An Evasion Attack against ML-based Phishing URL Detectors

Background: Over the year, Machine Learning Phishing URL classification (MLPU) systems have gained tremendous popularity to detect phishing URLs proactively. Despite this vogue, the security vulnerabilities of MLPUs remain mostly unknown. Aim: To address this concern, we conduct a study to understand the test time security vulnerabilities of the state-of-the-art MLPU systems, aiming at providing guidelines for the future development of these systems. Method: In this paper, we propose an evasion attack framework against MLPU systems. To achieve this, we first develop an algorithm to generate adversarial phishing URLs. We then reproduce 41 MLPU systems and record their baseline performance. Finally, we simulate an evasion attack to evaluate these MLPU systems against our generated adversarial URLs. Results: In comparison to previous works, our attack is: (i) effective as it evades all the models with an average success rate of 66% and 85% for famous (such as Netflix, Google) and less popular phishing targets (e.g., Wish, JBHIFI, Officeworks) respectively; (ii) realistic as it requires only 23ms to produce a new adversarial URL variant that is available for registration with a median cost of only $11.99/year. We also found that popular online services such as Google SafeBrowsing and VirusTotal are unable to detect these URLs. (iii) We find that Adversarial training (successful defence against evasion attack) does not significantly improve the robustness of these systems as it decreases the success rate of our attack by only 6% on average for all the models. (iv) Further, we identify the security vulnerabilities of the considered MLPU systems. Our findings lead to promising directions for future research. Conclusion: Our study not only illustrate vulnerabilities in MLPU systems but also highlights implications for future study towards assessing and improving these systems.Comment: Draft for ACM TOP

Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT)

Transformer-based text classifiers like BERT, Roberta, T5, and GPT-3 have shown impressive performance in NLP. However, their vulnerability to adversarial examples poses a security risk. Existing defense methods lack interpretability, making it hard to understand adversarial classifications and identify model vulnerabilities. To address this, we propose the Interpretability and Transparency-Driven Detection and Transformation (IT-DT) framework. It focuses on interpretability and transparency in detecting and transforming textual adversarial examples. IT-DT utilizes techniques like attention maps, integrated gradients, and model feedback for interpretability during detection. This helps identify salient features and perturbed words contributing to adversarial classifications. In the transformation phase, IT-DT uses pre-trained embeddings and model feedback to generate optimal replacements for perturbed words. By finding suitable substitutions, we aim to convert adversarial examples into non-adversarial counterparts that align with the model's intended behavior while preserving the text's meaning. Transparency is emphasized through human expert involvement. Experts review and provide feedback on detection and transformation results, enhancing decision-making, especially in complex scenarios. The framework generates insights and threat intelligence empowering analysts to identify vulnerabilities and improve model robustness. Comprehensive experiments demonstrate the effectiveness of IT-DT in detecting and transforming adversarial examples. The approach enhances interpretability, provides transparency, and enables accurate identification and successful transformation of adversarial inputs. By combining technical analysis and human expertise, IT-DT significantly improves the resilience and trustworthiness of transformer-based text classifiers against adversarial attacks

Towards Robust Machine Learning-based Cybersecurity: Investigating Adversarial Evasion Attacks in Data Exfiltration Systems

In 2020, cyberattacks ranked fifth among security risks linked to digitalization in businesses, with their prevalence rising across public and private sectors. This escalation continues in 2023 and is projected to double by 2025. Data Exfiltration (DE), a sophisticated cyberattack, has become a critical concern, comprising 52% of security incidents from 2021 to 2023. DE involves unauthorized attempts to steal sensitive data, threatening confidentiality and integrity. Given the complexity of DE attacks, Machine Learning-based Data Exfiltration (MDE) countermeasures have been increasingly adopted for accurate DE detection and mitigation. However, recent research has revealed that ML-based systems are vulnerable to adversarial evasion attacks designed to cause misclassification. Despite substantial research into adversarial evasion attacks in various domains, their prevalence and impact on MDE defences have not been adequately studied. Nevertheless, MDE evasion can lead to successful data breaches and, consequently, reputation and financial losses. Therefore, it is essential to examine this aspect to strengthen the robustness of MDE countermeasures comprehensively and effectively. This thesis aims to enhance the robustness of MDE countermeasures against adversarial evasion attacks, contributing to ML and cybersecurity. First, a Systematic Literature Review (SLR) is conducted to understand the design and development processes of MDE systems, identifying their methods, strengths, limitations, and constraints. One significant challenge discovered through the SLR is the lack of adversarial evaluation in these systems, hindering their practical applicability and reliability. Consequently, two frameworks, namely URLBUG and ReinforceBug, are proposed to address this challenge. URLBUG and ReinforceBug frameworks assess the robustness of two popular MDE countermeasures: Machine Learning-based Phishing URL detectors (MLPU) and Context-Inspection-based MDE (CMDE) defences. The methodological core of these frameworks revolves around generating adversarial examples, effectively simulating realistic evasion attacks, and systematically evaluating the robustness of the targeted MDE systems. The findings of this evaluation reveal significant security vulnerabilities in these systems and highlight the need for robust and trustworthy MDE solutions. Based on these findings, this thesis demonstrates methods for designing robust MLPU models and developing a novel human-centric defense mechanism called Explainability-Driven Detection, Identification and Transformation (EDIT) to defend CMDE systems from evasion attacks proactively. The extensive evaluation exhibits the effectiveness of these defence mechanisms in mitigating evasion attacks, providing valuable insights for future research. The thesis serves as a valuable resource and guide for practitioners and researchers in the ML and cybersecurity domain, offering implications for developing trustworthy and robust MDE systems and advancing the field.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

Effect of compound Unani Drug in the management of cervical spondylosis (Wajaur Raqaba): A case study

Cervical spondylosis is also known as cervical osteoarthritis. It is a disorder characterised by alterations in the bones, discs, and joints of the neck. These changes are induced by the regular wear and tear of ageing, which leads to intervertebral disc degeneration and osteophyte production. The most common complaints are pain in the head, neck, and shoulders, as well as tenderness in these areas. There is also pain radiation and a reduction in cervical range of motion. Wajaur Raqaba (cervical spondylosis) is treated through Ilaj bit Tadbeer (Regional therapy), Ilaj bid Dawa (Pharmacotherapy), and Ilaj bil Yad (Surgery). The purpose of this case study was to assess the efficacy of Unani formulations Habbe Asgand and Habbe Gul-e-akh in the treatment of cervical spondylosis. A 24-year-old female patient with cervical spondylosis presented to the OPD of Ajmal Khan Tibbia College, Aligarh. Treatment was given to the patient for a period of one month. The Northwick Park Neck Pain Questionnaire (NPQ) is used for the assessment of cervical pain. As assessed by NPQ, Unani formulations were proven to be safe and effective in the management of cervical spondylosis. Keywords Cervical spondylosis, Wajaur Raqaba, Unani formulations, Habbe suranjan and Habbe gul-e-akh

Bacteremia associated with central line infection by chryseomonas luteola in a case of recurrent meningiomas

A 52 years old diabetic and asthmatic lady was admitted with a post-operative wound infection 10 days following removal of meningioma. The patient had a history of recurrent meningiomas for which she had undergone multiple surgeries during the past ten years. On admission, the patient was febrile and drowsy. There surgical wound site over the scalp was swollen, exuding a pussy discharge. Subsequently, a lumbar drain was inserted for CSF drainage, the yellowish discharge from the wound was sent for culture, which grew Streptococcus pyogenes for which I/V Ceftriaxone was started. The patient improved and remained stable till about the 25th day of hospital stay when she developed fever, chest infiltrates as well as copious pussy discharge from the wound. Due to rapid deterioration in patient`s condition she was shifted to ICU. Piperacillin tazobactam was started and lumbar drain was removed. The scalp wound was re-explored and a flap closure was done; an epidural drain was inserted for CSF drainage. As the patient did not improve clinically, all antibiotics were stopped and patient was rescreened for infection. One set of blood culture drown from a peripheral vein and the tip of pulmonary artery catheter grew Chryseomonas luteola. This organism was sensitive only to Ofloxacin and the patient`s antibiotic regimen was changed to Ofloxacin along with Aztreonam and Amikacin. The patient gradually improved on this regimen, was moved out of the ICU and subsequently managed in the ward

Reptile Search Algorithm (RSA)-Based Selective Harmonic Elimination Technique in Packed E-Cell (PEC-9) Inverter

The multilevel inverters (MLIs) are capable of handling large quantities of power and generating high-quality output voltages. Consequently, the size of the filters is reduced, and the circuitry is simplified. As a result, they have a diverse range of uses in the industrial sector, especially in smart grids. The input voltage boosting feature is required to utilize the MLI with renewable energy. In addition, a large number of components are required to attain higher output voltage levels, which increases the cost of the circuit and weight. A variety of MLI topologies have been identified to reduce losses, device quantity, and device ratings. The selective harmonic elimination (SHE) approaches reduce distinct lower order harmonics by computing the ideal switching angles. This research presents a nine–level Packed E–Cell (PEC–9) inverter that uses selective harmonic elimination to eliminate total harmonic distortion. In order to calculate the best switching angle, the reptile search algorithm (RSA) is implemented in this paper, a nature–inspired metaheuristic algorithm inspired by the hunting behavior of the crocodile. The hunting behavior of crocodiles is implemented in two main steps: the first is encircling, which is accomplished by belly walking or high walking, and the second is hunting, which is accomplished by hunting cooperation or hunting coordination. In this technique, nonlinear transcendental equations have been solved. The simulation was run in the MATLAB R2021b software environment. The simulation results suggest that the RSA outperforms the other metaheuristic algorithms. Furthermore, the simulation result was validated on a hardware setup using DSP–TMS320F28379D in the laboratory

A neural network computational structure for the fractional order breast cancer model

Abstract The current study provides the numerical performances of the fractional kind of breast cancer (FKBC) model, which are based on five different classes including cancer stem cells, healthy cells, tumor cells, excess estrogen, and immune cells. The motive to introduce the fractional order derivatives is to present more precise solutions as compared to integer order. A stochastic computing reliable scheme based on the Levenberg Marquardt backpropagation neural networks (LMBNNS) is proposed to solve three different cases of the fractional order values of the FKBC model. A designed dataset is constructed by using the Adam solver in order to reduce the mean square error by taking the data performances as 9% for both testing and validation, while 82% is used for training. The correctness of the solver is approved through the negligible absolute error and matching of the solutions for each model’s case. To validates the accuracy, and consistency of the solver, the performances based on the error histogram, transition state, and regression for solving the FKBC model