Faheem Ullah

Faheem Ullah, Muhammad Ali Babar

Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Apache Spark) to collect, store, and analyze a large volume of security event data for detecting cyber-attacks. The volume of digital data in general and security event data in specific is increasing exponentially. The velocity with which the security event data is generated and fed into a BDCA system is unpredictable. Therefore, a BDCA system should be highly scalable to deal with the unpredictable increase/decrease in the velocity of security event data. However, there has been little effort to investigate the scalability of BDCA systems to identify and exploit the sources of scalability improvement. In this paper, we first investigate the scalability of a Spark-based BDCA system with default Spark settings. we then identify Spark configuration parameters (e.g., execution memory) that can significantly impact the scalability of a BDCA system. Based on the identified parameters, we finally propose a parameter-driven adaptation approach, SCALER, for optimizing a system's scalability. We have conducted a set of experiments by implementing a Spark-based BDCA system on a large-scale OpenStack cluster. We ran our experiments with four security datasets. We have found that (i) a BDCA system with default Spark configuration parameters deviates from ideal scalability by 59.5% (ii) 9 out of 11 studied Spark configuration parameters significantly impact scalability (iii) SCALER improves the BDCA system's scalability by 20.8% compared to the scalability with default Spark parameter setting. The findings of our study highlight the importance of exploring the parameter space of the underlying big data framework (e.g., Apache Spark) for scalable cyber security analytics.

Xuanyu Duan, Mengmeng Ge, Triet H. M. Le et al.

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and potential vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.

Hussain Ahmad, Isuru Dharmadasa, Faheem Ullah et al.

Command, Control, Communication, and Intelligence (C3I) systems are increasingly used in critical civil and military domains for achieving information superiority, operational efficacy, and greater situational awareness. Unlike traditional systems facing widespread cyber-attacks, the sensitive nature of C3I tactical operations make their cybersecurity a critical concern. For instance, tampering or intercepting confidential information in military battlefields not only damages C3I operations, but also causes irreversible consequences such as loss of human lives and mission failures. Therefore, C3I systems have become a focal point for cyber adversaries. Moreover, technological advancements and modernization of C3I systems have significantly increased the potential risk of cyber-attacks on C3I systems. Consequently, cyber adversaries leverage highly sophisticated attack vectors to exploit security vulnerabilities in C3I systems. Despite the burgeoning significance of cybersecurity for C3I systems, the existing literature lacks a comprehensive review to systematize the body of knowledge on C3I systems' security. Therefore, in this paper, we have gathered, analyzed, and synthesized the state-of-the-art on the cybersecurity of C3I systems. In particular, this paper has identified security vulnerabilities, attack vectors, and countermeasures/defenses for C3I systems. Furthermore, our survey has enabled us to: (i) propose a taxonomy for security vulnerabilities, attack vectors and countermeasures; (ii) interrelate attack vectors with security vulnerabilities and countermeasures; and (iii) propose future research directions for advancing the state-of-the-art on the cybersecurity of C3I systems.

Bushra Sabir, Faheem Ullah, M. Ali Babar et al.

Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective: This paper aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. Method: We used a Systematic Literature Review (SLR) method to select and review {92} papers. Results: The review has enabled us to (a) classify the ML approaches used in the countermeasures into data-driven, and behaviour-driven approaches, (b) categorize features into six types: behavioural, content-based, statistical, syntactical, spatial and temporal, (c) classify the evaluation datasets into simulated, synthesized, and real datasets and (d) identify 11 performance measures used by these studies. Conclusion: We conclude that: (i) the integration of data-driven and behaviour-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) the use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks.

Faheem Ullah, M. Ali Babar

Context: Big Data Cybersecurity Analytics is aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity Analytic Systems have been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective. Objective: This paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for Big Data Cybersecurity Analytic Systems. Method: We used Systematic Literature Review (SLR) method for reviewing 74 primary studies selected using well-defined criteria. Results: Our findings are twofold: (i) identification of 12 most frequently reported quality attributes and the justification for their significance for Big Data Cybersecurity Analytic Systems; and (ii) identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with Big Data Cybersecurity Analytic systems. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each. Conclusion: Our findings have revealed that (a) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support in the literature (b) empirical investigation is required to evaluate the impact of codified architectural tactics (c) a good deal of research effort should be invested to explore the trade-offs and dependencies among the identified tactics and (d) there is a general lack of effective collaboration between academia and industry for supporting the field of Big Data Cybersecurity Analytic Systems.

Faheem Ullah, Adam Johannes Raft, Mojtaba Shahin et al.

Continuous Deployment (CD) has emerged as a new practice in the software industry to continuously and automatically deploy software changes into production. Continuous Deployment Pipeline (CDP) supports CD practice by transferring the changes from the repository to production. Since most of the CDP components run in an environment that has several interfaces to the Internet, these components are vulnerable to various kinds of malicious attacks. This paper reports our work aimed at designing secure CDP by utilizing security tactics. We have demonstrated the effectiveness of five security tactics in designing a secure pipeline by conducting an experiment on two CDPs - one incorporates security tactics while the other does not. Both CDPs have been analyzed qualitatively and quantitatively. We used assurance cases with goal-structured notations for qualitative analysis. For quantitative analysis, we used penetration tools. Our findings indicate that the applied tactics improve the security of the major components (i.e., repository, continuous integration server, main server) of a CDP by controlling access to the components and establishing secure connections.