Detecting deceptive behavior in LLMs is typically done post-hoc on outputs or by probing static activations.We instead treat deception as a dynamic process, a trajectory through the model's hidden-state space during inference.We capture layerwise activations at sparse "decision points" where the model is uncertain between competing tokens, forming activation trajectories for matched truthful vs. deceptive responses across strategic deception, sycophancy, instructed deception, and confabulation.Across GPT-2 and Llama variants, deceptive generation is associated with changes in trajectory geometry, but increases in path length are model and deception-type-dependent.Sycophancy shows the clearest signal, whereas instructed deception yields near-null signatures.With just 7 geometric features, a lightweight classifier achieves performance comparable to PCA-reduced probing at matched dimensionality for binary sycophancy detection and shows preliminary utility for 4-way deceptiontype classification.These findings indicate that trajectory-based monitoring can provide process-level signals associated with deceptive generation during inference, complementing methods that focus on endpoint activation states.

Alzheimer’s disease represents a growing global health concern, emphasizing the need for early diagnosis to mitigate neurocognitive decline. Speech analysis has emerged as a promising, non-invasive approach, yet limited data availability hinders the development of robust Machine Learning (ML) models. To address this challenge, this study exploits the potentialities of Large Language Models (LLMs)—both their ability to generate synthetic data and their capacity to extract complex linguistic features from speech. We employ GPT-4 to generate synthetic transcripts, thus expanding the ADReSS2020 dataset and enhancing its diversity while preserving semantic and structural coherence. Moreover, we propose a novel multilevel feature extraction framework that integrates Bidirectional Encoder Representations from Transformers (BERT) embeddings fine-tuned with linguistic features obtained through Computerized Language Analysis (CLAN). The study involved two experiments: first, to identify the optimal feature extraction strategy and second, to evaluate the impact of synthetic data generated by GPT-4. In both experiments, the performance of five classifiers was evaluated to determine the most effective configuration. Our results demonstrated that fine-tuned BERT embeddings slightly improve classification performance compared to pre-trained models, highlighting the value of domain-specific fine-tuning. Although adding CLAN-like linguistic features yielded limited benefits, GPT-4-generated synthetic data demonstrated promising potential, particularly when combined with sentence embeddings. Classifiers such as Random Forest showed an improvement in accuracy, increasing from 0.79 to 0.88 when using the augmented dataset. This study paves the way for the use of LLMs to expand the diversity of datasets and improve the robustness of ML models in clinical applications.

Autonomous cybersecurity represents a significant evolution in information security, where systems independently detect, respond to, and neutralize cyber threats without the need for human intervention. This level of autonomy is a more advanced stage in cybersecurity, enabling systems not only to execute tasks but also to interpret contexts, make decisions, and adapt strategies in realtime. The shift towards autonomy promises enhanced adaptability, faster response times, and a reduction in human error. This domain stands out for its unique blend of advanced Machine Learning (ML) systems such as Reinforcement Learning (RL)-driven and Quantum Machine Learning (QML)-based agents with cybersecurity automation techniques such as automated patch management systems, automated incident response systems to forge self-reliant cybersecurity systems. The AutonomousCyber workshop provides a venue for presenting and discussing new developments in this field.

Stress is considered one of the most prevalent concerns among individuals. Studies have shown that experiencing long-term stress can cause severe health issues such as cardiovascular diseases, hypertension, depression, etc. Preventative measures, such as early stress detection, can help individuals mitigate these health issues. When a person gets stressed, physiological values like blood volume pulse, temperature, and electrodermal activity signals get affected. Machine Learning techniques can be utilized to identify stress by analyzing these physiological signals. This paper presents a machine learning method for detecting stress levels of an individual using the publicly available dataset called "Wearable Stress and Affect Detection"(WESAD), which has physiological data collected from the wrist-worn and chest-worn sensors attached to 15 different subjects. We used physiological signals, including Blood Volume Pulse(BVP), Body Temperature(TEMP), and Electrodermal Activity(EDA) signals, collected from wrist-worn sensors to detect the state of the mind. For the implementation, we used different Machine Learning models, like Logistic Regression, Decision Tree, Random Forest, and Stacking Ensemble Learning technique. During the investigation, personalized models, utilizing individual subject data, and generalized models, amalgamating all subject data, were developed. Evaluation reveals accuracy values reaching up to 99% and 91% for individual subject data and combined data, respectively.

Although federated learning offers a level of privacy by aggregating user data without direct access, it remains inherently vulnerable to various attacks, including poisoning attacks where malicious actors submit gradients that reduce model accuracy. In addressing model poisoning attacks, existing defense strategies primarily concentrate on detecting suspicious local gradients over plaintext. However, detecting non-independent and identically distributed encrypted gradients poses significant challenges for existing methods. Moreover, tackling computational complexity and communication overhead becomes crucial in privacy-preserving federated learning, particularly in the context of encrypted gradients. To address these concerns, we propose a robust privacy-preserving federated learning model resilient against model poisoning attacks without sacrificing accuracy. Our approach introduces an internal auditor that evaluates encrypted gradient similarity and distribution to differentiate between benign and malicious gradients, employing a Gaussian Mixture Model and Mahalanobis Distance for byzantine-tolerant aggregation. The proposed model utilizes Additive Homomorphic Encryption to ensure confidentiality while minimizing computational and communication overhead. Our model demonstrates superior performance in accuracy and privacy compared to existing strategies and encryption techniques, such as Fully Homomorphic Encryption and Two-Trapdoor Homomorphic Encryption. The proposed model effectively addresses the challenge of detecting maliciously encrypted non-independent and identically distributed gradients with low computational and communication overhead.

The convergence of Metaverse and Internet of Things (IoT) presents new opportunities for exchanging data, but it also introduces unprecedented security challenges. With the proliferation of IoT devices, the risk of unauthorized access and data breaches is on the rise, posing significant threats to data confidentiality and integrity. To address these challenges and protect user privacy, comprehensive security solutions are essential. We propose the SafeMetaNet approach, which combines proximity-based authentication, encryption, and blockchain technology to establish secure data exchange in the IoT-Metaverse convergence. SafeMetaNet ensures data confidentiality and integrity through encryption and establishes a tamper-proof record of data exchange using blockchain technology. We evaluated the approach’s performance using various metrics, including latency, throughput, and two security metrics: data confidentiality and data integrity, and compared it with existing approaches. Our findings show that SafeMetaNet outperforms existing approaches, providing improved security. SafeMetaNet is a promising solution for secure data exchange in the IoT-Metaverse convergence.

Heart disease is a leading cause of morbidity and mortality worldwide, necessitating the development of innovative diagnostic methodologies for early detection. This study presents a novel deep convolutional neural network model that leverages Mel-spectrograms to accurately classify heart sounds. Our approach demonstrates significant advancements in heart disease detection, achieving high accuracy, specificity, and unweighted average recall scores (UAR), which are critical factors for practical clinical applications. The comparison of our proposed model's performance with a PANN-based model from a previous study highlights the strengths of our approach, particularly in terms of specificity and UAR. The successful application of Mel-spectrograms in conjunction with deep learning techniques illustrates the potential for widespread clinical adoption of our model, ultimately contributing to early detection and improved patient outcomes. Furthermore, we discuss potential avenues for future research to enhance the model's effectiveness, such as incorporating additional features and exploring alternative deep learning architectures. In conclusion, our deep convolutional neural network model, combined with Mel-spectrograms, offers a significant step forward in the field of heart sound classification and the early detection of heart diseases, demonstrating its potential for real-world clinical applications and improved patient outcomes.

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we explored the effectiveness of Resnet 50v2 and Resnet 152v2 deep learning models for classifying mammograms using EMBED datasets for the first time. We preprocessed the datasets and utilized various techniques to enhance the performance of the models. Our results suggest that the choice of model architecture depends on the dataset used, with ResNet152 outperforming ResNet50 in terms of recall score. These findings have implications for cancer screening, where recall is an important metric. Our research highlights the potential of deep learning to improve breast cancer classification and underscores the importance of selecting the appropriate model architecture.

Ethereum blockchain has shown great potential in providing the next generation of the decentralized platform beyond crypto payments. Recently, it has attracted researchers and industry players to experiment with developing various Web3 applications for the Internet of Things (IoT), Defi, Metaverse, and many more. Although Ethereum provides a secure platform for developing decentralized applications, it is not immune to security risks and has been a victim of numerous cyber attacks. Adversarial attacks are a new cyber threat to systems that have been rising. Adversarial attacks can disrupt and exploit decentralized applications running on the Ethereum platform by creating fake accounts and transactions. Detecting adversarial attacks is challenging because the fake materials (e.g., accounts and transactions) as malicious payloads are similar to benign data. This article proposes a model using Generative Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN) for cyber threat hunting in the Ethereum blockchain. Firstly, we employ GAN to generate fake transactions using genuine Ethereum transactions as the first phase of the proposed model. Then in the second phase, we utilize bi-directional Long Short-Term Memory (LSTM) to identify adversarial transactions in a hunting exercise. The results of the first phase evaluation show that the GAN can generate transactions identical to the actual Ethereum transactions with an accuracy of 82.51%. Also, the results of the second phase show 99.98% accuracy in identifying adversarial transactions.

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising artificial intelligence (AI) solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate temporal convolutional network, graph convolutional neural network, and gated recurrent unit architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

Intelligent sensing solutions bridge the gap between the physical world and the cyber-physical systems by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide physical and virtual sensing device resources and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to transfer the entire training dataset from end devices to a central server. To address this, we propose a Quantized Federated Learning (FL) based approach, called CloudFL, to ensure data privacy on end devices in a sensor cloud network. Our framework enables a personalized version of FL implementation and enhances privacy and security with cryptosystem tools to obfuscate the information of the FL process from unauthorized access. Furthermore, microservices of our approach provide software as a service implementation of FL with instances of cloud servers that require zero-touch on local data for training.

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which, in turn, makes IoT networks an open source of personally identifiable information data for malicious attackers to steal, manipulate, and perform nefarious activities. A huge interest has been developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data are widely available and transferable to the main server because data are born at the edge and are generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose the federated-learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on gated recurrent units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyberattacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this article, we use federated learning to build a threat hunting framework called block hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, block hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the block hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.

Billions of IoT devices are connected to networks all around us, enabling cyber-physical systems. These devices can carry and generate user-sensitive data, examples of such devices are smartwatches, medical equipment, and smart home gadgets. Individual IoT devices have some form of intrusion detection system integrated, but once they are all connected, a network threat to one device could mean a threat to many. IoT devices must have a robust intrusion detection system that would keep devices secure over a network. To aid with this, we provide a machine learning solution that adheres to Global Data Protection Regulation by keeping the user data secure locally on the IoT device itself. We propose a Federated Learning (FL) approach that capitalizes on a decentralized and collaborative way of training machine learning models. In this study, we practice federated learning technique to train and create a robust intrusion detection model for the security of IoT devices. We evaluate our proposed approach using three different use-cases to show the security enhancements that improve using the FL technique, resulting in a more reliable performance in this domain.

Blockchain technology has found extensive applications in recent years, especially in financial and currency exchange applications, due to improved trustworthiness and security. Although blockchain technology improves security by design, it is not immune to security threats and vulnerabilities. Ethereum, as a decentralized, open-source blockchain, has shown high growth and widespread adoption in recent years, however, there is a wide range of vulnerability, security risks, and also attacks around it. To tackle such issues, machine learning could be a viable solution for threat hunting in the Ethereum blockchain. Machine learning algorithms, by analyzing the behav-ioral patterns, can achieve an insight for threat hunting. In this paper, we proposed a deep learning-based model for Ethereum threat hunting. The model applies a deep neural network for attack detection and uses a combination of machine learning algorithms (unsupervised with supervised algorithms) for attack classification. The performance evolution of the proposed model in terms of accuracy presents 97.72 % in Ethereum attack detection and 99.4% in attack classification.

Designing and developing autonomous vehicles that are capable of moving safely on roads by sensing the environment has motivated researchers to focus on pedestrian detection systems so they can detect people as fast and accurately as possible. However, for pedestrian detection, it is crucial to consider not only the pedestrians themselves but their color as well, because color has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Therefore, considering skin color detection for implementing pedestrian detection systems is an essential required step in ensuring autonomous vehicles are further incorporated into our society. Detecting human skin has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, imaging conditions, and others. Recently it has been noted that pedestrian detection systems for autonomous vehicles perform poorly at detecting people with darker skin tones. Such findings indicate that there is a larger problem that is causing these issues: algorithmic bias. Algorithmic bias in pedestrian detection systems could be the leading factor of their poor performance due to the methods implemented and datasets used. Unfortunately, the algorithmic bias in this context has not been considered closely and it seems that many studies do not cover this aspect closely when discussing pedestrian detection systems for autonomous vehicles. To alleviate this, we attempt to explore different techniques that can be used to detect pedestrians while minimizing bias. In our experiment, we use both a YOLO v3 convolutional neural network and K-Means clustering for classifying skin-tones.

With the rapid development of commercial drones, drone detection and classification have emerged and grown recently. Drone detection works to detect unmanned aerial vehicles (UAVs). Usually, systems for drone detection utilize a combination of one or more sensors and some methodology. Many unique technologies and methods are used to detect drones. However, each type of technology offers its benefits and limitations. Most approaches use computer vision or machine learning, but one methodology that has not been given much attention is Sensor Fusion. Sensor Fusion has less uncertainty than most methods, making it suitable for drone detection. In this paper, we propose an artificial neural network-based detection system that uses a deep neural network (DNN) to process the RF data and a convolutional neural network (CNN) to process image data. The features from CNNs and DNNs are concatenated and input into another DNN, which outputs a single prediction score of drone presence. Our model achieved a validation accuracy of 75% that shows the feasibility of a sensor fusion based technique for drone detection.

Hadoop Distributed File System (HDFS) is one of the widely used distributed file systems in big data analysis for frameworks such as Hadoop. HDFS allows one to manage large volumes of data using low-cost commodity hardware. However, vulnerabilities in HDFS can be exploited for nefarious activities. This reinforces the importance of ensuring robust security to facilitate file sharing in Hadoop as well as having a trusted mechanism to check the authenticity of shared files. This is the focus of this paper, where we aim to improve the security of HDFS using a blockchain-enabled approach (hereafter referred to as BlockHDFS). Specifically, the proposed BlockHDFS uses the enterprise-level Hyperledger Fabric platform to capitalize on files' metadata for building trusted data security and traceability in HDFS.

There is currently widespread use of drones and drone technology due to their rising applications that have come into fruition in the military, safety surveillance, agriculture, smart transportation, shipping, and delivery of packages in our Internet-of-Things global landscape. However, there are security-specific challenges with the authentication of drones while airborne. The current authentication approaches, in most drone-based applications, are subject to latency issues in real time with security vulnerabilities for attacks. To address such issues, we introduce a secure authentication model with low latency for drones in smart cities that looks to leverage blockchain technology. We apply a zone-based architecture in a network of drones, and use a customized decentralized consensus, known as drone-based delegated proof of stake (DDPOS), for drones among zones in a smart city that does not require reauthentication. The proposed architecture aims for positive impacts on increased security and reduced latency on the Internet of Drones (IoD). Moreover, we provide an empirical analysis of the proposed architecture compared to other peer models previously proposed for IoD to demonstrate its performance and security authentication capability. The experimental results clearly show that not only does the proposed architecture have low packet loss rate, high throughput, and low end-to-end delay in comparison to peer models but also can detect 97.5% of attacks by malicious drones while airborne.

Abstract A flaky test is a test that may lead to different results in different runs on a single code under test without any change in the test code. Test flakiness is a noxious phenomenon that slows down software deployment, and increases the expenditures in a broad spectrum of platforms such as software‐defined networks and Internet of Things environments. Industrial institutes and labs have conducted a whole lot of research projects aiming at tackling this problem. Although this issue has been receiving more attention from academia in recent years, the academic research community is still behind the industry in this area. A systematic review and trend analysis on the existing approaches for detecting and root causing flaky tests can pave the way for future research on this topic. This can help academia keep pace with industrial advancements and even lead the research in this field. This article first presents a comprehensive review of recent achievements of the industry as well as academia regarding the detection and mitigation of flaky tests. In the next step, recent trends in this line of research are analyzed and a roadmap is established for future research.

The size and number of cloud data centers (CDCs) have grown rapidly with the increasing popularity of cloud computing and high-performance computing. This has the unintended consequences of creating new challenges due to inefficient use of resources and high energy consumption. Hence, this necessitates the need to maximize resource utilization and ensure energy efficiency in CDCs. One viable approach to achieve energy efficiency and resource utilization in CDC is task scheduling. While several task scheduling approaches have been proposed in the literature, there appears to be a lack of classification-based merging concept for real-time tasks in these existing approaches. Thus, an energy-efficient dynamic scheduling scheme (EDS) of real-time tasks for virtualized CDC is presented in this paper. In the scheduling scheme, the heterogeneous tasks and virtual machines are first classified based on a historical scheduling record. Then, similar type of tasks are merged and scheduled to maximally utilize an operational state of the host. In addition, energy efficiencies and optimal operating frequencies of heterogeneous physical hosts are employed to attain energy preservation while creating and deleting the virtual machines. Experimental results show that, in comparison with existing techniques, EDS significantly improves overall scheduling performance, achieves a higher CDC resource utilization, increases task guarantee ratio, minimizes the mean response time, and reduces energy consumption.

introduction Introduction to the Special Issue on Decentralized Blockchain Applications and Infrastructures for Next Generation Cyber-Physical Systems Share on Editors: Kim Kwang Raymond Choo University of Texas at San Antonio University of Texas at San AntonioView Profile , Uttam Ghosh Vanderbilt University Vanderbilt UniversityView Profile , Deepak Tosh University of Texas El Paso University of Texas El PasoView Profile , Reza M. Parizi Kennesaw State University Kennesaw State UniversityView Profile , Ali Dehghantanha University of Guelph University of GuelphView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 2June 2021 Article No.: 38epp 1–3https://doi.org/10.1145/3464768Online:15 June 2021Publication History 0citation68DownloadsMetricsTotal Citations0Total Downloads68Last 12 Months68Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The recent Covid-19 pandemic instigated many changes in our way of life within the United States, and slowly but surely we are working towards mitigating the virus. Due to Covid-19, there are higher demands for models to accurately forecast the number of Covid-19 cases that factor mandated guidelines such as social-distancing. Many scholarly and corporate research entities are investigating ways to achieve this goal preemptively; Unfortunately, current models are not yet able to accurately model future Covid-19 cases that factor in various guidelines; What is lacking with these models is an understanding of crucial factors affecting spread, accuracy, availability of reported cases on a small scale, and quantifiable metrics for how social distancing and quarantine efforts mitigate the spread. Therefore, the goal of this study is to produce a mathematical model to directly aid policy decisions by comparing predicted models of various decisions and social distancing protocols. This model can be applied on top of existing models to factor in more imminent data and produce predictive curves, indicating troughs and peaks of new daily Covid-19 cases with comparatively high accuracy, which can aid in analysis. These predictive curves can, therefore, be generated using data corresponding to projected responses to proposed guidelines and compared to each other to choose the optimal solution for “flattening the curve” of the Covid19 infection rate. We use an LSTM-RNN model with ANN Regression in an attempt to predict future Covid-19 cases. Our model achieved comparable results, but further improvements could be implemented for more optimal results.

The Roadside Unit (RSU) allocation is critical for the functionality and topology control of Vehicular Ad-Hoc Networks. However, due to the complexity of different transportation scenarios and the challenging coordination among different RSUs, the allocation is still a challenging issue in both the academic and practical industry. In this paper, we utilize the game theoretic RSU deployment to fundamentally improve the allocation of RSUs with practical consideration. Given a set of RSUs of arbitrary covering radii, assuming there is a budget requirement that specifies the total number of RSUs to be placed. In addition, considering the minimum distance requirement between any pair of RSUs, how to select a subset of RSUs to cover the maximum number of Points of Interest (POIs). We consider the selfish behaviors of RSU allocation and apply a game theoretic technique. We propose a mechanism to achieve a small price of anarchy.

Traditional Internet of Things (IoT) system architectures are centralized. Data from the devices are stored on the back-end, where they are processed and analyzed, and then reconnected to IoT devices. The scalability of centralized systems is very limited especially when an abundance of devices exist on an IoT network. Network security in IoT networks is another aspect at stake that could be compromised easily due to the unavailability of security in design mechanisms in most IoT networks. Blockchain technology is a distributed ledger without any intensive management that can store all transactions which leads to large amounts of data that increases over time. Large data amounts will be more pronounced with the increasing IoT devices and blockchain use cases involving IoT. IoT devices are for the most part constrained in both energy, storage, and computation, unlikely to be able to store all blockchain data. The current implementation of blockchain is not IoT friendly. Moreover, consensus on the blockchain using Proof of Work (PoW) is infeasible due to computational constraints. In this paper, we propose a Secure and Low latency Proof of Work (SLPoW) protocol. We also bring the computation of miners onto a Field-programmable gate array (FPGA) to improve the processing speeds of computation. We consider our resulting blockchain technology using SLPoW suitable for the evolving Green IoT setting.

Internet-of-Things (IoT) devices and systems will be increasingly targeted by cybercriminals (including nation state-sponsored or affiliated threat actors) as they become an integral part of our connected society and ecosystem. However, the challenges in securing these devices and systems are compounded by the scale and diversity of deployment, the fast-paced cyber threat landscape, and many other factors. Thus, in this article, we design an approach using advanced deep learning to detect cyber attacks against IoT systems. Specifically, our approach integrates a set of long short-term memory (LSTM) modules into an ensemble of detectors. These modules are then merged using a decision tree to arrive at an aggregated output at the final stage. We evaluate the effectiveness of our approach using a real-world data set of Modbus network traffic and obtain an accuracy rate of over 99% in the detection of cyber attacks against IoT devices.

In any interconnected healthcare system (e.g., those that are part of a smart city), interactions between patients, medical doctors, nurses and other healthcare practitioners need to be secure and efficient. For example, all members must be authenticated and securely interconnected to minimize security and privacy breaches from within a given network. However, introducing security and privacy-preserving solutions can also incur delays in processing and other related services, potentially threatening patients lives in critical situations. A considerable number of authentication and security systems presented in the literature are centralized, and frequently need to rely on some secure and trusted third-party entity to facilitate secure communications. This, in turn, increases the time required for authentication and decreases throughput due to known overhead, for patients and inter-hospital communications. In this paper, we propose a novel decentralized authentication of patients in a distributed hospital network, by leveraging blockchain. Our notion of a healthcare setting includes patients and allied health professionals (medical doctors, nurses, technicians, etc), and the health information of patients. Findings from our in-depth simulations demonstrate the potential utility of the proposed architecture. For example, it is shown that the proposed architecture's decentralized authentication among a distributed affiliated hospital network does not require re-authentication. This improvement will have a considerable impact on increasing throughput, reducing overhead, improving response time, and decreasing energy consumption in the network. We also provide a comparative analysis of our model in relation to a base model of the network without blockchain to show the overall effectiveness of our proposed solution.

Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data transmission across IoT and edge networks, due to limited energy and computing (e.g., processing and storage) resources of networked devices. Whether malicious or accidental, interference with data in an IoT network potentially has real-world consequences. In this article, we explore the potential of integrating blockchain and software-defined networking (SDN) in mitigating some of the challenges. Specifically, we propose a secure and energy-efficient blockchain-enabled architecture of SDN controllers for IoT networks using a cluster structure with a new routing protocol. The architecture uses public and private blockchains for Peer to Peer (P2P) communication between IoT devices and SDN controllers, which eliminates Proof-of-Work (POW), as well as using an efficient authentication method with the distributed trust, making the blockchain suitable for resource-constrained IoT devices. The experimental results indicate that the routing protocol based on the cluster structure has higher throughput, lower delay, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV routing protocols. In other words, our proposed architecture is demonstrated to outperform classic blockchain.

The majority of malicious mobile attacks take advantage of vulnerabilities in mobile applications, such as sensitive data leakage via inadvertent or side channel, unsecured sensitive data storage, data transmission, and many others. Most of these mobile vulnerabilities can be detected in the mobile software testing phase. However, most development teams often have virtually no time to address them due to critical project deadlines. To combat this, the more defect removal filters there are in the software development life cycle, the fewer defects that can lead to vulnerabilities will remain in the software product when it is released. As part of Secure Mobile Software Development (SMSD) project, we are currently developing capacity to address the lack of pedagogical materials and real world learning environment in secure mobile software development through effective, engaging, and investigative approaches. In this session, we provide details of a new implemented module named data protection. We also share our initial experience and feedback on the developed module.

The surging pace of Internet of Things (IoT) development and its applications has resulted in significantly large amounts of data (commonly known as big data) being communicated and processed across IoT networks. While cloud computing has led to several possibilities in regard to this computational challenge, there are several security risks and concerns associated with it. Edge computing is a state-of-the-art subject in IoT that attempts to decentralize, distribute and transfer computation to IoT nodes. Furthermore, IoT nodes that perform applications are the primary target vectors which allow cybercriminals to threaten an IoT network. Hence, providing applied and robust methods to detect malicious activities by nodes is a big step to protect all of the network. In this study, we transmute the programs’ OpCodes into a vector space and employ fuzzy and fast fuzzy pattern tree methods for malware detection and categorization. We obtained a high degree of accuracy during reasonable run-times especially for the fast fuzzy pattern tree. Both utilized feature extraction and fuzzy classification, which were robust, led to more powerful edge computing malware detection and categorization method.

Consistent growth in the software sector of the world economies has attracted both targeted and mass-scale attacks by cybercriminals. Producing reliable and secure software is difficult because of its growing complexity and the increasing number of sophisticated attacks. Developers cannot afford to believe that their security measures during development are perfect and impenetrable. In fact, many new software security vulnerabilities are discovered on a daily basis. Therefore, it is vital to identify and resolve those security vulnerabilities as early as possible. Security Vulnerability Testing (SVT), as an active defense, is the key to the agile detection and prevention of known and unknown security vulnerabilities. However, many software engineers lack the awareness of the importance of security vulnerability and the necessary knowledge and skills at the testing and operational stages. As a first step towards filling this gap, this paper advocates for building skills in selecting proper benchmarks for the assessment of SVT tools to enable distinguishing effective security tools from trivial ones. Thus, we develop a set of benchmark requirements to fulfill this need, primarily guiding newcomers and researcher into this discipline.

Software engineering is inherently a human-centric and collaborative process and this reflects in its teaching programs, as most of the courses comprise projects and team efforts. In order to fairly evaluate students, there is the problem of quantifying the amount of work contributed to the team development project by each of its members. Most commonly, in order to estimates student contributions, instructors use arbitrary and subjective judgment derived from observations and evaluations. The currently used process is not a complete picture and is time consuming since it requires numerous observations and extensive paperwork's review. Emerging decentralized systems (such as git) and their widespread applications in all realms of development which capitalize on team-aware metrics, are worthwhile and can provide a solution to the problem. In this work we support a solution that utilizes git-driven technology, and its related features, to measure a team member's contributions objectively, based not only upon the completion of the project, but also at any time during progression development. Such performance assessment could generate more productive team-based learning with higher-quality graduates for better meeting software industry's expectations.

As mobile computing is now becoming more and more popular, the security threats to mobile applications are also growing explosively. Mobile app flaws and security defects could open doors for hackers to break into them and access sensitive information. Most vulnerabilities should be addressed in the early stage of mobile software development. However, many software development professionals lack awareness of the importance of security vulnerability and the necessary security knowledge and skills at the development stage. The combination of the prevalence of mobile devices and the rapid growth of mobile threats has resulted in a shortage of secure software development professionals. Many schools offer mobile app development courses in computing curriculum; however, secure software development is not yet well represented in most schools' computing curriculum. This paper addresses the needs of authentic and active pedagogical learning materials for SSD and challenges of building Secure Software Development (SSD) capacity through effective, engaging, and investigative approaches. In this paper, we present an innovative authentic and active SSD learning approach through a collection of transferrable learning modules with hands-on companion labs based on the Open Web Application Security Project (OWASP) recommendations. The preliminary feedback from students is positive. Students have gained hands-on real world SSD learning experiences with Android mobile platform and also greatly promoted self-efficacy and confidence in their mobile SSD learning.

This innovative practice work in progress paper tackles the problem of unfairness and bias in software, that recently has emerged in countless cases. This unfairness can be present in the way software makes its decision or can limit the software functionalities to work only with certain populations. Well-known examples of this problem are the Microsoft Kinect facial recognition algorithm, which does not work properly with darker skin players, and the software used in 2016 by Amazon.com to determine the parts of the United States to which offer free same-day delivery that made decisions that prevented minority neighborhoods from participating in the program. The reasons behind these phenomena have often roots in the fact that software is created by humans who are biased and live in biased and non-inclusive environments. Recent research from the software engineering community is starting to tackle this problem at many levels from requirements analysis to the new automatic fairness testing technique (proposed first at FSE 2017 conference). However, research in bias of software is still a very undervalued and rarely discussed problem as software is often seen as a product immune to bias and non-inclusivity. This problem will be not addressed unless software engineering educators start to include this notion as a first-class problem in their foundation courses to future generation of scholars. In this work, we propose a set of bias-aware guidelines and taxonomy on how to flesh out this problem and possible solutions to it in software engineering curricula.

Traceability is the ability to trace the influence of one software artifact on another by linking dependencies. Test-to-code traceability (relationships between test and system code) plays a vital role in the production, verification, reliability and certification of highly dependable systems. In practical settings, however, traceability tasks are most often observed in the breach by human developers/testers. Prior research works on test-to-code traceability in software engineering do not provide high traceability output and accuracy as they mainly rely on sought-after approaches to recover links. Gamified Software Engineering (GSE) is a growing field that in particular taps into gamification, the application of game mechanics in non-game contexts, to address human-related concerns in the field of SE. This paper argues that a new gamified approach is necessary to tackle the human issue of capturing traceability information in a by-product manner. Thus, it advocates for the induction of gamification concepts in software traceability. We propose a conceptual framework where the gamification is infusing engagement into human-centric traceability tasks to record trace links. An empirical evaluation was performed to assess the performance of the framework compared with a state-of-the-art approach.

Testing is a key activity of software development and maintenance that determines the level of reliability. Traceability is the ability to describe and follow the life of software artifacts, and has been promoted as a means for supporting various activities, most importantly testing. Traceability information facilitates the testing and debugging of complex software by modeling the dependencies between code and tests. Actively supplementing traceability to testing enables rectifying defects more reliably and efficiently. Despite its importance, the development of test-to-code traceability has not been sufficiently addressed in the literature, and even worse there is currently no organized review of traceability studies in this field. In this work, we have investigated the main conferences, workshops, and journals of the requirements engineering, testing, and reliability, and identified those contributions that refer to traceability topics. From that starting point, we characterized and analyzed the chosen contributions against three research questions by utilizing a comparative framework including nine criteria. As a result, our study arrives to some interesting points, and outlines a number of potential research directions. This, in turn, can pave the way for facilitating and empowering traceability research in this domain to assist software engineers and testers in test management.

In this paper, we propose a hybrid approach based on the aspect-orientation methodology and time series analysis to the runtime monitoring and quality forecasting of OSS. Specifically, the major objective of this work is to combine the idea of time series analysis with the area of software quality assurance of OSS in which statistical techniques for analyzing of time series is used to facilitate the prediction and forecasting (the term ‘prediction’ and ‘forecasting’ are interchangeably used in the literature) of probabilistic quality properties, which are difficult or inapplicable to be evaluated by current approaches such as testing, and also help to increase the reliability and productivity of working OSS system components (towards trustworthy open source software development) requiring extreme runtime quality control. Furthermore, in order to reduce the human effort and to cope with more sophisticated scenarios, this study also aims to automate the analysis and modeling process by providing appropriate tool.