arXiv subjects

Asha Rao

Publications and source records attributed to Asha Rao.

At least 19 recordsLinked to original sources

arXiv2025

Generative AI in Collaborative Academic Report Writing: Advantages, Disadvantages, and Ethical Considerations

The availability and abundance of GenAI tools to administer tasks traditionally managed by people have raised concerns, particularly within the education and academic sectors, as some students may highly rely on these tools to complete the assignments designed to enable learning. This article focuses on informing students about the significance of investing their time during their studies on developing essential life-long learning skills using their own critical thinking, rather than depending on AI models that are susceptible to misinformation, hallucination, and bias. As we transition to an AI-centric era, it is important to educate students on how these models work, their pitfalls, and the ethical concerns associated with feeding data to such tools.

cs.CY↗

arXiv2025

Post-Quantum Homomorphic Encryption: A Case for Code-Based Alternatives

Homomorphic Encryption (HE) allows secure and privacy-protected computation on encrypted data without the need to decrypt it. Since Shor's algorithm rendered prime factorisation and discrete logarithm-based ciphers insecure with quantum computations, researchers have been working on building post-quantum homomorphic encryption (PQHE) algorithms. Most of the current PQHE algorithms are secured by Lattice-based problems and there have been limited attempts to build ciphers based on error-correcting code-based problems. This review presents an overview of the current approaches to building PQHE schemes and justifies code-based encryption as a novel way to diversify post-quantum algorithms. We present the mathematical underpinnings of existing code-based cryptographic frameworks and their security and efficiency guarantees. We compare lattice-based and code-based homomorphic encryption solutions identifying challenges that have inhibited the progress of code-based schemes. We finally propose five new research directions to advance post-quantum code-based homomorphic encryption.

cs.CR↗

arXiv2025

Analysis of the Impact of the Union Budget Announcements on the Indian Stock Market: A Fractal Perspective

The stock market closely monitors macroeconomic policy announcements, such as annual budget events, due to their substantial influence on various economic participants. These events tend to impact the stock markets initially before affecting the real sector. Our study aims to analyze the effects of the budget on the Indian stock market, specifically focusing on the announcement for the year 2024. We will compare this with the years 2023, 2022, and 2020, assessing its impact on the NIFTY50 index using average abnormal return (AAR) and cumulative average abnormal return (CAAR) over a period of -15 and +15 days, including the budget day. This study utilizes an innovative approach involving the fractal interpolation function, paired with fractal dimensional analysis, to study the fluctuations arising from budget announcements. The fractal perspective on the data offers an effective framework for understanding complex variations.

q-fin.ST↗

arXiv2024

Gender of Recruiter Makes a Difference: A study into Cybersecurity Graduate Recruitment

An ever-widening workforce gap exists in the global cybersecurity industry but diverse talent is underutilized. The global cybersecurity workforce is only 25% female. Much research exists on the effect of gender bias on the hiring of women into the technical workforce, but little on how the gender of the recruiter (gender difference) affects recruitment decisions. This research reveals differences between the non-technical skills sought by female vs non-female cybersecurity recruiters. The former look for recruits with people-focused skills while the latter look for task-focused skills, highlighting the need for gender diversity in recruitment panels. Recruiters are increasingly seeking non-technical (soft) skills in technical graduate recruits. This requires STEM curriculum in Universities to adapt to match. Designing an industry-ready cybersecurity curriculum requires knowledge of these non-technical skills. An online survey of cybersecurity professionals was used to determine the most sought after non-technical skills in the field. Analysis of the data reveals distinct gender differences in the non-technical skills most valued in a recruit, based on the gender of the recruiter (not the recruited). The gender differences discovered do not correspond to the higher proportion of women employed in non-technical cybersecurity roles.

cs.CY↗

arXiv2024

Structuring the Chaos: Enabling Small Business Cyber-Security Risks & Assets Modelling with a UML Class Model

Small businesses are increasingly adopting IT, and consequently becoming more vulnerable to cyber-incidents. Whilst small businesses are aware of the cyber-security risks, many struggle with implementing mitigations. Some of these can be traced to fundamental differences in the characteristics of small business versus large enterprises where modern cyber-security solutions are widely deployed. Small business specific cyber-security tools are needed. Currently available cyber-security tools and standards assume technical expertise and time resources often not practical for small businesses. Cyber-security competes with other roles that small business owners take on, e.g. cleaning, sales etc. A small business model, salient and implementable at-scale, with simplified non-specialist terminologies and presentation is needed to encourage sustained participation of all stakeholders, not just technical ones. We propose a new UML class (Small IT Data (SITD)) model to support the often chaotic information-gathering phase of a small business' first foray into cyber-security. The SITD model is designed in the UML format to help small business implement technical solutions. The SITD model structure stays relevant by using generic classes and structures that evolve with technology and environmental changes. The SITD model keeps security decisions proportionate to the business by highlighting relationships between business strategy tasks and IT infrastructure. We construct a set of design principles to address small business cyber-security needs. Model components are designed in response to these needs. The uses of the SITD model are then demonstrated and design principles validated by examining a case study of a real small business operational and IT information. The SITD model's ability to illustrate breach information is also demonstrated using the NotPetya incident.

cs.CR↗

arXiv2023

Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory

Continuously evolving cyber-attacks against industrial networks reduce the effectiveness of signature-based detection methods. Once malware has infiltrated a network (for example, entering via an unsecured device), it can infect further network nodes and carry out malicious activity. Infected nodes can exhibit unusual behaviour in their use of Address Resolution Protocol (ARP) calls within the network. In order to detect such anomalous nodes, we propose a two-stage method: (i) modelling of ARP call behaviour via hierarchical time series prediction methods, and (ii) exploiting Extreme Value Theory (EVT) to robustly detect whether deviations from expected behaviour are anomalous. EVT is able to handle heavy-tailed distributions which are exhibited by internet traffic. Empirical evaluations on a real-life dataset containing over 10M ARP calls from 362 nodes show that the proposed method results in considerably reduced number of false positives, addressing the problem of alert fatigue commonly reported by security professionals.

cs.CR↗

arXiv2022

QC-LDPC Codes from Difference Matrices and Difference Covering Arrays

We give a framework for generalizing LDPC code constructions that use Transversal Designs or related structures such as mutually orthogonal Latin squares. Our construction offers a broader range of code lengths and codes rates. Similar earlier constructions rely on the existence of finite fields of order a power of a prime. In contrast the LDPC codes constructed here are based on difference matrices and difference covering arrays, structures available for any order $a$. They satisfy the RC constraint and have, for $a$ odd, length $a^2$ and rate $1-\frac{4a-3}{a^2}$, and for $a$ even, length $a^2-a$ and rate at least $1-\frac{4a-6}{a^2-a}$. When $3$ does not divide $a$, these LDPC codes have stopping distance at least $8$. When $a$ is odd and both $3$ and $5$ do not divide $a$, our construction delivers an infinite family of QC-LDPC codes with minimum distance at least $10$. The simplicity of the construction allows us to theoretically verify these properties and analytically determine lower bounds for the minimum distance and stopping distance of the code. The BER and FER performance of our codes over AWGN (via simulation) is at the least equivalent to codes constructed previously, while in some cases significantly outperforming them.

math.CO↗

arXiv2021

Honeyboost: Boosting honeypot performance with data fusion and anomaly detection

With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Systems (NADS) to identify unusual behavior makes them useful in predicting such attacks. However, NADS often suffer from high false positive rates. In this paper, we introduce a novel framework called Honeyboost that enhances the performance of honeypot aided NADS. Using data from the LAN Security Monitoring Project, Honeyboost identifies most anomalous nodes before they access the honeypot aiding early detection and prediction. Furthermore, using extreme value theory, we achieve the highly desirable low false positive rates. Honeyboost is an unsupervised method comprising two approaches: horizontal and vertical. The horizontal approach constructs a time series from the communications of each node, with node-level features encapsulating their behavior over time. The vertical approach finds anomalies in each protocol space. Using a window-based model, which is typically used in online scenarios, the horizontal and vertical approaches are combined to identify anomalies and gain useful insights. Experimental results indicate the efficacy of our framework in identifying suspicious activities of nodes.

cs.CR↗

arXiv2021

The Good, The Bad and The Missing: A Narrative Review of Cyber-security Implications for Australian Small Businesses

Small businesses (0-19 employees) are becoming attractive targets for cyber-criminals, but struggle to implement cyber-security measures that large businesses routinely deploy. There is an urgent need for effective and suitable cyber-security solutions for small businesses as they employ a significant proportion of the workforce. In this paper, we consider the small business cyber-security challenges not currently addressed by research or products, contextualised via an Australian lens. We also highlight some unique characteristics of small businesses conducive to cyber-security actions. Small business cyber-security discussions to date have been narrow in focus and lack re-usability beyond specific circumstances. Our study uses global evidence from industry, government and research communities across multiple disciplines. We explore the technical and non-technical factors negatively impacting a small business' ability to safeguard itself, such as resource constraints, organisational process maturity, and legal structures. Our research shows that some small business characteristics, such as agility, large cohort size, and piecemeal IT architecture, could allow for increased cyber-security. We conclude that there is a gap in current research in small business cyber-security. In addition, legal and policy work are needed to help small businesses become cyber-resilient.

cs.CR↗

arXiv2016

Active Fault Tolerant Flight Control System Design - A UAV Case Study

Fault tolerance is achieved through multiply redundant hardware systems in large civil aircraft. This means of achieving fault tolerance is infeasible for small compact unmanned aerial vehicles. In this paper we apply a fault tolerant control system which exploits analytical redundancy rather than hardware redundancy to an actual UAV model currently in operation via model-in-the-loop simulation. The fault tolerant control system comprises a nonlinear model predictive controller integrated with an unscented Kalman filter for fault detection and identification. The results show that our fault tolerant control system design is able to identify engine failure within seconds of fault occurrence and distribute control authority to the healthy actuators to maintain safe flight.

math.OC↗

arXiv2016

Designing a Nonlinear Model Predictive Controller for Fault Tolerant Flight Control

This paper describes the design process for developing a nonlinear model predictive controller for fault tolerant flight control. After examining and implementing a number of numerical techniques, this paper identifies pseudospectral discretisation as the most suitable for this design. Applying the controller to a 2D robot model shows that the nonlinear controller performs much better than the linear controller, especially in the closed loop scenario. Assuming fault detection information, applying the technique to the longitudinal motion of a generic aircraft model shows the design to be eminently suitable for flight control.

math.OC↗

arXiv2016

Active Fault Tolerant Flight Control System Design

In this paper we investigate the design of an active fault tolerant control system applicable to autonomous flight. The system comprises a nonlinear model predictive based controller integrated with an unscented Kalman filter for fault detection and identification. We apply the fault tolerant control system design to a generic aircraft model, and simulate a failed engine scenario. The results show that the system correctly identifies the fault within seconds of occurrence and updates the nonlinear model predictive controller which is then able to reallocate control authority to the healthy actuators based upon up to date fault information.

math.OC↗

arXiv2016

Fault Detection and Identification - a Filter Investigation

This paper develops a new active fault tolerant control system based on the concept of analytical redundancy. The novel design consists of an observation filter based fault detection and identification system integrated with a nonlinear model predictive controller. A number of observation filters were designed, integrated with the nonlinear controller and tested before reaching the final design which comprises an unscented Kalman filter for fault detection and identification together with a nonlinear model predictive controller to form an active fault tolerant control system design.

math.OC↗

arXiv2016

Detecting Anomalous User Behavior Using an Extended Isolation Forest Algorithm: An Enterprise Case Study

Anomalous user behavior detection is the core component of many information security systems, such as intrusion detection, insider threat detection and authentication systems. Anomalous behavior will raise an alarm to the system administrator and can be further combined with other information to determine whether it constitutes an unauthorised or malicious use of a resource. This paper presents an anomalous user behaviour detection framework that applies an extended version of Isolation Forest algorithm. Our method is fast and scalable and does not require example anomalies in the training data set. We apply our method to an enterprise dataset. The experimental results show that the system is able to isolate anomalous instances from the baseline user model using a single feature or combined features.

cs.CR↗

arXiv2016

Fast extraction of the backbone of projected bipartite networks to aid community detection

This paper introduces a computationally inexpensive method of extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in the one-mode projections are distributed according to a Poisson binomial distribution and that finding the expected weight distribution of a one-mode network projected from a random bipartite network only requires knowledge of the bipartite degree distributions. Being able to extract the backbone of a projection is highly beneficial in filtering out redundant information in large complex networks and narrowing down the information in the one-mode projection to the most relevant. We demonstrate that the backbone of a one-mode projection aids in the detection of communities.

cs.SI↗

arXiv2016

Ranking the Importance Level of Intermediaries to a Criminal using a Reliance Measure

Recent research on finding important intermediate nodes in a network suspected to contain criminal activity is highly dependent on network centrality values. Betweenness centrality, for example, is widely used to rank the nodes that act as brokers in the shortest paths connecting all source and all the end nodes in a network. However both the shortest path node betweenness and the linearly scaled betweenness can only show rankings for all the nodes in a network. In this paper we explore the mathematical concept of pair-dependency on intermediate nodes, adapting the concept to criminal relationships and introducing a new source-intermediate reliance measure. To illustrate our measure, we apply it to rank the nodes in the Enron email dataset and the Noordin Top Terrorist networks. We compare the reliance ranking with Google PageRank, Markov centrality as well as betweenness centrality and show that a criminal investigation using the reliance measure, will lead to a different prioritisation in terms of possible people to investigate. While the ranking for the Noordin Top terrorist network nodes yields more extreme differences than for the Enron email transaction network, in the latter the reliance values for the set of finance managers immediately identified another employee convicted of money laundering.

cs.SI↗

arXiv2015

An Algorithm for constructing Hjelmslev planes

Projective Hjelmslev planes and Affine Hjelmselv planes are generalisations of projective planes and affine planes. We present an algorithm for constructing a projective Hjelmslev planes and affine Hjelsmelv planes using projective planes, affine planes and orthogonal arrays. We show that all 2-uniform projective Hjelmslev planes, and all 2-uniform affine Hjelsmelv planes can be constructed in this way. As a corollary it is shown that all 2-uniform Affine Hjelmselv planes are sub-geometries of 2-uniform projective Hjelmselv planes.

math.CO↗

arXiv2015

Using shortest path to discover criminal community

Extracting communities using existing community detection algorithms yields dense sub-networks that are difficult to analyse. Extracting a smaller sample that embodies the relationships of a list of suspects is an important part of the beginning of an investigation. In this paper, we present the efficacy of our shortest paths network search algorithm (SPNSA) that begins with an "algorithm feed", a small subset of nodes of particular interest, and builds an investigative sub-network. The algorithm feed may consist of known criminals or suspects, or persons of influence. This sets our approach apart from existing community detection algorithms. We apply the SPNSA on the Enron Dataset of e-mail communications starting with those convicted of money laundering in relation to the collapse of Enron as the algorithm feed. The algorithm produces sparse and small sub-networks that could feasibly identify a list of persons and relationships to be further investigated. In contrast, we show that identifying sub-networks of interest using either community detection algorithms or a k-Neighbourhood approach produces sub-networks of much larger size and complexity. When the 18 top managers of Enron were used as the algorithm feed, the resulting sub-network identified 4 convicted criminals that were not managers and so not part of the algorithm feed. We also directly tested the SPNSA by removing one of the convicted criminals from the algorithm feed and re-running the algorithm; in 5 out of 9 cases the left out criminal occurred in the resulting sub-network.

cs.SI↗