Biblio

Language-based information-flow control (IFC) techniques often rely on special purpose, ad-hoc primitives to address different covert channels that originate in the runtime system, beyond the scope of language constructs. Since these piecemeal solutions may not compose securely, there is a need for a unified mechanism to control covert channels. As a first step towards this goal, we argue for the design of a general interface that allows programs to safely interact with the runtime system and the available computing resources. To coordinate the communication between programs and the runtime system, we propose the use of asynchronous exceptions (interrupts), which, to the best of our knowledge, have not been considered before in the context of IFC languages. Since asynchronous exceptions can be raised at any point during execution-often due to the occurrence of an external event-threads must temporarily mask them out when manipulating locks and shared data structures to avoid deadlocks and, therefore, breaking program invariants. Crucially, the naive combination of asynchronous exceptions with existing features of IFC languages (e.g., concurrency and synchronization variables) may open up new possibilities of information leakage. In this paper, we present MACasync, a concurrent, statically enforced IFC language that, as a novelty, features asynchronous exceptions. We show how asynchronous exceptions easily enable (out of the box) useful programming patterns like speculative execution and some degree of resource management. We prove that programs in MACasync satisfy progress-sensitive non-interference and mechanize our formal claims in the Agda proof assistant.

A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.

A common technique for building ICT systems is to build them as successive layers of bstraction: for instance, the Instruction Set Architecture (ISA) is an abstraction of the hardware, and compilers or interpreters build higher level abstractions on top of the ISA.The functionality of an ICT application can often be understood by considering only a single level of abstraction. For instance the source code of the application defines the functionality using the level of abstraction of the source programming language. Functionality can be well understood by just studying this source code.Many important security issues in ICT system however are cross-layer issues: they can not be understood by considering the system at a single level of abstraction, but they require understanding how multiple levels of abstraction are implemented. Attacks may rely on, or exploit, implementation details of one or more layers below the source code level of abstraction.The purpose of this paper is to illustrate this cross-layer nature of security by discussing old and new examples of cross-layer security issues, and by providing a classification of these issues.

Ethereum, one of the most popular blockchain platforms, provides financial transactions like payments and auctions through smart contracts. Due to the immense interest in smart contracts in academia, the research community of smart contract security has made a significant improvement recently. Researchers have reported various security vulnerabilities in smart contracts, and developed static analysis tools and verification frameworks to detect them. However, it is unclear whether such great efforts from academia has indeed enhanced the security of smart contracts in reality. To understand the security level of smart contracts in the wild, we empirically studied 55,046 real-world Ethereum smart contracts written in Solidity, the most popular programming language used by Ethereum smart contract developers. We first examined how many well-known vulnerabilities the Solidity compiler has patched, and how frequently the Solidity team publishes compiler releases. Unfortunately, we observed that many known vulnerabilities are not yet patched, and some patches are not even sufficient to avoid their target vulnerabilities. Subsequently, we investigated whether smart contract developers use the most recent compiler with vulnerabilities patched. We reported that developers of more than 98% of real-world Solidity contracts still use older compilers without vulnerability patches, and more than 25% of the contracts are potentially vulnerable due to the missing security patches. To understand actual impacts of the missing patches, we manually investigated potentially vulnerable contracts that are detected by our static analyzer and identified common mistakes by Solidity developers, which may cause serious security issues such as financial loss. We detected hundreds of vulnerable contracts and about one fourth of the vulnerable contracts are used by thousands of people. We recommend the Solidity team to make patches that resolve known vulnerabilities correctly, and developers to use the latest Solidity compiler to avoid missing security patches.

Improving the security of data transmission in wireless channels is a key and challenging problem in wireless communication. This paper presents a data security transmission scheme based on high efficiency fountain code. If the legitimate receiver can decode all the original files before the eavesdropper, it can guarantee the safe transmission of the data, so we use the efficient coding scheme of the fountain code to ensure the efficient transmission of the data, and add the feedback mechanism to the transmission of the fountain code so that the coding scheme can be updated dynamically according to the decoding situation of the legitimate receiver. Simulation results show that the scheme has high security and transmitter transmission efficiency in the presence of eavesdropping scenarios.

The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

Malware threats often go undetected immediately, because attackers can camouflage well within the system. The users realize this after the devices stop working and cause harm for them. One way to deceive malicious content detection, malware authors use packers. Malware analysis is an activity to gain knowledge about malware. Reverse engineering is a technique used to identify and deal with new viruses or to understand malware behavior. Therefore, this technique can be the right choice for conducting malware analysis, especially for malware with packers. The results of the analysis are used as a source for making creating indicator of compromise in the YARA rule format. YARA rule is used as a component for detecting malware using the indicators obtained in the analysis process.

Preserving medical data is of utmost importance to stake holders. There are not many laws in India about preservation, usability of patient records. When data is transmitted across the globe there are chances of data getting tampered intentionally or accidentally. Tampered data loses its authenticity for diagnostic purpose, research and various other reasons. This paper proposes an authenticity based ECDSA algorithm by signature verification to identify the tampering of medical image files and alerts by the rules of authenticity. The algorithm can be used by researchers, doctors or any other educated person in order to maintain the authenticity of the record. Presently it is applied on medical related image files like DICOM. However, it can support any other medical related image files and still preserve the authenticity.

This paper deals with novel group-based Authentication and Key Agreement protocol for Internet of Things(IoT) enabled LTE/LTE-A network to overcome the problems of computational overhead, complexity and problem of heterogeneous devices, where other existing methods are lagging behind in attaining security requirements and computational overhead. In this work, two Groups are created among Machine Type Communication Devices (MTCDs) on the basis of device type to reduce complexity and problems of heterogeneous devices. This paper fulfills all the security requirements such as preservation, mutual authentication, confidentiality. Bio-metric authentication has been used to enhance security level of the network. The security and performance analysis have been verified through simulation results. Moreover, the performance of the proposed Novel Group-Based Authentication and key Agreement(AKA) Protocol is analyzed with other existing IoT enabled LTE/LTE-A protocol.

Electronic voting systems have enhanced efficiency in student elections management in universities, supporting such elections to become less expensive, logistically simple, with higher accuracy levels as compared to manually conducted elections. However, e-voting systems that are confined to campus hall voting inhibits access to eligible voters who are away from campus. This study examined the challenges of lack of wide access and impersonation of voter in the student elections of 2018 in Kabarak University. The main objective of this study was therefore to upgrade the offline electronic voting system through developing a secure online voting system and deploying the system for use in the 2019 student elections at Kabarak University. The resultant system and development process employed demonstrate the applicability of a secure online voting not only in the higher education context, but also in other democracies where infusion of online access and authentication in the voting processes is a requisite.

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of The National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data.

Security has become the vital component of today's technology. People wish to safeguard their valuable items in bank lockers. With growing technology most of the banks have replaced the manual lockers by digital lockers. Even though there are numerous biometric approaches, these are not robust. In this work we propose a new approach for personal biometric identification based on features extracted from ECG.

With the help of technological advancements in the last decade, it has become much easier to extensively and remotely observe medical conditions of the patients through wearable biosensors that act as connected nodes on Body Area Networks (BANs). Sensitive nature of the critical data captured and communicated via wireless medium makes it extremely important to process it as securely as possible. In this regard, lightweight security mechanisms are needed to overcome the hardware resource restrictions of biosensors. Random and secure cryptographic key generation and agreement among the biosensors take place at the core of these security mechanisms. In this paper, we propose the SKA-PSAR (Augmented Randomness for Secure Key Agreement using Physiological Signals) system to produce highly random cryptographic keys for the biosensors to secure communication in BANs. Similar to its predecessor SKA-PS protocol by Karaoglan Altop et al., SKA-PSAR also employs physiological signals, such as heart rate and blood pressure, as inputs for the keys and utilizes the set reconciliation mechanism as basic building block. Novel quantization and binarization methods of the proposed SKA-PSAR system distinguish it from SKA-PS by increasing the randomness of the generated keys. Additionally, SKA-PSAR generated cryptographic keys have distinctive and time variant characteristics as well as long enough bit sizes that provides resistance against cryptographic attacks. Moreover, correct key generation rate is above 98% with respect to most of the system parameters, and false key generation rate of 0% have been obtained for all system parameters.

The paper proposes a novel technique of EEG induced Brain-Computer Interface system for user authentication of personal devices. The scheme enables a human user to lock and unlock any personal device using his/her mind generated password. A two stage security verification is employed in the scheme. In the first stage, a 3 × 3 spatial matrix of flickering circles will appear on the screen of which, rows are blinked randomly and user has to mentally select a row which contains his desired circle.P300 is released when the desired row is blinked. Successful selection of row is followed by the selection of a flickering circle in the desired row. Gazing at a particular flickering circle generates SSVEP brain pattern which is decoded to trace the mentally selected circle. User is able to store mentally uttered number in the selected circle, later the number with it's spatial position will serve as the password for the unlocking phase. Here, the user is equipped with a headphone where numbers starting from zero to nine are spelled randomly. Spelled number matching with the mentally uttered number generates auditory P300 in the subject's brain. The particular choice of mentally uttered number is detected by successful detection of auditory P300. A novel weight update algorithm of Recurrent Neural Network (RNN), based on Extended-Kalman Filter and Particle Filter is used here for classifying the brain pattern. The proposed classifier achieves the best classification accuracy of 95.6%, 86.5% and 83.5% for SSVEP, visual P300 and auditory P300 respectively.

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.

Accessing the secured data through the network is a major task in emerging technology. Data needs to be protected from the network vulnerabilities, malicious users, hackers, sniffers, intruders. The novel framework has been designed to provide high security in data transaction through computer network. The implant of network amalgamation in the recent trends, make the way in security enhancement in an efficient manner through the machine learning algorithm. In this system the usage of the biometric authenticity plays a vital role for unique approach. The novel mathematical approach is used in machine learning algorithms to solve these problems and provide the security enhancement. The result shows that the novel method has consistent improvement in enhancing the security of data transactions in the emerging technologies.

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots' correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5GiB instead of 230GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5h to 46min, with even more savings for less powerful devices.

Network adversaries, such as malicious transit autonomous systems (ASes), have been shown to be capable of partitioning the Bitcoin's peer-to-peer network via routing-level attacks; e.g., a network adversary exploits a BGP vulnerability and performs a prefix hijacking attack (viz. Apostolaki et al. [3]). Due to the nature of BGP operation, such a hijacking is globally observable and thus enables immediate detection of the attack and the identification of the perpetrator. In this paper, we present a stealthier attack, which we call the EREBUS attack, that partitions the Bitcoin network without any routing manipulations, which makes the attack undetectable to control-plane and even to data-plane detectors. The novel aspect of EREBUS is that it makes the adversary AS a natural man-in-the-middle network of all the peer connections of one or more targeted Bitcoin nodes by patiently influencing the targeted nodes' peering decision. We show that affecting the peering decision of a Bitcoin node, which is believed to be infeasible after a series of bug patches against the earlier Eclipse attack [29], is possible for the network adversary that can use abundant network address resources (e.g., spoofing millions of IP addresses in many other ASes) reliably for an extended period of time at a negligible cost. The EREBUS attack is readily available for large ASes, such as Tier-1 and large Tier-2 ASes, against the vast majority of 10K public Bitcoin nodes with only about 520 bit/s of attack traffic rate per targeted Bitcoin node and a modest (e.g., 5-6 weeks) attack execution period. The EREBUS attack can be mounted by nation-state adversaries who would be willing to execute sophisticated attack strategies patiently to compromise cryptocurrencies (e.g., control the consensus, take down a cryptocurrency, censor transactions). As the attack exploits the topological advantage of being a network adversary but not the specific vulnerabilities of Bitcoin core, no quick patches seem to be available. We discuss that some naive solutions (e.g., whitelisting, rate-limiting) are ineffective and third-party proxy solutions may worsen the Bitcoin's centralization problem. We provide some suggested modifications to the Bitcoin core and show that they effectively make the EREBUS attack significantly harder; yet, their non-trivial changes to the Bitcoin's network operation (e.g., peering dynamics, propagation delays) should be examined thoroughly before their wide deployment.

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

In the dawn of crypto-currencies the most talked currency is Bitcoin. Bitcoin is widely flourished digital currency and an exchange trading commodity implementing peer-to-peer payment network. No central athourity exists in Bitcoin. The users in network or pool of bitcoin need not to use real names, rather they use pseudo names for managing and verifying transactions. Due to the use of pseudo names bitcoin is apprehended to provide anonymity. However, the most transparent payment network is what bitcoin is. Here all the transactions are publicly open. To furnish wholeness and put a stop to double-spending, Blockchain is used, which actually works as a ledger for management of Bitcoins. Blockchain can be misused to monitor flow of bitcoins among multiple transactions. When data from external sources is amalgamated with insinuation acquired from the Blockchain, it may result to reveal user's identity and profile. In this way the activity of user may be traced to an extent to fraud that user. Along with the popularity of Bitcoins the number of adversarial attacks has also gain pace. All these activities are meant to exploit anonymity and privacy in Bitcoin. These acivities result in loss of bitcoins and unlawful profit to attackers. Here in this paper we tried to present analysis of major attacks such as malicious attack, greater than 52% attacks and block withholding attack. Also this paper aims to present analysis and improvements in Bitcoin's anonymity and privacy.

We investigate what we call the "Bitcoin Generator Scam" (BGS), a simple system in which the scammers promise to "generate" new bitcoins using the ones that were sent to them. A typical offer will suggest that, for a small fee, one could receive within minutes twice the amount of bitcoins submitted. BGS is clearly not a very sophisticated attack. The modus operandi is simply to put up some web page on which to find the address to send the money and wait for the payback. The pages are then indexed by search engines, and ready to find for victims looking for free bitcoins. We describe here a generic system to find and analyze scams such as BGS. We have trained a classifier to detect these pages, and we have a crawler searching for instances using a series of search engines. We then monitor the instances that we find to trace payments and bitcoin addresses that are being used over time. Unlike most bitcoin-based scam monitoring systems, we do not rely on analyzing transactions on the blockchain to find scam instances. Instead, we proactively find these instances through the web pages advertising the scam. Thus our system is able to find addresses with very few transactions, or even none at all. Indeed, over half of the addresses that have eventually received funds were detected before receiving any transactions. The data for this paper was collected over four months, from November 2019 to February 2020. We have found more than 1,300 addresses directly associated with the scam, hosted on over 500 domains. Overall, these addresses have received (at least) over 5 million USD to the scam, with an average of 47.3 USD per transaction.

Payment channel networks have been introduced to mitigate the scalability issues inherent to permissionless decentralized cryptocurrencies such as Bitcoin. Launched in 2018, the Lightning Network (LN) has been gaining popularity and consists today of more than 5000 nodes and 35000 payment channels that jointly hold 965 bitcoins (9.2M USD as of June 2020). This adoption has motivated research from both academia and industryPayment channels suffer from security vulnerabilities, such as the wormhole attack [39], anonymity issues [38], and scalability limitations related to the upper bound on the number of concurrent payments per channel [28], which have been pointed out by the scientific community but never quantitatively analyzedIn this work, we first analyze the proneness of the LN to the wormhole attack and attacks against anonymity. We observe that an adversary needs to control only 2% of nodes to learn sensitive payment information (e.g., sender, receiver, and amount) or to carry out the wormhole attack. Second, we study the management of concurrent payments in the LN and quantify its negative effect on scalability. We observe that for micropayments, the forwarding capability of up to 50% of channels is restricted to a value smaller than the channel capacity. This phenomenon hinders scalability and opens the door for denial-of-service attacks: we estimate that a network-wide DoS attack costs within 1.6M USD, while isolating the biggest community costs only 238k USDOur findings should prompt the LN community to consider the issues studied in this work when educating users about path selection algorithms, as well as to adopt multi-hop payment protocols that provide stronger security, privacy and scalability guarantees.

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.