Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works

Elliptic curve and pseudo-inverse matrix based cryptosystem for wireless sensor networks

Applying asymmetric key security to wireless sensor network (WSN) has been challenging task for the researcher of this field. One common trade-off is that asymmetric key architecture does provide good enough security than symmetric key but on the other hand, sensor network has some resource limitations to implement asymmetric key approach. Elliptic curve cryptography (ECC) has significant advantages than other asymmetric key system like RSA, D-H etc. The most important feature of ECC is that it has much less bit requirement and at the same time, ensures better security compared to others. Hence, ECC can be a better option for implementing asymmetric key approach for sensor network. We propose a new cryptosystem which is based on Pseudo-inverse matrix and Elliptic Curve Cryptography. We establish a relationship between these two different concepts and evaluate our proposed system on the basis of the results of similar works as well as our own simulation done in TinyOS environment

S-PkSec: an asymmetric key based security management scheme for sensor network operation

This paper proposes a public key based management scheme for secure sensor network operation namely S-PkSec (Public Key Based Security for Sensor Networks) and emphasizes detailed comparison with some similar type of schemes. Although there was a wide held belief of the incompatibility of public key cryptographic (PKC) schemes for wireless sensor networks (WSNs), some recent works have shown that, PKC or asymmetric key based schemes could be implemented for such networks in some ways. The major challenge of employing a PKC scheme in sensor network is posed by the limitations of resources of the tiny sensors. Considering this feature of the sensors, we enhance our previous work [1] with some effective comparisons and energy analysis with other two established asymmetric key based protocols. S-PkSec comprises basically of two parts; a key handshaking scheme based on simple linear operations and the derivation of decryption key by a receiver node. S-PkSec allows both base-station-to-node or node-to-base-station secure communications, and node-to-node secure communications. Analysis and simulation results show that, our proposed architecture ensures a good level of security for communications in the network and could effectively be implemented using the limited computation, memory and energy budgets of the current generation sensor nodes

A rule-based machine learning model for financial fraud detection

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively

An Efficient PKC-Based Security Architecture for Wireless Sensor Networks

In spite of previous widely held belief of the incompatibility of public key cryptography (PKC) schemes for wireless sensor networks (WSNs), some recent works have shown that, PKC based schemes could be implemented for such networks in some ways. The major challenge of employing a PKC scheme in wireless sensor network is posed by the limitations of resources of the tiny sensors. Considering this feature of the sensors, in this paper, we propose an efficient PKC based security architecture with relatively less resource requirements than those of the other previously proposed PKC schemes for WSN. Our security architecture comprises basically of two parts; a key handshaking scheme based on simple linear operations and the derivation of decryption key by a receiver node. Our architecture allows both base-station-to-node or node-to-base-station secure communications, and node-to-node secure communications. Analysis and simulation results show that, our proposed architecture ensures a good level of security for communications in the network and could effectively be implemented using the limited computation, memory and energy budgets of the current generation sensor nodes.Comment: 7 page

An asymmetric key-based security architecture for wireless sensor networks

In spite of previous common assumptions about the incompatibility of public key cryptography (PKC) schemes with wireless sensor networks (WSNs), recent works have shown that they can be utilized for such networks in some manner. The major challenge of employing a PKC-based scheme in a wireless sensor network is posed by the resource limitations of the tiny sensors. Considering this sensor feature, in this paper we propose an efficient PKC-based security architecture with relatively lower resource requirements than those of previously proposed PKC schemes for WSN. In addition, our scheme aims to provide robust security in the network. Our security architecture comprises two basic components; a key handshaking scheme based on simple, linear operations and the derivation of a decryption key by a receiver node. Our architecture enables node-to-base-station and node-to-node secure communications. Analysis and simulation results show that our proposed architecture ensures a good level of security for network communications, and can be effectively implemented with the limited computational, memory, and energy budgets of current-generation sensor nodes

Analysing recursive preprocessing of BKZ lattice reduction algorithm

Lattice problems are considered as the key elements in many areas of computer science as well as in cryptography; the most important of which is the shortest vector problem and its approximate variants. Algorithms for this problem are known as lattice reduction algorithms. Currently, the most practical lattice reduction algorithm for such problems is the block Korkine–Zolotarev (BKZ) algorithm and its variants. The authors optimise both the pruning and the preprocessing parameters of the recursive (aborted, extreme pruned) preprocessing of the BKZ lattice reduction algorithm and improve the results from Asiacrypt'11 by Chen and Nguyen. The authors derive approximate closed-form complexity formulas (based on the sandpile model assumption model by Hanrot et al.) for the enumeration time which allow a simple estimation of complexity without running the simulation algorithm (by Chen and Nguyen) and asymptotically suggests a modified extreme pruning bounding profiles with different parameters. Hence, the authors’ contributions are in optimising and improving the analysis of the complexity upper bound estimates presented by Chen and Nguyen, based on the same recursive-BKZ preprocessing model

Preprocessing optimisation: revisiting recursive-BKZ lattice reduction algorithm

Preprocessing is applied to certain lattice reduction algorithms such as block Korkine–Zolotarev (BKZ) variants to reduce the search time in the enumeration tree for a shortest vector. The most classical form of preprocessing the authors observe is with polynomial time Lenstra–Lenstra–Lovász algorithm to work with a slow enumeration-based algorithm like BKZ. The trade-off between the preprocessing and the enumeration stages in the context of time complexity of the whole algorithm is not well studied and explored. The main goal of this study is to re-investigate the preprocessing approach presented by Chen and Nguyen and improve its performance through optimisation. They extend the numeral results published by Haque et al. in IET Inf. Secur. for larger block sizes and report a comparison

Solving blockchain trilemma using off‐chain storage protocol

Abstract Trilemma in blockchain refers to the infamous problem of simultaneously not delivering the three critical aspects of a ledger: security, scalability, and decentralisation. While security and scalability hinder decentralisation, security is jeopardised if the scalability is escalated. This deficiency of not maintaining a balance among these three crucial factors restricts the broader adoption of blockchain technology and cryptocurrencies in the industries. This paper proposes a solution to the blockchain trilemma by implementing a public ledger using The InterPlanetary File System (IPFS) and a newly introduced strategy called the double‐chain technique. The scalability and decentralisation features are guaranteed by the distributed file system of IPFS and the public nature of the blockchain suggested in this study. Although any consensus can be plugged into our system, the proof‐of‐work consensus is utilised to ensure that the security is not compromised while stabilising scalability and decentralisation

Predicting tours and probabilistic simulation for BKZ lattice reduction algorithm

We investigate the terminating concept of BKZ reduction first introduced by Hanrot et al. [Crypto'11] and make extensive experiments to predict the number of tours necessary to obtain the best possible trade off between reduction time and quality. Then, we improve Buchmann and Lindner's result [Indocrypt'09] to find sub-lattice collision in SWIFFT. We illustrate that further improvement in time is possible through special setting of SWIFFT parameters and also through the combination of different reduction parameters adaptively. Our contribution also include a probabilistic simulation approach top-up deterministic simulation described by Chen and Nguyen [Asiacrypt'11] that can able to predict the Gram-Schmidt norms more accurately for large block sizes