Conference:
31st International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2023)
16-18. October 2023
Stony Brook University, Stony Brook, NY, USA

Authors:
Kuaban G, Gelenbe E, Czachórski T, Czekalski P.

Abstract:

There is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers). We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period.

Journal:
ACM Ubiquity, Volume: 2023, Issue: August

Authors:
Gelenbe E.

Abstract:

This paper considers key issues surrounding energy consumption by information and communication technologies (ICT), which has been steadily growing and is now attaining approximately 10% of the worldwide electricity consumption with a significant impact on greenhouse gas emissions. The perimeter of ICT systems is discussed, and the role of the subsystems that compose ICT is considered.
Data from recent years is used to understand how each of these sub-systems contribute to ICT’s energy consumption. The quantitatively demonstrated positive correlation between the penetration of ICT in the world’s different economies and the same economies’ contributions to undesirable greenhouse gas emissions is also discussed. We also examine how emerging technologies such as 5G, edge computing, and cryptocurrencies are contributing to the worldwide increase in electricity consumption by ICT, despite the increases in ICT efficiency, in terms of energy consumed per bit processed, stored, or transmitted. The measurement of specific ICT systems’ electricity consumption is also addressed, and the manner in which this consumption can be minimized in a specific edge-computing context is discussed.

Journal:
Information and Software Technology, Vol. 164, 2023.

Authors:
Kalouptsoglou I, Siavvas M, Ampatzoglou A, Kehagias D, Chatzigeorgiou A.

Abstract:
Context: Software security is considered a major aspect of software quality as the number of discovered vulnerabilities in software products is growing. Vulnerability prediction is a mechanism that helps engineers to prioritize their inspection efforts focusing on vulnerable parts. Despite the recent advancements, current literature lacks a systematic mapping study on vulnerability prediction.
Objective: This paper aims to analyze the state-of-the-art of vulnerability  prediction focusing on: (a) the goals of vulnerability prediction-related studies; (b) the data collection processes and the types of datasets that exist in the literature; (c) the mostly examined techniques for the construction of the prediction models and their input features; and (d) the utilized evaluation techniques.
Method: We collected 180 primary studies following a broad search methodology across four popular digital libraries. We mapped these studies to the variables of interest and we identified trends and relationships between the studies.
Results: The main findings suggest that: (i) there are two major study types, prediction of vulnerable software components and forecasting of the evolution of vulnerabilities in software; (ii) most studies construct their own vulnerability-related dataset retrieving information from vulnerability databases for real-world software; (iii) there is a growing interest for deep learning models along with a trend on textual source code representation; and (iv) F1-score was found to be the most widely used evaluation metric.
Conclusions: The results of our study indicate that there are several open challenges in the domain of vulnerability prediction. One of the major conclusions, is the fact that most studies focus on within-project prediction, neglecting the real-world scenario of cross-project prediction.

Journal:
Sensors 2023, 23(13)

Authors:
Kuaban G, Gelenbe E, Czachórski T, Czekalski P, Tangka J.

Abstract:

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device.
We also introduced an energy threshold after which the device’s battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.

Journal:
IEEE Access 2023. Vol. 11. 2023

Authors:
Gelenbe E, Nakip M.

Abstract:

This paper proposes a method to assess the security of an n device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called the Associated RNN (ARNN). For each of the n devices or IP addresses in the IoT network, two distinct neurons of the ARNN advocate opposite views: compromised or not compromised. The fully interconnected 2n neuron ARNN structure of paired neurons learns offline from ground truth data. Thus rather than requiring a separate attack detector at each network node, the ARNN offers a single overall attack detector that observes the incoming traffic at each node, learns about the interdependencies between network nodes, and formulates a recommendation for each device or IP address in an IoT network. The ARNN weight initialization and learning algorithm are discussed, and the ARNN performance is evaluated using real attack data, and compared against several learning and testing techniques. Results are obtained both for off-line learning with ground truth data, and for on-line incremental learning using a simplified average metric measured from incoming packet traffic. Comparisons with the best state-of-the-art techniques show that the ARNN significantly outperforms previously known approaches.

Journal:
IEEE Access 2023. Vol. 11. 2023

Authors:
Szántó M, Hidalgo C, González L, Pérez J, Asua E, and Vajta L.

Abstract:
The development of connected and automated vehicles (CAVs) presents a great opportunity to extend the current range of vehicle vision, by gathering information outside of its sensors. Two main sources could be aggregated for this extended perception; vehicles making use of vehicle-to-vehicle communication (V2V), and infrastructure using vehicle-to-infrastructure communication (V2I). In this paper, we focus on the infrastructure side and make the case for low-latency obstacle mapping using V2I communication. A map management framework is proposed, which allows vehicles to broadcast and subscribe to traffic information-related messages using the Message Queuing Telemetry Transport (MQTT) protocol. This framework makes use of our novel candidate/employed map (C/EM) model for the real-time updating of obstacles broadcast by individual vehicles. This solution has been implemented and tested using a scenario that contains real and simulated CAVs tasked with doing lane change and braking maneuvers. As a result, the simulated vehicle can optimize its trajectory planning based on information which could not be observed by its sensor suite but is instead received from the presented map-management module, while remaining capable of performing the maneuvers in an automated manner.

Conference:
The 48th IEEE Conference on Local Computer Networks 2023 (IEEE  LCN 2023), 2-5 October 2023, Daytona Beach, Florida, USA.

Authors:
Nasereddin M, Nakip M, Gelenbe E.

Abstract:
The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network test-bed  is used to evaluate the impact of UDP Flood Attacks on an IoT Server, whose first line of defence is an accurate IDS. We show that attacks overload the multi-core Server and paralyze its IDS. Thus a mitigation scheme that detects attacks rapidly, and drops packets within milli-seconds after the attack begins, is proposed and experimentally evaluated.

Conference:
IEEE International Symposium on Local and Metropolitan Area Networks 2023 (IEEE  LANMAN 2023), 10-11 July 2023, London, UK.

Authors:
Gelenbe E, Nakip M.

Abstract:
This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network. Some of these algorithms require offline learning, while others allow the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all these methods is carried out with real attack traces. The novel methods presented here are compared with other state-of-the-art approaches, showing that they offer better or equal performance, with lower learning times and shorter detection times, as compared to the existing state-of-the-art approaches.

Journal:
IEEE Access 2022. Vol. 10.

Authors:
Gelenbe E, Nakip M.

Abstract:
A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.

Journal:
Electronics 2022, 11(18), 2820

Authors:
Kalouptsoglou I, Tsoukalas D, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A.

Abstract:
Software security is a critical aspect of modern software products. The vulnerabilities that reside in their source code could become a major weakness for enterprises that build or utilize these products, as their exploitation could lead to devastating financial consequences. Therefore, the development of mechanisms capable of identifying and discovering software vulnerabilities
has recently attracted the interest of the research community. Besides the studies that examine software attributes in order to predict the existence of vulnerabilities in software components, there are also studies that attempt to predict the future number of vulnerabilities based on the already reported vulnerabilities of a project. In this paper, the evolution of vulnerabilities in a horizon of up to 24 months ahead is predicted using a univariate time series forecasting approach. Both statistical and deep learning models are developed and compared based on security data coming from five popular software projects. In contrast to related literature, the results indicate that the capacity of Deep Learning and statistical models in forecasting the evolution of software vulnerabilities, as well as the selection of the best-performing model, depends on the respective software project. In some cases, statistical models provided better accuracy, whereas in other cases, Deep Learning models demonstrated better predictive power. However, the difference in their performance was not found to be statistically significant. In general, the two model categories produced similar forecasts for the number of vulnerabilities expected in the future, without significant diversities.