Drone operations are gaining high interest in many sectors. Continuous and reliable communication between drones and their Ground Control Station (GSC) is crucial for successful and safe drone operations. However, due to the wireless nature of this communication, the Quality of Service (QoS) is often unreliable. Obstacles or cyberattacks (e.g., jamming) can impair communication, endangering drones, operators, and even public safety. In this paper, we therefore analyze drone communication QoS and propose a formal QoS definition to estimate QoS for a given situation and communication interface. We further present an Adaptive Drone Communication Redundancy (ADCR) mechanism to improve drone communication QoS efficiently. Using the estimated and monitored QoS, ADCR dynamically increases the number of used heterogeneous interfaces to communicate redundantly when the QoS is below a certain threshold. To conserve resources, when the QoS threshold is met, the redundancy is reduced again. We evaluate our work using a physical drone testbed which demonstrates that ADCR improves reliability even in the presence of jamming attacks, while creating a 50% lower overhead than fully redundant communication, thus balancing the trade-off between efficiency and redundancy.

Internet of Things (IoT) and Operational Technology (OT) are becoming essential parts of next-generation smart environments, including Industry 4.0 thanks to their capabilities of monitoring and/or controlling industrial devices, processes, and events. It is expected that there will be around 75 billion such devices, especially considering 5G (and 6G in the future) networking. However, security is one of the major concerns, especially because most legacy devices/equipment have little to no security measures applied. And, replacing all the equipment with new ones continuously is not a feasible solution to enforce security measures. One alternative is enforcing Zero Trust Architecture (ZTA) principles in these environments based on the “never trust, always verify” principle. Applying ZT for an OT environment through its network (by continuously monitoring and checking the activities based on ZTA-based policies) can improve the sustainability and security of the IoT/OT-based environments because no major changes would be needed in the existing infrastructures while improving security. For this reason, we propose an architecture that monitors the network behavior and compares them with the devices' proposed identities (their IP and MAC addresses). We first monitor the network behaviors, create flows, and select the best features. Then, we identify the individual devices using supervised machine learning techniques such as Gradient Boosting Classifier as they are lightweight and fast in detection compared to alternatives. Our results show high accuracy, precision, and recall values in our experiments. Using different scenarios, we evaluated our idea and if a device behaves differently than the proposed behavior, we are able to detect and put a firewall rule that blocks the device network traffic on the gateway.

“Crowd Counting” is a term used to describe the process of calculating the number of people in a given context. Originally answered with basic sensors and cameras, crowd counting has multiple challenges especially when images representing a given crowd span multiple images. In this paper, we propose a Crowd Counting Camera Array and Correction (CCCAC) method using a camera array of scaled, rotated, geometrically corrected, combined, processed, and then evaluated images to determine the number of people within the newly created combined crowd field. We present our approaches for each step and present our mean-squared-error (MSE) and mean-absolute-error (MAE) results. Experimental results using real data from multiple cameras show that CCCAC methods perform superior to raw image processing methods with an accuracy of approximately 95%.

Many people have online social accounts (OSAs) in online communities (OCs) such as Facebook (Meta), Twitter (X), Instagram (Meta), Mastodon, and Nostr. OCs enable quick and easy interaction with friends, family, and even online communities to share information about. However, there is also a dark side to OCs, where users with malicious intent join OC platforms with the purpose of criminal activities such as spreading fake news/information, cyberbullying, propaganda, phishing, stealing, and unjust enrichment. These criminal activities are especially concerning when harming minors. Detection and mitigation are needed to protect and help OCs by detecting, stopping, and preventing such criminals from harming others. To answer this challenge, we propose the first steps of a framework for analyzing and identifying malicious intent in OCs, which we refer to as the malicious intent detection framework (MIDF). MIDF is an extensible proof-of-concept that uses machine learning techniques to enable detection and mitigation. The framework will first be used to detect malicious users using only relationships. Then it can be leveraged to create a suite of malicious intent vector detection models, including phishing, propaganda, scams, cyberbullying, racism, spam, and bots for open-source online social networks, such as Mastodon, and Nostr,

Internet of Things (IoT) has been the enabling technology for using sensors and actuators to collect data, simple processing it, and taking some actions as well as communication. Hence, IoT has been commonly used in every parts of our daily lives; e.g., smart locks, smart fridges, and smart cars. However, IoT devices have brought different vulnerabilities to the cybersecurity world. This is because these smart devices do not have enough computation capabilities or have limited memory to perform complex algorithms. Consequently, these devices cannot be entirely trusted and we need to manage their activities with a focus on security. Therefore, in our work, we plan to implement the zero-trust architecture (ZTA) concept for IoT based environments. The ZTA concept was established by the National Institute of Standards and Technology (NIST) with the idea of ensuring every asset in the network is always verified and authenticated by continually monitoring the network. This work attempts to provide a continuous evaluation of the IoT device’s state within the network. The evaluation will provide us with a proactive mechanism for cyber threats.

Additive manufacturing (3D printing) has been seeing growth in recent years with the widespread use of 3D printers for production in different industries. As these systems become integrated into enterprise networks, the cybersecurity aspect of their functioning is gaining importance. In particular, there exist risks that these devices are exposed to a wide variety of data breaches. The latter range from unauthorized access to the printed designs to Stuxnet-like malware attacks. This research focuses on vulnerability and threat analysis for 3D Printers. Our ultimate goal is to introduce intrusion detection systems, which effectively address the current security challenges.

Welcome to the 19th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'2022), organized from December 5-7, 2022 in Zayed University- Abu Dhabi, United Arab Emirates (UAE). As co-chairs of the Program Committee (PC), we are delighted to introduce this year's technical program and proceedings of AICCSA'2022.

Advances in cloud computing create feasible and cost-effective solutions while improving the enterprise systems' computational resources and analysis results [1] . The global cloud storage market is worth around $30 billion in 2020, and the public cloud market will reach $ 338 billion in 2021 cloud-based apps are expanding their number has nearly tripled between 2013 and 2016, from 545 to 1427 different services [2] . However, the diverse cloud service architecture and complex distributed components will make these systems susceptible to failure and faults. Therefore, Fault Tolerance (FT) is one of the critical approaches for cloud computing to improve the reliability of cloud systems. These FT techniques will improve both the efficiency and availability of cloud applications. There are three main categories for FT: reactive, proactive, and resiliency methods. Reactive methods attempt to get the device back up when the system reaches a defect condition. When a system failure occurs, the system is configured to start from a safe and active checkpoint. Proactive approaches introduce actions to prevent defect status in the system and minimize the impact of the defect. For example, the FT system monitors computing resources like CPU and RAM to take the appropriate step to avoid failure. Lastly, resiliency methods attempt to diminish the time the cloud system takes to find a failure, such as Byzantine Fault Tolerant (BFT) systems to withstand attacks and failures.

The ubiquity and vulnerability of computer applications make them ideal places for intrusion attacks that increase in intensity and complexity. Computer applications have a relationship with various networks, physical components, host devices, and users with different roles and requirements. Therefore, securing computer applications in such a complex and dynamic cyberspace is urgent and challenging. This paper attempts to tackle the challenges by proposing a Multi-Layer Abnormal Behaviors Analysis (MLABA) framework for intrusion detection associated with three layers (i.e., system, process, and network layers) in cyberspace for characterizing their normal operations and detect any abnormal behavior that might be triggered by malicious activities. The proposed technique was evaluated on several popular applications (i.e., Firefox, Opera, Chrome, and Ruby). The experimental results demonstrate the feasibility of MLABA framework that can detect the intrusion and abuse for applications.

The usage and application areas of Unmanned Aerial Vehicles (UAVs) are increasing such as military services, live streaming events, aerial photography, agriculture, firefighting, product delivery, asset inspections, and so on owing to bringing along the many benefits with it. According to Federal Aviation Administration (FAA), 865,660 drones (or UAVs) are registered and of these, 340,247 are commercial drones, 521,819 are recreational drones, 3,594 are paper registrations [1] .

Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.

High performance computing is widely used for large-scale simulations, designs and analysis of critical problems especially through the use of cloud computing systems nowadays because cloud computing provides ubiquitous, on-demand computing capabilities with large variety of hardware configurations including GPUs and FPGAs that are highly used for high performance computing. However, it is well known that inefficient management of such systems results in excessive power consumption affecting the budget, cooling challenges, as well as reducing reliability due to the overheating and hotspots. Furthermore, considering the latest trends in the attack scenarios and crypto-currency based intrusions, security has become a major problem for high performance computing. Therefore, to address both challenges, in this paper we present an autonomic management methodology for both security and power/performance. Our proposed approach first builds knowledge of the environment in terms of power consumption and the security tools' deployment. Next, it provisions virtual resources so that the power consumption can be reduced while maintaining the required performance and deploy the security tools based on the system behavior. Using this approach, we can utilize a wide range of secure resources efficiently in HPC system, cloud computing systems, servers, embedded systems, etc.

The Internet of Things (IoT) is the new trend to make devices interact among themselves. The IoT will connect not only computers and mobile devices, but also wearable devices, smart buildings, smart cities, electrical grids, and automobiles just to name few. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require large storage and computational power than can be provided by Cloud and Fog Computing. The integration of IoT with Cloud and Fog Computing make IoT capable of processing large-scale geo-distributed information. In any IoT application, communications are critical to deliver the required information to the end user, device or application, for instance to take actions during crisis events. However, IoT communication elements such as gateways or Fog nodes, will introduce major security challenges as they contribute to increase the attack surface, preventing the IoT to deliver accurate information. In this paper, we propose a methodology to develop an Intrusion Detection System (IDS) based on Anomaly Behavior Analysis (ABA) to detect when a Fog node has been compromised. The preliminary experimental results show that our proposed approach accurately detects known and unknown anomalies due to misuses or cyber-attacks, with high detection rate and low false alarms.

Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.

Twitter is one of the key social media platforms, which is also used for cyber-crimes. Hence, monitoring and detecting the malicious activities of Twitter users is critically important for cybersecurity concerns around the globe since cybercriminals are heavily using Twitter for illegal purpose. It is increasingly common for cybercriminals signing up many accounts while masquerading different users for malicious behaviors. This fact has brought forward the issue of identifying the authors of Twitter accounts. In this paper, we propose a novel approach through a combination of feature extraction methods and then convert high dimensional data to kernel matrix for Twitter author clustering. The experimental results show that our approach can be used to effectively identify the groups among more than one hundred Twitter aliases even without knowing the number of authors.

The premise of the Internet of Things (IoT) is not only to connect computers and mobile devices, but also interconnect smart buildings, homes, and cities, as well as electrical and water grids, automobiles, and airplanes. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In order to deal with such challenges, in this paper we introduce an IoT Framework to build trustworthy and secure IoT applications and services. The framework enables developers to consider security issues at all IoT levels and integrate security algorithms with the functions and services offered in each layer instead of considering security in an ad-hoc and afterthought manner. We show the applicability of our methodology to secure and protect IoT services at cloud level.

With the advances in Internet technologies and services, the social media has been gaining excessive popularity, especially because these technologies provide anonymity where they use nicknames to post their messages. Unfortunately, the anonymity feature has been exploited by the cyber-criminals to hide their identities and their operations. Hence, there is a growing interest in cybersecurity research domain to identify the authors of malicious messages and activities. Internet Relay Chat (IRC) channels are widely used to exchange messages and information among malicious users involved in cybercrimes. In this paper, we present an autonomic author identification technique based on personality profile and analysis of IRC messages. We first monitor the IRC channels using our autonomic bots and then create a personality profile for each targeted author. We demonstrate that personality analysis for author detection/identification is an efficient approach and has high detection rates.

Computer manufacturers, software developers, and service providers spend significant time and resources to ensure that they can optimally support one class of applications. However, they cannot cope with the dynamic and continuous changes in applications and workload types, and consequently their offered services become unstable, fragile, and cannot guarantee the required Quality of Service (QoS). Furthermore, it becomes prohibitively expensive to build data centers (DCs) that are optimized for fixed types of workloads and businesses or to scale up for accommodating new growth in heterogeneous workload demands. Consequently, it is critically important that the architecture of next generation DCs is dynamically customizable to support a wide range of application types or businesses under constrained resources. In this paper, we present some design concepts for our Just In Time Architecture (JITA), a novel data center design to overcome the composable DC challenges by dynamically interconnecting DC components into a virtual DC (VDC) that is optimized to the service level objectives for each class of applications. We present how we can use optical waveguide links to build a passive crossbar that directly interconnects all DC resources at the required throughput and latency.

Smart Building (SB) exploits advances in information and communication technologies in order to provide the next generation of information and automation services that will significantly reduce operational costs and improve performance and efficiency. SB elements are typically interconnected using short range wireless communication technologies such as ZigBee, which is the most used wireless communication protocol for SBs. However, ZigBee protocol has multiple vulnerabilities that can be exploited by cyberattacks. In this paper, we present an Anomaly Behavior Analysis System (ABAS) for ZigBee protocol to be used in SBs. Our ABAS can detect both known and unknown ZigBee attacks with a high detection rate and low false alarms. Additionally, after detection, our system classifies the attack based on the impact, origin, and destination. We evaluate our approach by launching many attack scenarios such as DoS, Flooding, and Pulse DoS attacks, and then we compare our results with other intrusion detection systems such as secure HAN, signature IDS, and specification IDS.

Bottom-up self-assembly nanofabrication process yields nanodevices with significantly more variations compared to the conventional top-down lithography used in CMOS fabrication. This is in addition to an increased defect density expected for self-assembled nanodevices. Therefore, it is one of the major design challenges to tolerate variation, in addition to defect tolerance, in emerging nano architectures. In this paper, we present a solution for variation tolerant logic mapping for FET based crossbar array nano architectures using Simulated Annealing. Furthermore, we extended the framework for defect tolerance. Experimental results including comparison with exact method confirm the effectiveness of the proposed approach.

In hybrid nano-architectures, self-assembled nanoscale crossbars are fabricated on top of a reliable CMOS subsystem. Bottom-up self-assembly nanofabrication process, used in nano-architectures, yields nanodevices with significantly more variations compared to the conventional top-down lithography used in CMOS fabrication. This is in addition to an increased defect density expected for self-assembled nanodevices. Therefore, it is one of the major design challenges to tolerate variation and defects in emerging nano architectures. In this paper, we present an alternative approach for variation and defect tolerant mapping in which no application-independent test and characterization (defect and variation map) is required. The variation tolerant mapping is done on-the-fly which can ultimately be transformed into built-in self-mapping. Different mapping algorithms are presented and their efficiencies in terms of variation and defect tolerance as well as mapping time are compared. The experimental results show that the proposed heuristic mapping algorithm can achieve the same success rate with the exhaustive method in terms of meeting required timing constraints with orders of magnitude fewer reconfiguration retries.