On Non-Parallelizable Deterministic Client Puzzle Scheme with Batch Verification Modes

A (computational) client puzzle scheme enables a client to prove to a server that a certain amount of computing resources (CPU cycles and/or Memory look-ups) has been dedicated to solve a puzzle. Researchers have identified a number of potential applications, such as constructing timed cryptography, fighting junk emails, and protecting critical infrastructure from DoS attacks. In this paper, we first revisit this concept and formally define two properties, namely deterministic computation and parallel computation resistance. Our analysis show that both properties are crucial for the effectiveness of client puzzle schemes in most application scenarios. We prove that the RSW client puzzle scheme, which is based on the repeated squaring technique, achieves both properties. Secondly, we introduce two batch verification modes for the RSW client puzzle scheme in order to improve the verification efficiency of the server, and investigate three methods for handling errors in batch verifications. Lastly, we show that client puzzle schemes can be integrated with reputation systems to further improve the effectiveness in practice

Survey on: Software Puzzle for Offsetting DoS Attack

A Denial of Service (DoS) attack is a malevolent attempt to make a server or a network resource inaccessible to users, usually by temporarily breaking or suspending the services of a host connected to the Internet. DoS attacks and Distributed DoS (DDoS) attacks attempt to deplete an online service's resource such as network bandwidth, memory and computational power by overwhelming the service with bogus requests. Thus, DoS and DDoS attacks have become a major problem for users of computer systems connected to the Internet. Many state-art of the techniques used for defending the internet from these attacks have been discussed in this paper. After conducting an exhaustive survey on these techniques it has been found that the proposed software puzzle scheme that randomly generates only after a client request is received at the server side gives better performance as compared with previous techniques

Privacy-Aware Access Protocols for MEC Applications in 5G

Multi-access edge computing (MEC) is one of the emerging key technologies in fifth generation (5G) mobile networks, providing reduced end-to-end latency for applications and reduced load in the transport network. This paper proposes mechanisms to enhance user privacy in MEC within 5G. We consider a basic MEC usage scenario, where the user accesses an application hosted in the MEC platform via the radio access network of the mobile network operator (MNO). First, we create a system model based on this scenario. Second, we define the adversary model and give the list of privacy requirements for this system model. We also analyze the impact on user privacy when some of the parties in our model share information that is not strictly needed for providing the service. Third, we introduce a privacy-aware access protocol for the system model and analyze this protocol against the privacy requirements

Privacy-Preserving Access for Multi-Access Edge Computing (MEC) Applications

Multi-Access Edge Computing (MEC) is one of the emerging key technologies in Fifth Generation (5G) Mobile Networks, providing reduced end-to-end latency for applications and reduced load in the transport network. This paper is about user privacy in MEC within 5G. We consider a basic MEC usage scenario, where the user accesses an application hosted in the MEC platform via the radio access network of the Mobile Network Operator (MNO). First, we create a system model based on this scenario, then define the adversary model and privacy requirements for this system model. Second, we introduce a privacy-preserving access solution for the system model and analyze the solution against the privacy requirements.Peer reviewe

Packet Resonance Strategy: A Spoof Attack Detection and Prevention Mechanism in Cloud Computing Environment

Distributed Denial of Service (DDoS) is a major threat to server availability. The attackers hide from view by impersonating their IP addresses as the legitimate users. This Spoofed IP helps the attacker to pass through the authentication phase and to launch the attack. Surviving spoof detection techniques could not resolve different styles of attacks. Packet Resonance Strategy (PRS) armed to detect various types of spoof attacks that destruct the server resources or data theft at Datacenter. PRS ensembles to any Cloud Service Provider (CSP) as they are exclusively responsible for any data leakage and sensitive information hack. PRS uses two-level detection scheme, allows the clients to access Datacenter only when they surpass initial authentication at both levels. PRS provides faster data transmission and time sensitiveness of cloud computing tasks to the authenticated clients. Experimental results proved that the proposed methodology is a better light-weight solution and deployable at server-end

Foundations, Properties, and Security Applications of Puzzles: A Survey

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay---though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this paper, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.Comment: This article has been accepted for publication in ACM Computing Survey

DNA-based client puzzle for WLAN association protocol against connection request flooding

In recent past, Wireless Local Area Network (WLAN) has become more popular because of its flexibility. However, WLANs are subjected to different types of vulnerabilities. To strengthen WLAN security, many high security protocols have been developed. But those solutions are found to be ineffective in preventing Denial of Service (DoS) attacks. A ‘Connection Request Flooding’ DoS (CRF-DoS) attack is launched when an access point (AP) encounters a sudden explosion of connection requests. Among other existing anti CRF-DoS methods, a client puzzle protocol has been noted as a promising and secure potential solution. Nonetheless, so far none of the proposed puzzles satisfy the security requirement of resource-limited and highly heterogeneous WLANs. The CPU disparity, imposing unbearable loads on legitimate users, inefficient puzzle generation and verification algorithms; the susceptibility of puzzle to secondary attacks on legitimate users by embedding fake puzzle parameters; and a notable delay in modifying the puzzle difficulty – these are some drawbacks of currently existing puzzles. To deal with such problems, a secure model of puzzle based on DNA and queuing theory is proposed, which eliminates the above defects while satisfying the Chen puzzle security model. The proposed puzzle (OROD puzzle) is a multifaceted technology that incorporates five main components include DoS detector, queue manager, puzzle generation, puzzle verification, and puzzle solver. To test and evaluate the security and performance, OROD puzzle is developed and implemented in real-world environment. The experimental results showed that the solution verification time of OROD puzzle is up to 289, 160, 9, 3.2, and 2.3 times faster than the Karame-Capkun puzzle, the Rivest time-lock puzzle, the Rangasamy puzzle, the Kuppusamy DLPuz puzzle, and Chen's efficient hash-based puzzle respectively. The results also showed a substantial reduction in puzzle generation time, making the OROD puzzle from 3.7 to 24 times faster than the above puzzles. Moreover, by asking to solve an easy and cost-effective puzzle in OROD puzzle, legitimate users do not suffer from resource exhaustion during puzzle solving, even when under severe DoS attack (high puzzle difficulty)

Efficient Latent Semantic Extraction from Cross Domain Data with Declarative Language

With large amounts of data continuously generated by intelligence devices, efficient analysis of huge data collections to unearth valuable insights has become one of the most elusive challenges for both academia and industry. The key elements to establishing a scalable analyzing framework should involve (1) an intuitive interface to describe the desired outcome, (2) a well-crafted model that integrates all available information sources to derive the optimal outcome and (3) an efficient algorithm that performs the data integration and extraction within a reasonable amount of time. In this dissertation, we address these challenges by proposing (1) a cross-language interface for a succinct expression of recursive queries, (2) a domain specific neural network model that can incorporate information of multiple modalities, and (3) a sample efficient training method that can be used even for extremely-large output-class classifiers. Our contributions in this thesis are thus threefold: First, for the ubiquitous recursive queries in advanced data analytics, on top of BigDatalog and Apache Spark, we design a succinct and expressive analytics tool encapsulating the functionality and classical algorithms of Datalog, a quintessential logic programming language. We provide the Logical Library (LLib), a Spark MLlib-like high-level API supporting a wide range of recursive algorithms and the Logical DataFrame (LFrame), an extension to Spark DataFrame supporting both relational and logical operations. The LLib and LFrame enable smooth collaborations between logical applications and other Spark libraries and cross-language logical programming in Scala, Java, or Python. Second, we utilize variants of recurrent neural network (RNN) to incorporate some enlightening sequential information overlooked by the conventional works in two different domains including Spoken Language Understanding (SLU) and Internet Embedding (IE). In SLU, we address the problem caused by solely relying on the first best interpretation (hypothesis) of an audio command through a series of new architectures comprising bidirectional LSTM and pooling layers to jointly utilize the other hypotheses' texts or embedding vectors, which are neglected but with valuable information missed by the first best hypothesis. In IE, we propose the DIP, an extension of RNN, to build up the internet coordinate system with the IP address sequences, which are also unnoticed in conventional distance-based internet embedding algorithms but encode structural information of the network. Both DIP and the integration of all hypotheses bring significant performance improvements for the corresponding downstream tasks. Finally, we investigate the training algorithm for multi-class classifiers with a large output-class size, which is common in deep neural networks and typically implemented as a softmax final layer with one output neuron per each class. To avoid expensive computing the intractable normalizing constant of softmax for each training data point, we analyze the well-known negative sampling and improve it to the amplified negative sampling algorithm, which gains much higher performance with lower training cost