Computer networks--Security measures.

The Internet and computer networks have become an important part of our
organizations and everyday life. With the increase in our dependence on computers
and communication networks, malicious activities have become increasingly prevalent.
Network attacks are an important problem in today’s communication environments.
The network traffic must be monitored and analyzed to detect malicious activities
and attacks to ensure reliable functionality of the networks and security of users’
information. Recently, machine learning techniques have been applied toward the
detection of network attacks. Machine learning models are able to extract similarities
and patterns in the network traffic. Unlike signature based methods, there is no need
for manual analyses to extract attack patterns. Applying machine learning algorithms
can automatically build predictive models for the detection of network attacks.
This dissertation reports an empirical analysis of the usage of machine learning
methods for the detection of network attacks. For this purpose, we study the detection
of three common attacks in computer networks: SSH brute force, Man In The Middle
(MITM) and application layer Distributed Denial of Service (DDoS) attacks. Using
outdated and non-representative benchmark data, such as the DARPA dataset, in the intrusion detection domain, has caused a practical gap between building detection
models and their actual deployment in a real computer network. To alleviate this
limitation, we collect representative network data from a real production network for
each attack type. Our analysis of each attack includes a detailed study of the usage
of machine learning methods for its detection. This includes the motivation behind
the proposed machine learning based detection approach, the data collection process,
feature engineering, building predictive models and evaluating their performance.
We also investigate the application of feature selection in building detection models
for network attacks. Overall, this dissertation presents a thorough analysis on how
machine learning techniques can be used to detect network attacks. We not only study
a broad range of network attacks, but also study the application of different machine
learning methods including classification, anomaly detection and feature selection for
their detection at the host level and the network level.

The SSL/TLS is the main protocol used to provide secure data connection between a
client and a server. The main concern of using this protocol is to avoid the secure
connection from being breached. Computer systems and their applications are becoming
more complex and keeping these secure connections between all the connected components
is a challenge.
To avoid any new security flaws and protocol connections weaknesses, the SSL/TLS
protocol is always releasing newer versions after discovering security bugs and
vulnerabilities in any of its previous version. We have described some of the common
security flaws in the SSL/TLS protocol by identifying them in the literature and then by
analyzing the activities from each of their use cases to find any possible threats. These
threats are realized in the form of misuse cases to understand how an attack happens from
the point of the attacker. This approach implies the development of some security patterns
which will be added as a reference for designing secure systems using the SSL/TLS protocol. We finally evaluate its security level by using misuse patterns and considering
the threat coverage of the models.

High processing time and implementation complexity of the fully homomorphic
encryption schemes intrigued cryptographers to extend partially homomorphic
encryption schemes to allow homomorphic computation for larger classes of polynomials.
In this thesis, we study several public key and partially homomorphic schemes
and discuss a recent technique for boosting linearly homomorphic encryption schemes.
Further, we implement this boosting technique on CGS linearly homomorphic encryption
scheme to allow one single multiplication as well as arbitrary number of additions
on encrypted plaintexts. We provide MAGMA source codes for the implementation
of the CGS scheme along with the boosted CGS scheme.