Showing papers by "Muhammad Ali Babar published in 2019"

Real-time data processing scheme using big data analytics in internet of things based smart transportation environment

Abstract: In recent times, a massive amount of smart devices or objects are connected that enhances the scale of the digital world. These smart objects are referred as “things” or physical devices that have the potential to sense the real-world physical objects, collect the data, and network with others. The objects are connected through the internet, which crafts the terminology of Internet of Things (IoT). IoT has been developed and become the center of consideration due to the novelty of embedded device and a rapid enhancement in its number. This increase is resulting in the creative applications of smart environments. Smart transportation is a central stake for the quality of life of citizens in smart environment. Smart transportation involves the use of devices and sensors in the control system of vehicle; for example navigation system of cars, traffic signal management system, number recognition system and speed monitoring system. In this research article, we propose architecture for smart transportation system using Big Data analytics, in order to achieve real time processing and facilitate a friendly communication in the environment of IoT based smart transportation. The proposed architecture is a 3-phase scheme which is responsible for the organization and management of Big Data, real-time processing of Big Data and service management. The proposed architecture is a generic solution for the smart transportation planning using real time Big Data processing. The proposed scheme is realized using Spark over single node Hadoop setup with various input libraries. A huge amount of data from different authentic and reliable sources is measured to validate the proposed architecture. In addition, the effectiveness of proposed scheme also highlighted with regard to throughput.

A Multi-Vocal Review of Security Orchestration

Abstract: Organizations use diverse types of security solutions to prevent cyber-attacks. Multiple vendors provide security solutions developed using heterogeneous technologies and paradigms. Hence, it is a challenging rather impossible to easily make security solutions to work an integrated fashion. Security orchestration aims at smoothly integrating multivendor security tools that can effectively and efficiently interoperate to support security staff of a Security Operation Centre (SOC). Given the increasing role and importance of security orchestration, there has been an increasing amount of literature on different aspects of security orchestration solutions. However, there has been no effort to systematically review and analyze the reported solutions. We report a Multivocal Literature Review that has systematically selected and reviewed both academic and grey (blogs, web pages, white papers) literature on different aspects of security orchestration published from January 2007 until July 2017. The review has enabled us to provide a working definition of security orchestration and classify the main functionalities of security orchestration into three main areas—unification, orchestration, and automation. We have also identified the core components of a security orchestration platform and categorized the drivers of security orchestration based on technical and socio-technical aspects. We also provide a taxonomy of security orchestration based on the execution environment, automation strategy, deployment type, mode of task and resource type. This review has helped us to reveal several areas of further research and development in security orchestration.

An empirical study of architecting for continuous delivery and deployment

Abstract: Recently, many software organizations have been adopting Continuous Delivery and Continuous Deployment (CD) practices to develop and deliver quality software more frequently and reliably. Whilst an increasing amount of the literature covers different aspects of CD, little is known about the role of software architecture in CD and how an application should be (re-) architected to enable and support CD. We have conducted a mixed-methods empirical study that collected data through in-depth, semi-structured interviews with 21 industrial practitioners from 19 organizations, and a survey of 91 professional software practitioners. Based on a systematic and rigorous analysis of the gathered qualitative and quantitative data, we present a conceptual framework to support the process of (re-) architecting for CD. We provide evidence-based insights about practicing CD within monolithic systems and characterize the principle of “small and independent deployment units” as an alternative to the monoliths. Our framework supplements the architecting process in a CD context through introducing the quality attributes (e.g., resilience) that require more attention and demonstrating the strategies (e.g., prioritizing operations concerns) to design operations-friendly architectures. We discuss the key insights (e.g., monoliths and CD are not intrinsically oxymoronic) gained from our study and draw implications for research and practice.

A secured and efficient communication scheme for decentralized cognitive radio-based internet of vehicles

Abstract: The advancements in hardware technologies have driven the evolution of vehicular ad hoc networks into the Internet of Vehicles (IoV). The IoV is a decentralized network of IoT-enabled vehicles capable of smooth traffic flow to perform fleet management and accident avoidance. The IoV has many commercial applications due to improved security and safety on the roads. However, the rapidly increasing number of wireless applications have challenged the existing spectrum bands allocated to IoV. The IoV has only six communication channels that are congested during the peak hours. The limited number of channels and the presence of congestion on these channels are the challenging issues that affect the safety of vehicles on the road. To mitigate the congestion, Cognitive Radio (CR) can be an optimal solution for the existing IoV Paradigm. In this paper, we propose a secured and efficient communication scheme for a decentralized CR-based IoV (CIoV) network. In this scheme, the Roadside Unit (RSU) senses the spectrum using an energy detection method. Each vehicle independently predicts the Primary User (PU) activity pattern using a hidden Markov model (HMM). Once a vehicle detects a licensed channel free from the PUs, it informs the RSU to store the channel in a database alongside the dedicated direct short-range communication (DSRC) channels for data transmission. The RSU and vehicles are registered with a trusted authority and they mutually authenticate each other. Upon mutual authentication, the RSU assigns communication channels to the vehicles on the road, based on their density. When the density of the vehicles is high, the detected licensed channels are used, otherwise, the DSRC channels are used. We evaluate the performance of CIoV in terms of packet delivery and packet loss ratio, end-to-end delay, and throughput, using NS-2. The simulation results show that the CR-based approach of CIoV outperforms the existing schemes and significantly enhances the performance of the underlying network.

How Developers Discuss Architecture Smells? An Exploratory Study on Stack Overflow

Abstract: Architecture Smells (ASs) are design decisions that can have significant negative effects on a system's quality attributes such as reusability and testability. ASs are focused on higher level of software systems than code smells, which are implementation-level constructs. ASs can have much wider impact on a system than code smells. However, ASs usually receive less attention than code smells in both research and practice. We have conducted an exploratory study of developers' conception of ASs by analyzing related discussions in Stack Overflow. We used 14 ASs related terms to search the relevant posts in Stack Overflow and extracted 207 posts. We used Grounded Theory method for analyzing the extracted posts about developers' description of ASs, causes of ASs, approaches and tools for detecting and refactoring ASs, quality attributes affected by ASs, and difficulties in detecting and refactoring ASs. Our findings show that: (1) developers often describe ASs with some general terms; (2) ASs are mainly caused by violating architecture patterns, design principles, or misusing architecture antipatterns; (3) there is a lack of dedicated tools for detecting and refactoring ASs; (4) developers mainly concern about the maintainability and performance of systems affected by ASs; and (5) the inability to quantify the cost and benefit as well as the lack of approaches and tools makes detecting and refactoring ASs difficult.

Ethnographic research in software engineering: a critical review and checklist

Abstract: Software Engineering (SE) community has recently been investing significant amount of effort in qualitative research to study the human and social aspects of SE processes, practices, and technologies. Ethnography is one of the major qualitative research methods, which is based on constructivist paradigm that is different from the hypothetic-deductive research model usually used in SE. Hence, the adoption of ethnographic research method in SE can present significant challenges in terms of sufficient understanding of the methodological requirements and the logistics of its applications. It is important to systematically identify and understand various aspects of adopting ethnography in SE and provide effective guidance. We carried out an empirical inquiry by integrating a systematic literature review and a confirmatory survey. By reviewing the ethnographic studies reported in 111 identified papers and 26 doctoral theses and analyzing the authors' responses of 29 of those papers, we revealed several unique insights. These identified insights were then transformed into a preliminary checklist that helps improve the state-of-the-practice of using ethnography in SE. This study also identifies the areas where methodological improvements of ethnography are needed in SE.

ARCA-IoT: An Attack-Resilient Cloud-Assisted IoT System

Abstract: Putting trust in the world of the Internet of Things, where served and serving entities are often unknown, is very hard especially when personal and business information is often being exchanged for providing and consuming services. Moreover, the issues of interoperability and scalability of billions of heterogeneous things in the IoT systems require more attention. A user-centric model of complex interconnected things must be designed in a way that not only makes things trustworthy for common people but it also provides the solution for interoperability and scalability. ARCA-IoT is such a system which not only identifies the attributes (including quality of service) essential for trust but also presents a user-centric model that is robust enough to tackle the attacks made by dishonest entities to manipulate the trustworthiness. For scalability and interoperability, a cloud-assisted environment is introduced in the ARCA-IoT. An intuitive Naive Bayes approach is used to train the ARCA-IoT in a way that it calculates the probabilities of the trustworthiness of the entities and then identifies various types of attacks with the support of three proposed algorithms. The approach is validated with a specifically designed simulated environment. Based on our simulation results, the ARCA-IoT demonstrates the effectiveness in term of performance metrics such as accuracy, sensitivity, specificity, and precision. Besides this, the system outperforms the existing related approaches in terms of a qualitative analysis based on different parametric metrics such as interoperability, scalability, context-awareness, and a human-like decision.

Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Abstract: Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred to as threats. Cyber threat intelligence can be extracted from various sources including social media platforms where users publish the threat information in real-time. Gathering Cyber threat intelligence from social media sites is a time-consuming task for security analysts that can delay timely response to emerging Cyber threats. We propose a framework for automatically gathering Cyber threat intelligence from Twitter by using a novelty detection model. Our model learns the features of Cyber threat intelligence from the threat descriptions published in public repositories such as Common Vulnerabilities and Exposures (CVE) and classifies a new unseen tweet as either normal or anomalous to Cyber threat intelligence. We evaluate our framework using a purpose-built data set of tweets from 50 influential Cyber security-related accounts over twelve months (in 2018). Our classifier achieves the F1-score of 0.643 for classifying Cyber threat tweets and outperforms several baselines including binary classification models. Analysis of the classification results suggests that Cyber threat-relevant tweets on Twitter do not often include the CVE identifier of the related threats. Hence, it would be valuable to collect these tweets and associate them with the related CVE identifier for Cyber security applications.

An ontology-driven approach to automating the process of integrating security software systems

Abstract: A wide variety of security software systems need to be integrated into a Security Orchestration Platform (SecOrP) to streamline the processes of defending against and responding to cybersecurity attacks. Lack of interpretability and interoperability among security systems are considered the key challenges to fully leverage the potential of the collective capabilities of different security systems. The processes of integrating security systems are repetitive, time-consuming and error-prone; these processes are carried out manually by human experts or using ad-hoc methods. To help automate security systems integration processes, we propose an Ontology-driven approach for Security OrchestrAtion Platform (OnSOAP). The developed solution enables interpretability, and interoperability among security systems, which may exist in operational silos. We demonstrate OnSOAP's support for automated integration of security systems to execute the incident response process with three security systems (Splunk, Limacharlie, and Snort) for a Distributed Denial of Service (DDoS) attack. The evaluation results show that OnSOAP enables SecOrP to interpret the input and output of different security systems, produce error-free integration details, and make security systems interoperable with each other to automate and accelerate an incident response process.

Internet of Things–Based Smart City Environments Using Big Data Analytics: A Survey

Abstract: The intense growth and acceptance of the Internet of Things (IoT) is reflected in the trend of smart cities. Smart cities are being implemented to improve standards of living and provide higher-quality services to residents. These services may include (but are not limited to) parking, water, health, transportation, environment, and power. The varied implementations of smart cities and the IoT are challenged by the processing of gigantic data and real-time decision management. In this chapter, we explore the use of big data analytics in IoT-based smart city development and design. This chapter provides a conceptual framework for the use of big data analytics in IoT-based smart city environments.

An Architecture-Driven Adaptation Approach for Big Data Cyber Security Analytics

Abstract: Big Data Cyber Security Analytics (BDCA) systems leverage big data technologies (e.g., Hadoop and Spark) for collecting, storing, and analyzing large volume of security event data to detect cyber-attacks. Accuracy and response time are the two most important quality concerns for BDCA systems. However, the frequent changes in the operating environment of a BDCA system (such as quality and quantity of security event data) significantly impact these qualities. In this paper, we first study the impact of such environmental changes. We then present ADABTics, an architecture-driven adaptation approach that (re)composes the system at runtime with a set of components to ensure optimal accuracy and response time. We finally evaluate our approach both in a single node and multinode settings using a Hadoop-based BDCA system and different adaptation scenarios. Our evaluation shows that on average ADABTics improves BDCA's accuracy and response time by 6.06% and 23.7% respectively.

A Review on Requirements Engineering for Internet of Things (loT) Applications

Abstract: Organizations and customers are adopting Internet of things (loT) due to many advantages that have impacted human life in various ways - smart homes, smart transportation, smart parking, and many smart applications in the medical, defense and other sectors. As these loT applications are emerging rapidly, there is a need to focus on software development life cycle (SDLC) of these systems to ensure necessary levels of quality. Requirements engineering for loT based systems did not get that much attention in its early years and thus has not been well explored by the Software Requirements Engineering (SRE) research community. Hence, it required to identify and recognize the challenges in loT development especially from the SRE perspectives. This article presents a comprehensive and inclusive overview of the issues pertaining to requirements engineering in the context of loT-based smart applications. Each issue pertaining to RE of loT based system has been evaluated using parameters based on the existing literature.

Agile Approaches for Teaching and Learning Software Architecture Design Processes and Methods

Abstract: Software architecture plays a vital role in the analysis, design, evaluation and evolution of large-scale projects. Successful adoption of an agile methodology in large-scale projects requires not only tailoring of the software architecture analysis, design and evaluation methods but also a fundamental understanding of these methods. In this chapter, we provide agile teaching and learning approaches for software architecture analysis, design and evaluation. In particular, we focus on agile teams in architecturally significant (quality) requirements analysis and change management for collocated and distributed agile projects, iterative and continuous architecture design delivery using story boards and collaboration platforms, and using software reference architectures to monitor and control the design and evolution of a software architecture. The methods presented in this chapter are based upon the following research methods. We have explored the literature to identify key characteristics of agile software architecture processes and roles of agile teams in software architecture. We have presented agile teaching and learning approaches with reference to the case studies conducted in classes over 2 years of software architecture courses. We have specifically focused on designing course activities that can support lean education and collaboration among the students and course instructors. We foresee that the presented approaches can be used by academics to teach software architecture design methods and processes in particular, and software engineering techniques in general. Practitioners can also take advantage of the proposed approaches to continuously educate their staff when applying agile methods for architecture design and evolution of complex software systems.

Internet of Things Based Smart Community Design and Planning Using Hadoop-Based Big Data Analytics

Abstract: The current spreading out in big data is offering a hefty invention potential in itinerary of the fresh epoch of smart community. The foremost endeavor of smart community is to competently employ the asset of Big Data to manage and determine the issues face by recent smart cities for enhanced decision making. The applications of smart city fabricate a gigantic number of data that compose Big Data. This research proposes Big Data analytics architecture to address the challenges in Big Data analytics using Hadoop framework. The proposed framework is dealing particularly with data loading and processing. The proposal is consist of two parts that are Big Data loading (storage) in Hadoop file system and Big Data computation. The first part is liable for transferring Big Data from outer world and storing in Hadoop. The second part of the research deals with the data processing. YARN-based cluster management solution is provided to manage the cluster resource and process the data using Map-Reduce algorithm separately unlike traditional MapReduce architecture. The proposed architecture is tested with a variety of reliable datasets using Hadoop framework to verify and expose that the architecture offers precious imminent into the society organizations for development to improve the existing smart city architecture.

Game theoretical modelling of network/cyber security [Review paper].

Abstract: Game theory is an established branch of mathematics that offers a rich set of mathematical tools for multi-person strategic decision making that can be used to model the interactions of decision makers in security problems who compete for limited and shared resources. This article presents a review of the literature in the area of game theoretical modelling of network/cybersecurity.

Managing and Processing Information in the Internet of Things-Based Smart City Environment Using Big Data Analytics

Abstract: The extensive growth of the Internet of Things (IoT) is giving the direction toward the smart cities. The smart city is preferred because it improves the living standard of the people of the society and provides quality in the services. These services are parking, health, transport, water, power, environment, and so forth. The assorted environment of IoT and smart city is challenged by data processing, decision-making, and notification management. In this research article, specific architecture is proposed for data processing and notification management in the smart city environment using IoT. The processing is carried out with Hadoop server using authentic dataset, and notification management is done based on ontology.