Mohamed Amine Ben Farah

Anum Paracha, Junaid Arshad, Mohamed Ben Farah et al.

Poisoning attacks are a primary threat to machine learning models, aiming to compromise their performance and reliability by manipulating training datasets. This paper introduces a novel attack - Outlier-Oriented Poisoning (OOP) attack, which manipulates labels of most distanced samples from the decision boundaries. The paper also investigates the adverse impact of such attacks on different machine learning algorithms within a multiclass classification scenario, analyzing their variance and correlation between different poisoning levels and performance degradation. To ascertain the severity of the OOP attack for different degrees (5% - 25%) of poisoning, we analyzed variance, accuracy, precision, recall, f1-score, and false positive rate for chosen ML models.Benchmarking our OOP attack, we have analyzed key characteristics of multiclass machine learning algorithms and their sensitivity to poisoning attacks. Our experimentation used three publicly available datasets: IRIS, MNIST, and ISIC. Our analysis shows that KNN and GNB are the most affected algorithms with a decrease in accuracy of 22.81% and 56.07% while increasing false positive rate to 17.14% and 40.45% for IRIS dataset with 15% poisoning. Further, Decision Trees and Random Forest are the most resilient algorithms with the least accuracy disruption of 12.28% and 17.52% with 15% poisoning of the IRIS dataset. We have also analyzed the correlation between number of dataset classes and the performance degradation of models. Our analysis highlighted that number of classes are inversely proportional to the performance degradation, specifically the decrease in accuracy of the models, which is normalized with increasing number of classes. Further, our analysis identified that imbalanced dataset distribution can aggravate the impact of poisoning for machine learning models

Elochukwu Ukwandu, Mohamed Amine Ben Farah, Hanan Hindy et al.

The integration of Information and Communication Technology (ICT) tools into mechanical devices found in aviation industry has raised security concerns. The more integrated the system, the more vulnerable due to the inherent vulnerabilities found in ICT tools and software that drives the system. The security concerns have become more heightened as the concept of electronic-enabled aircraft and smart airports get refined and implemented underway. In line with the above, this paper undertakes a review of cyber-security incidence in the aviation sector over the last 20 years. The essence is to understand the common threat actors, their motivations, the type of attacks, aviation infrastructure that is commonly attacked and then match these so as to provide insight on the current state of the cyber-security in the aviation sector. The review showed that the industry's threats come mainly from Advance Persistent Threat (APT) groups that work in collaboration with some state actors to steal intellectual property and intelligence, in order to advance their domestic aerospace capabilities as well as possibly monitor, infiltrate and subvert other nations' capabilities. The segment of the aviation industry commonly attacked is the Information Technology infrastructure, and the prominent type of attacks is malicious hacking activities that aim at gaining unauthorised access using known malicious password cracking techniques such as Brute force attacks, Dictionary attacks and so on. The review further analysed the different attack surfaces that exist in aviation industry, threat dynamics, and use these dynamics to predict future trends of cyberattacks in the industry. The aim is to provide information for the cybersecurity professionals and aviation stakeholders for proactive actions in protecting these critical infrastructures against cyberincidence for an optimal customer service oriented industry.

Elochukwu Ukwandu, Mohamed Amine Ben Farah, Hanan Hindy et al.

Cyber situational awareness has been proven to be of value in forming a comprehensive understanding of threats and vulnerabilities within organisations, as the degree of exposure is governed by the prevailing levels of cyber-hygiene and established processes. A more accurate assessment of the security provision informs on the most vulnerable environments that necessitate more diligent management. The rapid proliferation in the automation of cyber-attacks is reducing the gap between information and operational technologies and the need to review the current levels of robustness against new sophisticated cyber-attacks, trends, technologies and mitigation countermeasures has become pressing. A deeper characterisation is also the basis with which to predict future vulnerabilities in turn guiding the most appropriate deployment technologies. Thus, refreshing established practices and the scope of the training to support the decision making of users and operators. The foundation of the training provision is the use of Cyber-Ranges (CRs) and Test-Beds (TBs), platforms/tools that help inculcate a deeper understanding of the evolution of an attack and the methodology to deploy the most impactful countermeasures to arrest breaches. In this paper, an evaluation of documented CR and TB platforms is evaluated. CRs and TBs are segmented by type, technology, threat scenarios, applications and the scope of attainable training. To enrich the analysis of documented CR and TB research and cap the study, a taxonomy is developed to provide a broader comprehension of the future of CRs and TBs. The taxonomy elaborates on the CRs/TBs different dimensions, as well as, highlighting a diminishing differentiation between application areas.