Biblio

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

Risk assessment of cyber-physical systems, such as power plants, connected devices and IT-infrastructures has always been challenging: safety (i.e., absence of unintentional failures) and security (i. e., no disruptions due to attackers) are conditions that must be guaranteed. One of the traditional tools used to help considering these problems is attack trees, a tree-based formalism inspired by fault trees, a well-known formalism used in safety engineering. In this paper we define and implement the translation of attack-fault trees (AFTs) to a new extension of timed automata, called parametric weighted timed automata. This allows us to parametrize constants such as time and discrete costs in an AFT and then, using the model-checker IMITATOR, to compute the set of parameter values such that a successful attack is possible. Using the different sets of parameter values computed, different attack and fault scenarios can be deduced depending on the budget, time or computation power of the attacker, providing helpful data to select the most efficient counter-measure.

The rapid growth of the connected devices driven by Internet of Things (IoT) concept requires a complete rethinking of the conventional approaches for the network design. One of the key constraints of the IoT devices are their low capabilities in order to optimize energy consumption. On the other hand, many IoT applications require high level of data protection and privacy, which can be provided only by advanced cryptographic algorithms, which are not feasible for IoT devices. In this paper, we propose a scalable quadrature modulation aiming to solve the problem of secure communications at the physical layer. The key idea of the proposed approach is to transmit only part of information in way that allows target receiver to retrieve the complete information. Such approach allows to ensure the security of wireless channel, while reducing the overhead of advanced cryptographic algorithms.

IoT Network Monitor is an intuitive and user-friendly interface for consumers to visualize vulnerabilities of IoT devices in their home. Running on a Raspberry Pi configured as a router, the IoT Network Monitor analyzes the traffic of connected devices in three ways. First, it detects devices with default passwords exploited by previous attacks such as the Mirai Botnet, changes default device passwords to randomly generated 12 character strings, and reports the new passwords to the user. Second, it conducts deep packet analysis on the network data from each device and notifies the user of potentially sensitive personal information that is being transmitted in cleartext. Lastly, it detects botnet traffic originating from an IoT device connected to the network and instructs the user to disconnect the device if it has been hacked. The user-friendly IoT Network Monitor will enable homeowners to maintain the security of their home network and better understand what actions are appropriate when a certain security vulnerability is detected. Wide adoption of this tool will make consumer home IoT networks more secure.