ENSURING SPECIFICATION COMPLIANCE, ROBUSTNESS, AND SECURITY OF WIRELESS NETWORK PROTOCOLS

Several newly emerged wireless technologies (e.g., Internet-of-Things, Bluetooth, NFC)—extensively backed by the tech industry—are being widely adopted and have resulted in a proliferation of diverse smart appliances and gadgets (e.g., smart thermostat, wearables, smartphones), which has ensuingly shaped our modern digital life. These technologies include several communication protocols that usually have stringent requirements stated in their specifications. Failing to comply with such requirements can result in incorrect behaviors, interoperability issues, or even security vulnerabilities. Moreover, lack of robustness of the protocol implementation to malicious attacks—exploiting subtle vulnerabilities in the implementation—mounted by the compromised nodes in an adversarial environment can limit the practical utility of the implementation by impairing the performance of the protocol and can even have detrimental effects on the availability of the network. Even having a compliant and robust implementation alone may not suffice in many cases because these technologies often expose new attack surfaces as well as new propagation vectors, which can be exploited by unprecedented malware and can quickly lead to an epidemic

Emergence in the security of protocols for mobile ad-hoc networks

This thesis is concerned with the study of secure wireless routing protocols, which have been deployed for the purpose of exchanging information in an adhoc networking enviromnent. A discrete event simulator is developed, utilising an adaptive systems modelling approach and emergence that aims to assess networking protocols in the presence of adversarial behaviour. The model is used in conjunction with the characteristics that routing protocols have and also a number of cryptographic primitives that can be deployed in order to safeguard the information being exchanged. It is shown that both adversarial behaviour, as well as protocol descriptions can be described in a way that allows for them to be treated as input on the machine level. Within the system, the output generated selects the fittest protocol design capable of withstanding one or more particular type of attacks. As a result, a number of new and improved protocol specifications are presented and benchmarked against conventional metrics, such as throughput, latency and delivery criteria. From this process, an architecture for designing wireless routing protocols based on a number of security criteria is presented, whereupon the decision of using particular characteristics in a specification has been passed onto the machine level

On the difficulty of hiding the balance of lightning network channels

International audienceThe Lightning Network is a second layer technology running on top of Bitcoin and other Blockchains. It is composed of a peer-to-peer network, used to transfer raw information data. Some of the links in the peer-to-peer network are identified as payment channels, used to conduct payments between two Lightning Network clients (i.e., the two nodes of the channel). Payment channels are created with a fixed credit amount, the channel capacity. The channel capacity, together with the IP address of the nodes, is published to allow a routing algorithm to find an existing path between two nodes that do not have a direct payment channel. However, to preserve users' privacy, the precise balance of the pair of nodes of a given channel (i.e. the bandwidth of the channel in each direction), is kept secret. Since balances are not announced, second-layer nodes probe routes iteratively, until they find a successful route to the destination for the amount required, if any. This feature makes the routing discovery protocol less efficient but preserves the privacy of channel balances. In this paper, we present an attack to disclose the balance of a channel in the Lightning Network. Our attack is based on performing multiple payments ensuring that none of them is finalized, minimizing the economical cost of the attack. We present experimental results that validate our claims, and countermeasures to handle the attack

On the difficulty of hiding the balance of lightning network channels

The Lightning Network is a second layer technology running on top of Bitcoin and other Blockchains. It is composed of a peer-to-peer network, used to transfer raw information data. Some of the links in the peer-to-peer network are identified as payment channels, used to conduct payments between two Lightning Network clients (i.e., the two nodes of the channel). Payment channels are created with a fixed credit amount, the channel capacity. The channel capacity, together with the IP address of the nodes, is published to allow a routing algorithm to find an existing path between two nodes that do not have a direct payment channel. However, to preserve users' privacy, the precise balance of the pair of nodes of a given channel (i.e. the bandwidth of the channel in each direction), is kept secret. Since balances are not announced, second-layer nodes probe routes iteratively, until they find a successful route to the destination for the amount required, if any. This feature makes the routing discovery protocol less efficient but preserves the privacy of channel balances. In this paper, we present an attack to disclose the balance of a channel in the Lightning Network. Our attack is based on performing multiple payments ensuring that none of them is finalized, minimizing the economical cost of the attack. We present experimental results that validate our claims, and countermeasures to handle the attac

Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems

IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead