tbwdvimjt.pdf

Website Fingerprinting at Internet Scale

A key feature that distinguishes modern botnets from earlier counterparts is their increasing use of structured overlay topologies. This lets them carry out sophisticated coordinated activities while being resilient to churn, but it can also be used as a point of detection. In this work, we devise techniques to localize botnet members based on the unique communication patterns arising from their overlay topologies used for command and control. Experimental results on synthetic topologies embedded within Internet traffic traces from an ISP's backbone network indicate that our techniques (i) can localize the majority of bots with low false positive rate, and (ii) are resilient to incomplete visibility arising from partial deployment of monitoring systems and measurement inaccuracies from dynamics of background traffic.

/pdf/botgrep-finding-p2p-bots-with-structured-graph-analysis-4grgf6xkjq.pdf

BotGrep: finding P2P bots with structured graph analysis

Malicious software is rampant on the Internet and costs billions of dollars each year. Safe and thorough analysis of malware is key to protecting vulnerable systems and cleaning those that have already been infected. Most current state-of-the-art analysis platforms run alongside the malware, increasing their detectability. This reduces the value of analysis because some malware is known to behave differently when being analyzed. Virtualization offers a compelling platform for malware analysis, with strong isolation and the ability to save and restore guest state. Current virtual machine monitors (VMMs), however, are not designed for malware analysis. Due to their complexity, they often fail to provide transparency and even expose vulnerabilities which could be exploited by the malware running inside guest system. We propose a lightweight VMM (namely MAVMM) that is designed specially for a single job: malware analysis. MAVMM does not implement unnecessary virtualization features commonly found in general purpose hypervisors, including virtual device emulation. We take advantage of hardware virtualization support to make MAVMM more simple, secure and transparent. In this paper, we describe the design and implementation of MAVMM, and the features that we can extract from programs running inside the guest OS. We evaluate our platform in three aspects: functionality, detectability and performance. We show that our system can extract useful information from malicious software, and that it is not susceptible to known virtualization detection techniques.

MAVMM: Lightweight and Purpose Built VMM for Malware Analysis

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.

Computing on Masked Data: a High Performance Method for Improving Big Data Veracity

The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals. We then survey three cryptographic techniques - homomorphic encryption, verifiable computation, and multi-party computation - that can be used to achieve these goals. We describe the cryptographic techniques in the context of our cloud model and highlight the differences in performance cost associated with each.

https://www.ieee-hpec.org/2014/CD/index_htm_files/FinalPapers/28.pdf

A survey of cryptographic approaches to securing big-data analytics in the cloud

Recent work in traffic analysis has shown that traffic patterns leaked through side channels can be used to recover important semantic information. For instance, attackers can find out which website, or which page on a website, a user is accessing simply by monitoring the packet size distribution. We show that traffic analysis is even a greater threat to privacy than previously thought by introducing a new attack that can be carried out remotely. In particular, we show that, to perform traffic analysis, adversaries do not need to directly observe the traffic patterns. Instead, they can gain sufficient information by sending probes from a far-off vantage point that exploits a queuing side channel in routers. To demonstrate the threat of such remote traffic analysis, we study a remote website detection attack that works against home broadband users. Because the remotely observed traffic patterns are more noisy than those obtained using previous schemes based on direct local traffic monitoring, we take a dynamic time warping (DTW) based approach to detecting fingerprints from the same website. As a new twist on website fingerprinting, we consider a website detection attack, where the attacker aims to find out whether a user browses a particular web site, and its privacy implications. We show experimentally that, although the success of the attack is highly variable, depending on the target site, for some sites very low error rates. We also show how such website detection can be used to deanonymize message board users.

Website Detection Using Remote Traffic Analysis

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth “V” is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.

/pdf/computing-on-masked-data-a-high-performance-method-for-fdoqt8mp5w.pdf

Nabil Schear

Papers

MAVMM: Lightweight and Purpose Built VMM for Malware Analysis

Computing on Masked Data: a High Performance Method for Improving Big Data Veracity

A survey of cryptographic approaches to securing big-data analytics in the cloud

Website Detection Using Remote Traffic Analysis

Computing on masked data: a high performance method for improving big data veracity