As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, research on network intrusion detection systems (NIDSs) has been actively conducted. Among the various NIDS technologies, recent interest is focused on artificial intelligence (AI)-based anomaly detection systems, and various models have been proposed to improve the performance of NIDS. However, there still exists the problem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accurately. In this study, we propose a novel AI-based NIDS that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforementioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic. In particular, we focused on the reconstruction error and Wasserstein distance-based generative adversarial networks, and autoencoder-driven deep learning models. To demonstrate the effectiveness of our system, we performed comprehensive evaluations over various data sets and demonstrated that the proposed systems significantly outperformed the previous AI-based NIDS.

Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.

Over the recent years, cloud storage services have become increasingly popular, where users can outsource data and access the outsourced data anywhere, anytime. Accordingly, the data in the cloud is growing explosively. Among the outsourced data, most of them are duplicated. Cloud storage service providers can save huge amounts of resources via client-side deduplication. On the other hand, for safe outsourcing, clients who use the cloud storage service desire data integrity and confidentiality of the outsourced data. However, ensuring confidentiality and integrity in the cloud storage environment can be difficult. Recently, in order to achieve integrity with deduplication, the notion of deduplicatable proof of storage has emerged, and various schemes have been proposed. However, previous schemes are still inefficient and insecure. In this paper, we propose a symmetric key based deduplicatable proof of storage scheme, which ensures confidentiality with dictionary attack resilience and supports integrity auditing based on symmetric key cryptography. In our proposal, we introduce a bit-level challenge in a deduplicatable proof of storage protocol to minimize data access. In addition, we prove the security of our proposal in the random oracle model with information theory. Implementation results show that our scheme has the best performance.

Multiplication schemes based on Toeplitz matrix-vector product (TMVP) have been proposed by many researchers. TMVP can be computed using the recursive two-way and three-way split methods, which are composed of four blocks. Among them, we improve the space complexity of the component matrix formation (CMF) block. This result derives the improvements of multiplication schemes based on TMVP. Also, we present a subquadratic space complexity$GF(2^m)$multiplier with even type Gaussian normal basis (GNB). In order to design the multiplier, we formulate field multiplication as a sum of two TMVPs and efficiently compute the sum. As a result, for type 2 and 4 GNBs, the proposed multipliers outperform other similar schemes. The proposed type 6 GNB is the first subquadrtic space complexity multiplier with its explicit complexity formula.

In this paper, we present new parallel polynomial multiplication formulas which result in subquadratic space complexity. The schemes are based on a recently proposed block recombination of polynomial multiplication formula. The proposed two-way, three-way, and four-way split polynomial multiplication formulas achieve the smallest space complexities. Moreover, by providing area-time tradeoff method, the proposed formulas enable one to choose a parallel formula for polynomial multiplication which is suited for a design environment.

We propose block decompositions for the Toeplitz matrix-vector product (TMVP) using the$k$-way splitting method presented in the above paper. As a result, we show that the space complexity for TMVP can be improved.

In the above paper, Akleylekproposed an efficient multiplication algorithm for polynomials in Chebyshev form. In this comment, we show that a recombination of the above proposed algorithm induces more efficient algorithm for the multiplications of polynomials in Chebyshev form.

Due to recent advanced technology in the field of HDD, forensic investigators and analysts are dealing with terabyte data sets and spending tremendous time and effort in forensic investigations. It makes "Speed" one of the hot issues in digital forensics. To get speed up or to improve efficiency, some approaches have been proposed. One of them getting attention is a hardware-based approach. However, such a way is limitedly used in the field of evidence cloning or password cracking while rarely applied in search and analysis for the digital evidence. A general approach to the forensic search is to find specific text strings by comparing every byte of the digital evidence at the physical level. Besides, alternative approaches have been proposed for speedup of search and analysis process. They are usually based on the technologies such like indexing algorithms, distributed processing, and data mining. However, these methods have some drawbacks. Some require a lot of initial time for preprocessing, others are impractical. In order to solve this problem, we have already proposed an efficient and practical approach for forensic analysis in. In this paper, we present the system architecture and show feasibility and scalability of our approach by comparing its performance to those of a popular forensic tool currently on the market.

In this paper, we present a bit-parallel multiplier for GF(2^m) defined by an irreducible pentanomial x^m+x^{k_3}+x^{k_2}+x^{k_1}+1, where 1\leq k_1 < k_2

This paper presents a new bit-parallel multiplier for the finite field GF(2/sup m/) defined by an irreducible all-one polynomial. In order to reduce the complexity of the multiplier, we introduce a redundant representation and use the well-known multiplication method proposed by Karatsuba. The main idea is to combine the redundant representation and the Karatsuba method to design an efficient bit-parallel multiplier. As a result, the proposed multiplier requires about 25 percent fewer AND/XOR gates than the previously proposed multipliers using an all-one polynomial, while it has almost the same time delay as the previously proposed ones.