This page has only limited features, please log in for full access.

Unclaimed
Francesco Palmieri
Department of Computer Science University of Salerno Fisciano (SA) Italy

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

Francesco Palmieri received his M.S. “Laurea” Degree in Information Sciences, M.S. “Laurea” Degree in Computer Science, and Ph.D in Computer Science from the University of Salerno, Italy. He also achieved his National Professional Engineer Certification at the University of Naples Federico II. He is currently a full professor of computer science at the Department of Computer Science, University of Salerno, Italy. He has been involved in several national and international research and network development projects and worked as a network and security consultant for many large and important academic, government, and public organizations in the south of Italy. He has published a significant number of papers in leading technical journals, books, and conferences and currently serves as the editor-in-chief of an international journal and is part of the editorial board of several other journals. His major research interests concern high performance networking protocols and architectures, routing algorithms, and network security.

Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Research article
Published: 26 July 2021 in International Journal of Intelligent Systems
Reads 0
Downloads 0

Modern interconnected power grids are a critical target of many kinds of cyber-attacks, potentially affecting public safety and introducing significant economic damages. In such a scenario, more effective detection and early alerting tools are needed. This study introduces a novel anomaly detection architecture, empowered by modern machine learning techniques and specifically targeted for power control systems. It is based on stacked deep neural networks, which have proven to be capable to timely identify and classify attacks, by autonomously eliciting knowledge about them. The proposed architecture leverages automatically extracted spatial and temporal dependency relations to mine meaningful insights from data coming from the target power systems, that can be used as new features for classifying attacks. It has proven to achieve very high performance when applied to real scenarios by outperforming state-of-the-art available approaches.

ACS Style

Gianni D'Angelo; Francesco Palmieri. A stacked autoencoder‐based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems. International Journal of Intelligent Systems 2021, 1 .

AMA Style

Gianni D'Angelo, Francesco Palmieri. A stacked autoencoder‐based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems. International Journal of Intelligent Systems. 2021; ():1.

Chicago/Turabian Style

Gianni D'Angelo; Francesco Palmieri. 2021. "A stacked autoencoder‐based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems." International Journal of Intelligent Systems , no. : 1.

Editorial
Published: 23 July 2021 in Concurrency and Computation: Practice and Experience
Reads 0
Downloads 0
ACS Style

Marek R. Ogiela; Wenny Rahayu; Francesco Palmieri. Human oriented solutions for intelligent analysis, multimedia and communication systems. Concurrency and Computation: Practice and Experience 2021, e6532 .

AMA Style

Marek R. Ogiela, Wenny Rahayu, Francesco Palmieri. Human oriented solutions for intelligent analysis, multimedia and communication systems. Concurrency and Computation: Practice and Experience. 2021; ():e6532.

Chicago/Turabian Style

Marek R. Ogiela; Wenny Rahayu; Francesco Palmieri. 2021. "Human oriented solutions for intelligent analysis, multimedia and communication systems." Concurrency and Computation: Practice and Experience , no. : e6532.

Journal article
Published: 01 July 2021 in Applied Soft Computing
Reads 0
Downloads 0

The occurrence of natural and man-made disasters usually leads to significant social and economic disruption, as well as high numbers of casualties. Such occurrences are difficult to predict due to the huge number of parameters with mutual interdependencies which need to be investigated to provide reliable predictive capabilities. In this work, we present high-Performance Emergent Rescue Management e-System (PERMS), an efficient rescue route planning scheme operating within a high-performance emergent rescue management system for vehicles based on the mobile cloud computing paradigm. More specifically, an emergency rescue planning problem (ERRP) is investigated as a multiple travelling salesman problem (MTSP), as well as a novel phased heuristic rescue route planning scheme. This consists of an obstacle constraints and task of equal division-based K-means++ clustering algorithm (OT-K-means++), which is more suitable for clustering victims in disaster environments, and a glow-worm swarm optimisation algorithm based on chaotic initialisation (GSOCI), which provides the appropriate rescue route for each vehicle. A prototype is developed to evaluate the performance of this proposed approach, which demonstrates a better performance compared to other well-known and widely used algorithms. As demonstrated by the validation process, our approach enhances the accuracy and convergence speed for solving the emergency rescue planning problem. Furthermore, it shortens the length of the rescue route, as well as rescue time, and it leads to reasonable and balanced allocation of emergency rescue tasks, whilst achieving an overall efficient rescue process. More specifically, by considering scenarios with 200 victims, compared with K-means and K-means++, OT-K-means++ reduces the time cost of clustering by 9.52% and 17.39% respectively, and reduces the number of iterations by 11.11% and 15.78% respectively. Compared with ACO or GA, GSOCI reduces the length of rescue route by 9.81% and 16.36% respectively, and reduces the time of rescue by 4.35% and 15.38% respectively.

ACS Style

Xiaolong Xu; Lei Zhang; Marcello Trovati; Francesco Palmieri; Eleana Asimakopoulou; Olayinka Johnny; Nik Bessis. PERMS: An efficient rescue route planning system in disasters. Applied Soft Computing 2021, 111, 107667 .

AMA Style

Xiaolong Xu, Lei Zhang, Marcello Trovati, Francesco Palmieri, Eleana Asimakopoulou, Olayinka Johnny, Nik Bessis. PERMS: An efficient rescue route planning system in disasters. Applied Soft Computing. 2021; 111 ():107667.

Chicago/Turabian Style

Xiaolong Xu; Lei Zhang; Marcello Trovati; Francesco Palmieri; Eleana Asimakopoulou; Olayinka Johnny; Nik Bessis. 2021. "PERMS: An efficient rescue route planning system in disasters." Applied Soft Computing 111, no. : 107667.

Article
Published: 09 June 2021 in The Journal of Supercomputing
Reads 0
Downloads 0

In wireless sensor networks applications involving a huge number of sensors, some of the sensor devices may result to be redundant. As a consequence, the simultaneous usage of all the sensors may lead to a faster depletion of the available energy and to a shorter network lifetime. In this context, one of the well-known and most important problems is Maximum Network Lifetime Problem (MLP). MLP consists in finding non-necessarily disjoint subsets of sensors (covers), which are autonomously able to surveil specific locations (targets) in an area of interest, and activating each cover, one at a time, in order to guarantee the network activity as long as possible. MLP is a challenging optimization problem and several approaches have been proposed to address it in the last years. A recently proposed variant of the MLP is the Maximum Lifetime Problem with Time Slots (MLPTS), where the sensors belonging to a cover must be operational for a fixed amount of time, called operating time slot, whenever the cover is activated. In this paper, we generalize MLPTS by taking into account the possibility, for each subset of active sensors, to neglect the coverage of a small percentage of the whole set of targets. We define such new problem as \(\alpha _c\)-MLPTS, where \(\alpha _c\) defines the percentage of targets that each cover has to monitor. For this new scenario we propose three approaches: a classical Greedy algorithm, a Carousel Greedy algorithm and a modified version of the genetic algorithm already proposed for MLPTS. The comparison of the three heuristic approaches is carried out through extensive computational experiments. The computational results show that the Carousel Greedy represents the best trade-off between the proposed approaches and confirm that the network lifetime can be considerably improved by omitting the coverage of a percentage of the targets.

ACS Style

Raffaele Cerulli; Ciriaco D’Ambrosio; Antonio Iossa; Francesco Palmieri. Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches. The Journal of Supercomputing 2021, 1 -26.

AMA Style

Raffaele Cerulli, Ciriaco D’Ambrosio, Antonio Iossa, Francesco Palmieri. Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches. The Journal of Supercomputing. 2021; ():1-26.

Chicago/Turabian Style

Raffaele Cerulli; Ciriaco D’Ambrosio; Antonio Iossa; Francesco Palmieri. 2021. "Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches." The Journal of Supercomputing , no. : 1-26.

Article
Published: 02 June 2021 in Computational and Applied Mathematics
Reads 0
Downloads 0

This work presents a novel formulation for the numerical solution of optimal control problems related to nonlinear Volterra fractional integral equations systems. A spectral approach is implemented based on the new polynomials known as Chelyshkov polynomials. First, the properties of these polynomials are studied to solve the aforementioned problems. The operational matrices and the Galerkin method are used to discretize the continuous optimal control problems. Thereafter, some necessary conditions are defined according to which the optimal solutions of discrete problems converge to the optimal solution of the continuous ones. The applicability of the proposed approach has been illustrated through several examples. In addition, a comparison is made with other methods for showing the accuracy of the proposed one, resulting also in an improved efficiency.

ACS Style

Leila Moradi; Dajana Conte; Eslam Farsimadan; Francesco Palmieri; Beatrice Paternoster. Optimal control of system governed by nonlinear volterra integral and fractional derivative equations. Computational and Applied Mathematics 2021, 40, 1 -15.

AMA Style

Leila Moradi, Dajana Conte, Eslam Farsimadan, Francesco Palmieri, Beatrice Paternoster. Optimal control of system governed by nonlinear volterra integral and fractional derivative equations. Computational and Applied Mathematics. 2021; 40 (4):1-15.

Chicago/Turabian Style

Leila Moradi; Dajana Conte; Eslam Farsimadan; Francesco Palmieri; Beatrice Paternoster. 2021. "Optimal control of system governed by nonlinear volterra integral and fractional derivative equations." Computational and Applied Mathematics 40, no. 4: 1-15.

Special issue paper
Published: 13 May 2021 in Concurrency and Computation: Practice and Experience
Reads 0
Downloads 0

Nowadays, several tools have been proposed to support the operations performed during a security assessment process. In particular, it is a common practice to rely on automated tools to carry out some phases of this process in an automatic or semiautomatic way. In this article, we focus on tools for the automatic generation of custom executable payloads. Then, we will show how these tools can be transformed, through some human‐oriented modifications on the generated payloads, into threats for a given asset's security. The danger of such threats lies in the fact that they may not be detected by common antivirus (AVs). More precisely, in this article, we show a general approach to make a payload generated through automated tools run undetected by most AVs. In detail, we first analyze and explain most of the methods used by AVs to recognize malicious payloads and, for each one of them, we outline the relative strengths and flaws, showing how these flaws could be exploited using a general approach to evade AVs controls, by performing simple human‐oriented operations on the payloads. The testing activity we performed shows that our proposal is helpful in evading virtually all the most popular AVs on the market. Therefore, low‐skilled malicious users could easily use our approach.

ACS Style

Bruno Carpentieri; Arcangelo Castiglione; Francesco Palmieri; Raffaele Pizzolante. On the undetectability of payloads generated through automatic tools: A human‐oriented approach. Concurrency and Computation: Practice and Experience 2021, e6351 .

AMA Style

Bruno Carpentieri, Arcangelo Castiglione, Francesco Palmieri, Raffaele Pizzolante. On the undetectability of payloads generated through automatic tools: A human‐oriented approach. Concurrency and Computation: Practice and Experience. 2021; ():e6351.

Chicago/Turabian Style

Bruno Carpentieri; Arcangelo Castiglione; Francesco Palmieri; Raffaele Pizzolante. 2021. "On the undetectability of payloads generated through automatic tools: A human‐oriented approach." Concurrency and Computation: Practice and Experience , no. : e6351.

Journal article
Published: 11 May 2021 in Technological Forecasting and Social Change
Reads 0
Downloads 0

Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data samples in classes, especially when the number of data representing the larger class (majority) is much greater than that of the smaller class (minority). In order to solve this problem, various types of undersampling or oversampling techniques have been proposed to create a dataset with equal number of samples in each class by reducing or increasing the number of samples in majority or minority classes, respectively. Ensemble classifiers use multiple learning algorithms to enhance the accuracy of classification. Based on the results, combining undersampling or oversampling methods with ensemble classifiers can result in models with better performance. By using both clustering and new undersampling methods, the present study aimed to propose a novel clustering-based undersampling method to create a balanced dataset. This method uses k-means clustering algorithm for clustering the data, Mahalanobis distance to analyze samples distance in each cluster to centroid, and a selection method that preserves the pattern of data distribution in each cluster. Regarding the experimental results obtained by 44 benchmark datasets from KEEL repository, the proposed approach performed better than that of seven state-of-the-art approaches.

ACS Style

Mohammad Saleh Ebrahimi Shahabadi; Hamed Tabrizchi; Marjan Kuchaki Rafsanjani; B.B. Gupta; Francesco Palmieri. A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems. Technological Forecasting and Social Change 2021, 169, 120796 .

AMA Style

Mohammad Saleh Ebrahimi Shahabadi, Hamed Tabrizchi, Marjan Kuchaki Rafsanjani, B.B. Gupta, Francesco Palmieri. A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems. Technological Forecasting and Social Change. 2021; 169 ():120796.

Chicago/Turabian Style

Mohammad Saleh Ebrahimi Shahabadi; Hamed Tabrizchi; Marjan Kuchaki Rafsanjani; B.B. Gupta; Francesco Palmieri. 2021. "A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems." Technological Forecasting and Social Change 169, no. : 120796.

Journal article
Published: 06 April 2021 in Applied Sciences
Reads 0
Downloads 0

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.

ACS Style

Arpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences 2021, 11, 3278 .

AMA Style

Arpad Gellert, Stefan-Alexandru Precup, Bogdan-Constantin Pirvu, Ugo Fiore, Constantin-Bala Zamfirescu, Francesco Palmieri. An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems. Applied Sciences. 2021; 11 (7):3278.

Chicago/Turabian Style

Arpad Gellert; Stefan-Alexandru Precup; Bogdan-Constantin Pirvu; Ugo Fiore; Constantin-Bala Zamfirescu; Francesco Palmieri. 2021. "An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems." Applied Sciences 11, no. 7: 3278.

Journal article
Published: 30 March 2021 in Neural Computing and Applications
Reads 0
Downloads 0

With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.

ACS Style

Gianni D’Angelo; Francesco Palmieri. Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images. Neural Computing and Applications 2021, 1 -17.

AMA Style

Gianni D’Angelo, Francesco Palmieri. Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images. Neural Computing and Applications. 2021; ():1-17.

Chicago/Turabian Style

Gianni D’Angelo; Francesco Palmieri. 2021. "Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images." Neural Computing and Applications , no. : 1-17.

Journal article
Published: 05 March 2021 in Applied Soft Computing
Reads 0
Downloads 0

Emerging malware pose increasing challenges to detection systems as their variety and sophistication continue to increase. Malware developers use complex techniques to produce malware variants, by removing, replacing, and adding useless API calls to the code, which are specifically designed to evade detection mechanisms, as well as do not affect the original functionality of the malicious code involved. In this work, a new recurring subsequences alignment-based algorithm that exploits associative rules has been proposed to infer malware behaviors. The proposed approach exploits the probabilities of transitioning from two API invocations in the call sequence, as well as it also considers their timeline, by extracting subsequence of API calls not necessarily consecutive and representative of common malicious behaviors of specific subsets of malware. The resulting malware classification scheme, capable to operate within dynamic analysis scenarios in which API calls are traced at runtime, is inherently robust against evasion/obfuscation techniques based on the API call flow perturbation. It has been experimentally compared with two detectors based on Markov chain and API call sequence alignment algorithms, which are among the most widely adopted approaches for malware classification. In such experimental assessment the proposed approach showed an excellent classification performance by outperforming its competitors.

ACS Style

Gianni D’Angelo; Massimo Ficco; Francesco Palmieri. Association rule-based malware classification using common subsequences of API calls. Applied Soft Computing 2021, 105, 107234 .

AMA Style

Gianni D’Angelo, Massimo Ficco, Francesco Palmieri. Association rule-based malware classification using common subsequences of API calls. Applied Soft Computing. 2021; 105 ():107234.

Chicago/Turabian Style

Gianni D’Angelo; Massimo Ficco; Francesco Palmieri. 2021. "Association rule-based malware classification using common subsequences of API calls." Applied Soft Computing 105, no. : 107234.

Editorial
Published: 05 March 2021 in Applied Sciences
Reads 0
Downloads 0

Today, applications can be instantiated in a number of data centers located in different segments of the network, from the core to the edge

ACS Style

Davide Careglio; Mirosław Klinkowski; Francesco Palmieri. Special Issue: Novel Algorithms and Protocols for Networks. Applied Sciences 2021, 11, 2296 .

AMA Style

Davide Careglio, Mirosław Klinkowski, Francesco Palmieri. Special Issue: Novel Algorithms and Protocols for Networks. Applied Sciences. 2021; 11 (5):2296.

Chicago/Turabian Style

Davide Careglio; Mirosław Klinkowski; Francesco Palmieri. 2021. "Special Issue: Novel Algorithms and Protocols for Networks." Applied Sciences 11, no. 5: 2296.

Journal article
Published: 31 October 2020 in Journal of Network and Computer Applications
Reads 0
Downloads 0

The right choice of features to be extracted from individual or aggregated observations is an extremely critical factor for the success of modern network traffic classification approaches based on machine learning. Such activity, usually in charge of the designers of the classification scheme is strongly related to their experience and skills, and definitely characterizes the whole approach, implementation strategy as well as its performance. The main aim of this work is supporting this process by mining new and more expressive, meaningful and discriminating features from the basic ones without human intervention. For this purpose, a novel autoencoder-based deep neural network architecture is proposed where multiple autoencoders are embedded with convolutional and recurrent neural networks to elicit relevant knowledge about the relations existing among the basic features (spatial-features) and their evolution over time (temporal-features). Such knowledge, consisting in new properties that are not immediately evident and better represent the most hidden and representative traffic dynamics can be successfully exploited by machine learning-based classifiers. Different network combinations are analyzed both from a theoretical perspective, and through specific performance evaluation experiments on a real network traffic dataset. We show that the traffic classifier obtained by stacking the autoencoder with a fully-connected neural network, achieves up to a 28% improvement in average accuracy over state-of-the-art machine learning-based approaches, up to a 10% over pure convolutional and recurrent stacked neural networks, and 18% over pure feed-forward networks. It is also able to maintain high accuracy even in the presence of unbalanced training datasets.

ACS Style

Gianni D’Angelo; Francesco Palmieri. Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction. Journal of Network and Computer Applications 2020, 173, 102890 .

AMA Style

Gianni D’Angelo, Francesco Palmieri. Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction. Journal of Network and Computer Applications. 2020; 173 ():102890.

Chicago/Turabian Style

Gianni D’Angelo; Francesco Palmieri. 2020. "Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction." Journal of Network and Computer Applications 173, no. : 102890.

Journal article
Published: 22 October 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Nowadays, modern vehicles are becoming even more connected, intelligent, and smart. A modern vehicle encloses several cyber-physical systems such as actuators and sensors, which are controlled by electronic control units (ECUs). Such ECUs are connected through in-vehicle networks, and, in turn, such networks are connected to the Internet of Vehicles (IoV) to provide advanced and smart features. However, the increase in vehicle connectivity and computerization, although it brings clear advantages, it introduces serious safety problems that can also endanger the life of the driver and passengers of the vehicle, as well as that of pedestrians. Such problems are mainly caused by the security weaknesses affecting the Controller Area Network (CAN) bus, used to exchange data between ECUs. In this paper, we provide two algorithms that implement a data-driven anomaly detection system. The first algorithm (Cluster-based Learning Algorithm), is used to learn the behavior of messages passing on the CAN bus, for baselining purposes, while the second one (Data-driven Anomaly Detection Algorithm) is used to perform real-time classification of such messages (licit or illicit) for early alerting in the presence of malicious usages. The experimental results, obtained by using data coming from a real vehicle, have shown that our approach is capable of performing better than other anomaly-detection based approaches.

ACS Style

Gianni D'Angelo; Arcangelo Castiglione; Francesco Palmieri. A Cluster-Based Multidimensional Approach for Detecting Attacks on Connected Vehicles. IEEE Internet of Things Journal 2020, 8, 12518 -12527.

AMA Style

Gianni D'Angelo, Arcangelo Castiglione, Francesco Palmieri. A Cluster-Based Multidimensional Approach for Detecting Attacks on Connected Vehicles. IEEE Internet of Things Journal. 2020; 8 (16):12518-12527.

Chicago/Turabian Style

Gianni D'Angelo; Arcangelo Castiglione; Francesco Palmieri. 2020. "A Cluster-Based Multidimensional Approach for Detecting Attacks on Connected Vehicles." IEEE Internet of Things Journal 8, no. 16: 12518-12527.

Journal article
Published: 08 September 2020 in IEEE Access
Reads 0
Downloads 0

With an enormous range of applications, the Internet of Things (IoT) has magnetized industries and academicians from everywhere. IoT facilitates operations through ubiquitous connectivity by providing Internet access to all the devices with computing capabilities. With the evolution of wireless infrastructure, the focus from simple IoT has been shifted to smart, connected and mobile IoT (M-IoT) devices and platforms, which can enable low-complexity, low-cost and efficient computing through sensors, machines, and even crowdsourcing. All these devices can be grouped under a common term of M-IoT. Even though the positive impact on applications has been tremendous, security, privacy and trust are still the major concerns for such networks and insufficient enforcement of these requirements introduces non-negligible threats to M-IoT devices and platforms. Thus, it is important to understand the range of solutions which are available for providing a secure, privacy-compliant, and trustworthy mechanism for M-IoT. There is no direct survey available, which focuses on security, privacy, trust, secure protocols, physical layer security and handover protections in M-IoT. This paper covers such requisites and presents comparisons of state-the-art solutions for IoT which are applicable to security, privacy, and trust in smart and connected M-IoT networks. Apart from these, various challenges, applications, advantages, technologies, standards, open issues, and roadmap for security, privacy and trust are also discussed in this paper.

ACS Style

Vishal Sharma; IlSun You; Karl Andersson; Francesco Palmieri; Mubashir Husain Rehmani; Jaedeok Lim. Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey. IEEE Access 2020, 8, 167123 -167163.

AMA Style

Vishal Sharma, IlSun You, Karl Andersson, Francesco Palmieri, Mubashir Husain Rehmani, Jaedeok Lim. Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey. IEEE Access. 2020; 8 (99):167123-167163.

Chicago/Turabian Style

Vishal Sharma; IlSun You; Karl Andersson; Francesco Palmieri; Mubashir Husain Rehmani; Jaedeok Lim. 2020. "Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey." IEEE Access 8, no. 99: 167123-167163.

Journal article
Published: 19 August 2020 in Information Sciences
Reads 0
Downloads 0

In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach for solving real-world optimization problems. However, it is known that, in presence of a huge solution space and many local optima, GAs cannot guarantee the achievement of global optimality. In this work, in order to make GAs more effective in finding the global optimal solution, we propose a hybrid GA which combines the classical genetic mechanisms with the gradient-descent (GD) technique for local searching and constraints management. The basic idea is to exploit the GD capability in finding local optima to refine search space exploration and to place individuals in areas that are more favorable for achieving convergence. This confers to GAs the capability of escaping from the discovered local optima, by progressively moving towards the global solution. Experimental results on a set of test problems from well-known benchmarks showed that our proposal is competitive with other more complex and notable approaches, in terms of solution precision as well as reduced number of individuals and generations.

ACS Style

Gianni D’Angelo; Francesco Palmieri. GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Information Sciences 2020, 547, 136 -162.

AMA Style

Gianni D’Angelo, Francesco Palmieri. GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Information Sciences. 2020; 547 ():136-162.

Chicago/Turabian Style

Gianni D’Angelo; Francesco Palmieri. 2020. "GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems." Information Sciences 547, no. : 136-162.

Special issue paper
Published: 21 July 2020 in Concurrency and Computation: Practice and Experience
Reads 0
Downloads 0

At present, 96% of the resources available into the World‐Wide‐Web belongs to the Deep Web , which is composed of contents that are not indexed by search engines. The Dark Web is a subset of the Deep Web , which is currently the favorite place for hiding illegal markets and contents. The most important tool that can be used to access the Dark Web is the Tor Browser . In this article, we propose a bottom‐up formal investigation methodology for the Tor Browser's memory forensics. Based on a bottom‐up logical approach, our methodology enables us to obtain information according to a level of abstraction that is gradually higher, to characterize semantically relevant actions carried out by the Tor browser. Again, we show how the proposed three‐layer methodology can be realized through open‐source tools. Also, we show how the extracted information can be used as input to a novel Artificial Intelligence‐based architecture for mining effective signatures capable of representing malicious activities in the Tor network. Finally, to assess the effectiveness of the proposed methodology, we defined three test cases that simulate widespread real‐life scenarios and discuss the obtained results. To the best of our knowledge, this is the first work that deals with the forensic analysis of the Tor Browser in a live system, in a formal and structured way.

ACS Style

Raffaele Pizzolante; Arcangelo Castiglione; Bruno Carpentieri; Roberto Contaldo; Gianni D'angelo; Francesco Palmieri. A machine learning‐based memory forensics methodology for TOR browser artifacts. Concurrency and Computation: Practice and Experience 2020, 1 .

AMA Style

Raffaele Pizzolante, Arcangelo Castiglione, Bruno Carpentieri, Roberto Contaldo, Gianni D'angelo, Francesco Palmieri. A machine learning‐based memory forensics methodology for TOR browser artifacts. Concurrency and Computation: Practice and Experience. 2020; ():1.

Chicago/Turabian Style

Raffaele Pizzolante; Arcangelo Castiglione; Bruno Carpentieri; Roberto Contaldo; Gianni D'angelo; Francesco Palmieri. 2020. "A machine learning‐based memory forensics methodology for TOR browser artifacts." Concurrency and Computation: Practice and Experience , no. : 1.

Editorial
Published: 20 July 2020 in Information Processing & Management
Reads 0
Downloads 0

This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data analysis, and thanks to the application of AI, opens a new areas of data exploration towards planning, decision supporting, and advanced secure information management in distributed systems. In this paper we'll focus on new possible areas of transformative computing applications, especially for semantic information processing, as well as cognitive data reasoning.

ACS Style

Marek R. Ogiela; Francesco Palmieri; Makoto Takizawa. Transformative computing approaches for advanced management solutions and cognitive processing. Information Processing & Management 2020, 57, 102358 .

AMA Style

Marek R. Ogiela, Francesco Palmieri, Makoto Takizawa. Transformative computing approaches for advanced management solutions and cognitive processing. Information Processing & Management. 2020; 57 (6):102358.

Chicago/Turabian Style

Marek R. Ogiela; Francesco Palmieri; Makoto Takizawa. 2020. "Transformative computing approaches for advanced management solutions and cognitive processing." Information Processing & Management 57, no. 6: 102358.

Journal article
Published: 11 June 2020 in Computer Networks
Reads 0
Downloads 0

The 5G technology is providing totally new opportunities to mobile communications and their related applications, fostering the creation of service ecosystems and business models that will significantly revolutionize our daily life. With its massive scalability, resiliency and time-sensitive performance demands it will significantly increase the footprint of the underlying transport infrastructure by requiring new control plane facilities capable of supporting the specific needs characterizing end-to-end connections between mobile devices and their targets. Such facilities, built on top of an optical backhaul/fronthaul layer must rely on new routing mechanisms able to meet 5G Ultra-Reliable Low-Latency Communication requirements. Accordingly, this work presents a novel multi-objective distributed online routing scheme for converged fiber-radio transport infrastructures. Such scheme is able to guarantee the 5G reliability and delay optimization needs, while simultaneously taking into account the more traditional network engineering objectives, aiming at satisfying the maximum number of communication requests by taking the best from the already made infrastructural investments. Its effectiveness has been demonstrated through extensive simulation experiments that resulted in quite promising outcomes, enforcing confidence in future industrial developments.

ACS Style

Francesco Palmieri. A Reliability and latency-aware routing framework for 5G transport infrastructures. Computer Networks 2020, 179, 107365 .

AMA Style

Francesco Palmieri. A Reliability and latency-aware routing framework for 5G transport infrastructures. Computer Networks. 2020; 179 ():107365.

Chicago/Turabian Style

Francesco Palmieri. 2020. "A Reliability and latency-aware routing framework for 5G transport infrastructures." Computer Networks 179, no. : 107365.

Editorial
Published: 03 June 2020 in Sensors
Reads 0
Downloads 0

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.

ACS Style

Chang Choi; Gianni D’Angelo; Francesco Palmieri. Special Issue on Intelligent Systems in Sensor Networks and Internet of Things. Sensors 2020, 20, 3182 .

AMA Style

Chang Choi, Gianni D’Angelo, Francesco Palmieri. Special Issue on Intelligent Systems in Sensor Networks and Internet of Things. Sensors. 2020; 20 (11):3182.

Chicago/Turabian Style

Chang Choi; Gianni D’Angelo; Francesco Palmieri. 2020. "Special Issue on Intelligent Systems in Sensor Networks and Internet of Things." Sensors 20, no. 11: 3182.

Editorial
Published: 08 May 2020 in Journal of Network and Computer Applications
Reads 0
Downloads 0
ACS Style

Li Jin; Dong Changyu; Palmieric Francesco. Special issue on advanced techniques for security and privacy of internet-of-things with machine learning. Journal of Network and Computer Applications 2020, 167, 102697 .

AMA Style

Li Jin, Dong Changyu, Palmieric Francesco. Special issue on advanced techniques for security and privacy of internet-of-things with machine learning. Journal of Network and Computer Applications. 2020; 167 ():102697.

Chicago/Turabian Style

Li Jin; Dong Changyu; Palmieric Francesco. 2020. "Special issue on advanced techniques for security and privacy of internet-of-things with machine learning." Journal of Network and Computer Applications 167, no. : 102697.