Among all the new developed cryptocurrencies from Bitcoin, Zcash comes out to be the strongest cryptocurrency providing both transparency and anonymity to the transactions and its users by deploying the strong mathematics of zk-SNARKs. We discussed the zero knowledge proofs which is a basic building block for providing the functionality to zk-SNARKs. It offers schnorr and sigma protocols with interactive and noninteractive versions. Non-interactive proofs are further used in Zcash transactions where the validation of sent transaction is proved by cryptographic proof. Further, we deploy zk-SNARKs proofs following common reference string as public parameter when transaction is made. The proof allows sender to prove that she knows a secret for an instance such that the proof is succinct, can be verified very efficiently and does not leak the secret. Non-malleability, small proofs and very effective verification make zk-SNARKs a classic tool in Zcash. Since we deal with NP problems therefore we have considered the elliptic curve cryptography to provide the same security like RSA but with smaller parameter size. Lastly, we explain Zcash transaction process after minting the coin, the corresponding transaction completely hides the sender, receiver and amount of transaction using zero knowledge proof. As future considerations, we talk about the improvements that can be done in term of decentralization, efficiency by comparing with top ranked cryptocurrencies namely Ethereum and Monero, privacy preserving against the thread of quantum computers and enhancements in shielded transactions.

In the practice of software engineering, project managers often face the problem of software project management. It is related to resource constrained project scheduling problem. In software project scheduling, main resources are considered to be the employees with some skill set and required amount of salary. The main purpose of software project scheduling is to assign tasks of a project to the available employees such that the total cost and duration of the project are minimized, while keeping in check that the constraints of software project scheduling are fulfilled. Software project scheduling (SPSP) has complex combined optimization issues and its search space increases exponentially when number of tasks and employees are increased, this makes software project scheduling problem (SPSP) a NP-Hard problem. The goal of software project scheduling problem is to minimize total cost and duration of project which makes it multi-objective problem. Many algorithms are proposed up till now that claim to give near optimal results for NP-Hard problems, but only few are there that gives feasible set of solutions for software project scheduling problem, but still we want to get more efficient algorithm to get feasible and efficient results. Nowadays, most of the problems are being solved by using nature inspired algorithms because these algorithms provide the behavior of exploration and exploitation. For solving software project scheduling (SPSP) some of these nature inspired algorithms have been used e.g. genetic algorithms, Ant Colony Optimization algorithm (ACO), Firefly etc. Nature inspired algorithms like particle swarm optimization, genetic algorithms and Ant Colony Optimization algorithm provides more promising result than naive and greedy algorithms. However there is always a quest and room for more improvement. The main purpose of this research is to use bat algorithm to get efficient results and solutions for software project scheduling problem. In this work modified bat algorithm is implemented where a different approach of random walk is used. The contributions of this thesis are to: (1) To adapt and apply modified multi-objective bat algorithm for solving software project scheduling (SPSP) efficiently, (2) to adapt and apply other nature inspired algorithms like genetic algorithms for solving software project scheduling (SPSP) and (3) to compare and analyze the results obtained by applied nature inspired algorithms and provide the conclusion.

This thesis deals with the development of a methodology / concept to analyse targeted attacks against IIoT / IoT devices. Building on the established background knowledge about honeypots, fileless malware and injection techniques a methodology is created that leads to a concept of a honeypot analyzation system. The system is created to analyse and detect novel threats like fileless attacks which are often utilized by Advanced Persistent Threats. That system is partially implemented and later evaluated by performing a simulated attack utilizing fileless attacks. The effectiveness is discussed and rated based on the results.

Neural networks have become one of the most powerful algorithms when it comes to learning from big data sets and it is used extensively for classification. But the deeper the network models, the lesser is the interpretability of such models. Although many methods exist to explain the output of such networks, the lack of interpretability makes them black boxes. On the other hand, prototype-based machine learning algorithms are known to be interpretable and robust. Therefore, the aim of this thesis is to find a way to interpret the functioning of the neural networks by introducing a prototype layer to the neural network architecture. This prototype layer will train alongside the neural network and help us interpret the model. We present architectures of neural networks consisting of autoencoders and prototypes that perform activity recognition from heart rates extracted from ECG signals. These prototypes represent the different activity groups that the heart rates belong to and thereby aid in interpretability.

After creating a new blockchain transaction, the next step usually is to make miners aware of it by having it propagated through the blockchain’s peer-to-peer network. We study an unintended alternative to peer-to-peer propagation: Exclusive mining. Exclusive mining is a type of collusion between a transaction initiator and a single miner (or mining pool). The initiator sends transactions through a private channel directly to the miner instead of propagating them through the peerto-peer network. Other blockchain users only become aware of these transactions once they have been included in a block by the miner. We identify three possible motivations for engaging in exclusive mining: (i) reducing transaction cost volatility (“confirmation as a service”), (ii) hiding unconfirmed transactions from the network to prevent frontrunning and (iii) camouflaging wealth transfers as transaction costs to evade taxes or launder money. We further outline why exclusive mining is difficult to prevent and introduce metrics which can be used to identify mining pools engaging in exclusive mining activity.