Comparative evaluation of anomaly-based controller area network IDS

The vulnerability of in-vehicle networks, particularly those based on the Controller Area Network (CAN) protocol, has prompted the development of numerous techniques for intrusion detection on the CAN bus. However, these CAN IDS are often evaluated in disparate experimental settings, with different datasets and evaluation metrics, which hinder direct comparison. This has given rise to efforts at benchmarking and comparative evaluation of CAN IDS under similar experimental conditions to provide an understanding of the relative performance of these CAN IDS. This work contributes to these efforts by reporting results of the comparative evaluation of four statistical and two machine learning-based CAN intrusion detection algorithm, against the Real ORNL Automotive Dynamometer (ROAD) CAN intrusion dataset. The ROAD dataset differs from datasets used in previous work in that it includes the stealthiest possible version of targeted ID fabrication attacks which are more difficult to detect. It also consists of masquerade attacks, which have not been commonly used in comparative evaluation studies. Furthermore, in addition to metrics such as accuracy, precision, recall, and F1-score, we report balanced accuracy, informedness, markedness, and Matthews correlation coefficient, which place equal important on positive and negative classes and are better measures of detection capability, especially for imbalanced datasets. We also report training and testing times for each CAN IDS as an indicator of real-time intrusion detection performance. Results of experiments were found to be generally concordant with previous work, in terms of accuracy, precision, recall, and F1-score. Entropy and frequency-based CAN IDS were found to be relatively better at detecting attacks, particularly fabrication attacks; while other algorithms did not perform well, as indicated by low MCC scores

Business model shifts: masjid kitchens as soup kitchens, jobs employment and empowering asnaf entrepreneurs, and income generation for the masjid

The UNICEF report titled “Families on The Edge” dated October 2020, stated that COVID-19 crisis has pushed more low-income or poor B40 urban Malaysian families into poverty. The poverty rate of these B40 urban families in 2020 is higher than last year, with 50% of the families now living in absolute poverty. 70% of these households reported that COVID-19 had affected their ability to meet their basic living expenses. 37% reporting that they struggle to purchase enough food for their families, while 35% are unable to pay their bills on time. This paper adapted the Design Thinking (DT) methodology. This is to understand and define the key B40 urban families problems; ideation of possible solution options, validation of solution options by various customer segments; and to suggest a conceptual business model as possible solution. This includes conducting literature review and benchmarking, and conducting interviews. An initial business model using Business Model Canvas (BMC) framework was formulated; tested and validated by various customer segments. Hence, the main contribution of this paper is to offer a validated conceptual business model in transforming current masjid kitchen (or mKitchen) as a potential in (a) providing soup kitchen for the poor B40 and Asnaf community, (b) providing job employment while reskilling, empowering, and nurturing the Asnaf as food entrepreneurs, and (c) generating income for the masjid via rental of existing kitchen facilities. This is to turn the Asnaf community ‘Dari Penerima Zakat ke Pembayar Zakat’ or from Zakat receivers to Zakat contributors. The conceptual mKitchen business model can be adopted or adapted for possible implementation, in general, by masjid or mosques

Inaugural edu-action journey with cooperative behavior (Ta’awun) of mKitchen® students in free food for the needy

This book narrates the inaugural edu-action journey with cooperative behaviour (ta’awun) in free food for the needy people at IIUM mKitchen® with Sri Mutiara Teguh Enterprise (cafeteria vendor at Mahallah Aminah IIUM Gombak). The main objective of the project is to transform Mahallah kitchen as a platform to provide free food, to up skill in food-preneurship, to create new employment opportunities, to give empowerment and to nurture entrepreneurship (3Es) among students. Ta’awun refers to cooperative behaviour among the participants, the sponsors, and the beneficiaries in initiating, planning, sponsoring, executing and evaluating the mKitchen® project at Sri Mutiara Teguh Enterprise, Mahallah Aminah. The journey officially started on 1 July 2021 with various student development entities at IIUM. After providing a 4-day online training on business model and plan, financial modelling, and digital marketing, the steering committee has decided to provide the real experience to the students. In conjunction with Malaysia Day on 16 September 2021, the students proposed for cooking and distributing free food to the needy as part of the first proof-of-concept for mKitchen students with the “Keluarga Malaysia” spirit. Nevertheless, the project has to adhere to movement restriction by the authority to prevent the spread of coronavirus disease (COVID-19). The second proof-of-concept is cooking food based on mKitchen students’ business plan

A Journey of Ta’awun (Cooperative Behaviour) in Edu-Action at IIUM Mkitchen® and Masjid al-Syakirin Gombak (MASG)

This book narrates the journey of ta’awun (cooperative behaviour) at IIUM Mkitchen® project with Masjid Al-Syakirin Gombak (MASG). The main objective of the project is transform the mosque as a platform to provide free food, to up skill in foodpreneurship, to create new employment opportunities, to give empowerment and to nurture entrepreneurship (3Es) for poor and needy people in the surrounding area of it. Ta’awun refers to cooperative behaviour among the participants, the sponsors, and the beneficiaries in initiating, planning, sponsoring, executing and evaluating the Mkitchen® project at Masjid Al-Syakirin Gombak (MASG). The journey officially started on 24th January 2021 through its first meeting among the interested as well as committed members. After executing two events, the project was continued with a full scale project for a month. The project has secured its proof of concept (POC). Nevertheless, the project has been continued with a small scale due to the movement restriction by the authority to prevent the spread of coronavirus disease (COVID-19)

Budiman Mahallah kitchen

This book narrates budiman journey in Mahallah Kitchen. Budiman Mahallah Kitchen who are searching for adab (virtue), budi (kindness) and sejahtera (peace). By doing so, budiman and budiwati will have the capacity of bijak berbudi (wisdom in kindness), berani (brave) and bijaksana (full with wisdom). Budiman and budiwati with these attributes will manifest budi bahasa (rasa), budi pekerti (rupa), and budi bicara (roh). Thus, budiman and budiwati will have sejahtera niat (peaceful intentions), sejahtera keputusan (peaceful decisions), and sejahtera tindakan (peaceful actions). Budiman and budiwati will be continuously making contribution of sejahtera to self and others. The foundation of adab (virtue) is based on formula A-D-A-B. A is amanah (trustworthy), dakar (staunch), akhlak (ethics), and bestari (intelligent)

Redesign business model of masjid kitchens as soup kitchens, providing jobs employment and nurturing of asnaf as entrepreneurs, and income generation for the masjid

A survey conducted by the Department of Statistics Malaysia in May 2020 has found that: (a) 46.6% of self-employed respondents had reported losing their jobs; (b) an estimated 90% respondents were still working with lower than usual salaries; (c) more than two-thirds (71.4%) of self-employed respondents have sufficient financial savings for less than one month; and (d) the majority of respondents said they are unprepared if the duration of the MCO was extended except for employees under Government Linked Companies and multinational companies. In the “Families on The Edge” UNICEF report dated October 2020, COVID-19 crisis has pushed more low-income (or B40) urban Malaysian families into poverty. The poverty rate in the B40 community is higher than last year, with 50% of the families now living in absolute poverty. 70% of these households reported that COVID-19 had affected their ability to meet their basic living expenses, with 37% reporting that they struggle to purchase enough food for their families while 35% are unable to pay their bills on time. Hence, the main objectives of this paper is to offer a conceptual business model in turning masjid kitchens (or mKitchen) as a potential (a) soup kitchen for the B40 and Asnaf community, (b) job employment and nurturing of the Asnaf as entrepreneurs, and (c) income generation for the masjid via rental of kitchens. This is to turn the Asnaf community ‘Dari Penerima Zakat ke Pembayar Zakat’. The conceptual mKitchen business model can be adapted for possible implementation by masjid or mosques

Using streaming data algorithm for intrusion detection on the vehicular controller area network

The Controller Area Network (CAN), which is a protocol for the in-vehicle network, is lacking in security features, making the CAN bus vulnerable to a range of cyberattacks such as message injections, replay attacks, and denial of service attacks. This has prompted researchers to develop statistical and machine learning based intrusion detection systems for the CAN bus that use various features such as message timing and frequency to detect attacks. In this paper, the adapted streaming data Isolation Forest (iForestASD) algorithm has been applied to CAN intrusion detection. While the Isolation Forest (iForest) anomaly detection algorithm has a linear time complexity and low memory requirement, iForestASD adapts iForest by employing a sliding window that introduces the ability to handle concept drift, which is often characteristic of streaming data such as CAN bus traffic. The detection model is trained with only message timing information, making it applicable to all vehicles regardless of make and model. Results of experiments that compare the attack detection performance of iForestASD and iForest show that CAN traffic stream demonstrates insignificant concept drift and the detection model does not benefit from being retrained with a sliding window of latest CAN traffic, as in iForestASD. The size of the training sample is, however, found to be an important consideration - a model trained with only 30 s of CAN traffic always yields better detection performance than a model trained with a larger window of CAN traffic

Securing online quizzes and test on iTa’LeEM

IIUM’s Interactive Teaching and Learning Environment System (iTa'LeEM) comes with many great security features to make conducting online assessment more secured. The assessment can be an online quiz or test within a specific time allocated. The built-in quiz functions allow customisation that can help to increase security and integrity when conducting online assessment such as quizzes and tests. These features can be found mostly by expanding the Extra restriction on attempts when setting up your assessment

Dynamic android malware category classification using semi-supervised deep learning

Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. We use dynamic analysis to craft dynamic behavior profiles as feature vectors. Furthermore, we develop a new dataset, namely CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories: Adware, Banking, SMS, Riskware, and Benign. Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. The experimental results show that the model can classify Android apps with respect to malware category with F 1 -Score of 97.84 percent and a false positive rate of 2.76 percent, considerably higher than LP. These results demonstrate the robustness of our model despite the small number of labeled instances