Vulnerability-Tolerant Transport Layer Security

SSL/TLS communication channels play a very important role in Internet security, including cloud computing and server infrastructures. There are often concerns about the strength of the encryption mechanisms used in TLS channels. Vulnerabilities can lead to some of the cipher suites once thought to be secure to become insecure and no longer recommended for use or in urgent need of a software update. However, the deprecation/update process is very slow and weeks or months can go by before most web servers and clients are protected, and some servers and clients may never be updated. In the meantime, the communications are at risk of being intercepted and tampered by attackers. In this paper we propose an alternative to TLS to mitigate the problem of secure commu- nication channels being susceptible to attacks due to unexpected vulnerabilities in its mechan- isms. Our solution, called Vulnerability-Tolerant Transport Layer Security (vtTLS), is based on diversity and redundancy of cryptographic mechanisms and certificates to ensure a secure communication even when one or more mechanisms are vulnerable. Our solution relies on a combination of k cipher suites which ensure that even if k ? 1 cipher suites are insecure or vul- nerable, the remaining cipher suite keeps the communication channel secure. The performance and cost of vtTLS were evaluated and compared with OpenSSL, one of the most widely used implementations of TLS

IoT-MQTT based denial of service attack modelling and detection

Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well as the usage of infected IoT devices to launch other cyber attacks. However, as sustained IoT deployment and functionality are heavily reliant on the use of e_ective data communication protocols, the attacks on other layers of IoT architecture are anticipated to increase. In the IoT landscape, the publish/- subscribe based Message Queuing Telemetry Transport (MQTT) protocol is widely popular. Hence, cyber security threats against the MQTT protocol are projected to rise at par with its increasing use by IoT manufacturers. In particular, the Internet exposed MQTT brokers are vulnerable to protocolbased Application Layer Denial of Service (DoS) attacks, which have been known to cause wide spread service disruptions in legacy systems. In this thesis, we propose Application Layer based DoS attacks that target the authentication and authorisation mechanism of the the MQTT protocol. In addition, we also propose an MQTT protocol attack detection framework based on machine learning. Through extensive experiments, we demonstrate the impact of authentication and authorisation DoS attacks on three opensource MQTT brokers. Based on the proposed DoS attack scenarios, an IoT-MQTT attack dataset was generated to evaluate the e_ectiveness of the proposed framework to detect these malicious attacks. The DoS attack evaluation results obtained indicate that such attacks can overwhelm the MQTT brokers resources even when legitimate access to it was denied and resources were restricted. The evaluations also indicate that the proposed DoS attack scenarios can signi_cantly increase the MQTT message delay, especially in QoS2 messages causing heavy tail latencies. In addition, the proposed MQTT features showed high attack detection accuracy compared to simply using TCP based features to detect MQTT based attacks. It was also observed that the protocol _eld size and length based features drastically reduced the false positive rates and hence, are suitable for detecting IoT based attacks

ERINYES: A CONTINUOUS AUTHENTICATION PROTOCOL

The need for user authentication in the digital domain is paramount as the number of digital interactions that involve sensitive data continues to increase. Advances in the fields of machine learning (ML) and biometric encryption have enabled the development of technologies that can provide fully remote continuous user authentication services. This thesis introduces the Erinyes protocol. The protocol leverages state of the art ML models, biometric encryption of asymmetric cryptographic keys, and a trusted third-party client-server architecture to continuously authenticate users through their behavioral biometrics. The goals in developing the protocol were to identify if biometric encryption using keystroke timing and mouse cursor movement sequences were feasible and to measure the performance of a continuous authentication system that utilizes biometric encryption. Our research found that with a combined keystroke and mouse cursor movement dataset, the biometric encryption system can perform with a 0.93% False Acceptance Rate (FAR), 0.00% False Reject Rate (FRR), and 99.07% accuracy. Using a similar dataset, the overall integrated system averaged 0% FAR, 2% FRR and 98% accuracy across multiple users. These metrics demonstrate that the Erinyes protocol can achieve continuous user authentication with minimal user intrusion.Lieutenant, United States NavyLieutenant, United States NavyApproved for public release. Distribution is unlimited

Detecting Impersonation Attacks in a Static WSN

The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks. A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device. The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system