This page has only limited features, please log in for full access.

Dr. Khairul Akram Zainol Ariffin
Universiti Kebangsaan Malaysia

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Digital Forensic
0 IoT Security
0 Intrusion detection system
0 Cybersecuirty
0 Digital Forensics Investigation

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 05 July 2021 in IEEE Access
Reads 0
Downloads 0

Systems of nonlinear equations are known as the basis for many models of engineering and data science, and their accurate solutions are very critical in achieving progress in these fields. However, solving a system with multiple nonlinear equations, usually, is not an easy task. Consequently, finding a robust and accurate solution can be a very challenging problem in complex systems. In this work, a novel hybrid method namely Newton-Harris hawks optimization (NHHO) for solving systems of nonlinear equations is proposed. The proposed NHHO combines Newton’s method, with a second-order convergence where the correct digits roughly double in every step, and the Harris hawks optimization (HHO) to enhance the search mechanism, avoid local optima, improve convergence speed, and find more accurate solutions. We tested a group of six well-known benchmark systems of nonlinear equations to evaluate the efficiency of NHHO. Further, comparisons between NHHO and other optimization algorithms, including the original HHO algorithm, Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Equilibrium Optimization (EO) were performed. The norm of the equation system was calculated as a fitness function to measure the optimization algorithms’ performance. A solution with less fitness value is considered a better solution. Furthermore, the experimental results confirmed the superiority of NHHO over the other optimization algorithms, in the comparisons, in different aspects, including best solution, average fitness value, and convergence speed. Accordingly, the proposed NHHO is powerful and more effective in all benchmark problems in solving systems of nonlinear equations compared to the other optimization algorithms. Finally, NHHO overcomes the limitations of Newton’s method, including selecting the initial point and divergence problems.

ACS Style

Rami Sihwail; Obadah Said Solaiman; Khairuddin Omar; Khairul Akram Zainol Ariffin; Mohammed Alswaitti; Ishak Hashim. A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method. IEEE Access 2021, 9, 95791 -95807.

AMA Style

Rami Sihwail, Obadah Said Solaiman, Khairuddin Omar, Khairul Akram Zainol Ariffin, Mohammed Alswaitti, Ishak Hashim. A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method. IEEE Access. 2021; 9 ():95791-95807.

Chicago/Turabian Style

Rami Sihwail; Obadah Said Solaiman; Khairuddin Omar; Khairul Akram Zainol Ariffin; Mohammed Alswaitti; Ishak Hashim. 2021. "A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method." IEEE Access 9, no. : 95791-95807.

Journal article
Published: 20 February 2021 in Computers & Security
Reads 0
Downloads 0

The introduction of Industrial Revolution 4.0 (IR 4.0) brings benefits to the industries and our daily life. Innovation such as the Internet of Things, cloud computing, and blockchain is not only confined to the manufacturing industry but covers the whole of human life. Notwithstanding the said innovation, it also gives rise to cybercrimes with these technologies’ assistance. The botnet called Mirai is one example of compromising the technology in IR 4.0 to launch large-scale cyberattacks through Internet access. It is therefore crucial for the digital forensic (DF) organization to be ready to handle this kind of incident. This paper aims to provide the indicators for DF organizations’ maturity and readiness in the era of IR 4.0. To establish the indicators, a systematic literature review (SLR) is conducted. It involves four phases in the SLR, where the focus is; (1) challenges of DF in IR 4.0, (2) chain of custody and DF readiness, (3) existing maturity model, and (4) benchmarking the maturity element, respectively. It covers the research studies taken from five databases. From the comparison analysis, this study has derived five indicators for the maturity and readiness of DF organization: (1) People and capacity development, (2) Organization, policy and cooperation, (3) Process, (4) Technology and technical, (5) Legislation and regulation. Finally the work outlines the DF practices based on the CMMI ver. 2 practice areas and potential governance and management objectives that can govern the DF organization.

ACS Style

Khairul Akram Zainol Ariffin; Faris Hanif Ahmad. Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0. Computers & Security 2021, 105, 102237 .

AMA Style

Khairul Akram Zainol Ariffin, Faris Hanif Ahmad. Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0. Computers & Security. 2021; 105 ():102237.

Chicago/Turabian Style

Khairul Akram Zainol Ariffin; Faris Hanif Ahmad. 2021. "Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0." Computers & Security 105, no. : 102237.

Review
Published: 01 January 2021 in Sustainability
Reads 0
Downloads 0

The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.

ACS Style

Nahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability 2021, 13, 338 .

AMA Style

Nahla Nurelmadina, Mohammad Hasan, Imran Memon, Rashid Saeed, Khairul Zainol Ariffin, Elmustafa Ali, Rania Mokhtar, Shayla Islam, Eklas Hossain, Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability. 2021; 13 (1):338.

Chicago/Turabian Style

Nahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. 2021. "A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications." Sustainability 13, no. 1: 338.

Journal article
Published: 13 October 2020 in Symmetry
Reads 0
Downloads 0

The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlefish algorithm (D-CFA). First, the dependency ratio between the features and the classes was calculated, using the RST. Second, each feature in the datasets became an input for the BPNN, to measure their ability for a classification task concerning each class. Third, a feature-selection process was carried out over multiple runs, to indicate the frequency of the selection of each feature. From the result, it indicated that some features in the KDD99 dataset could be used to achieve a classification accuracy above 84%. Moreover, a few features in both datasets were found to give a high contribution to increasing the classification’s performance. These features were present in a combination of features that resulted in high accuracy; the features were also frequently selected during the feature selection process. The findings of this study are anticipated to help the cybersecurity academics in creating a lightweight and accurate IDS model with a smaller number of features for the developing technologies.

ACS Style

Muataz Salam Al-Daweri; Khairul Akram Zainol Ariffin; Salwani Abdullah; Mohamad Firham Efendy Md. Senan. An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System. Symmetry 2020, 12, 1666 .

AMA Style

Muataz Salam Al-Daweri, Khairul Akram Zainol Ariffin, Salwani Abdullah, Mohamad Firham Efendy Md. Senan. An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System. Symmetry. 2020; 12 (10):1666.

Chicago/Turabian Style

Muataz Salam Al-Daweri; Khairul Akram Zainol Ariffin; Salwani Abdullah; Mohamad Firham Efendy Md. Senan. 2020. "An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System." Symmetry 12, no. 10: 1666.

Conference paper
Published: 29 September 2020 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Reads 0
Downloads 0

Cloud computing is widely used but with an undefined term for a multitude of different resources that are automatically distributed. Cloud computing can be called a double edge weapon from law enforcement and forensic investigation standpoint. Digital evidence collected from cloud sources, on the one hand, can present complex technical and cross-jurisdictional legal issues. This study explores the ability to retrieve possible data remnants for pCloud applications that can be applied in the preliminary analysis for forensic investigation. It is based on volatile memory analysis. The experiment on the retrieval involves three scenarios on pCloud; download, upload, and view the files on the cloud. The retrieval of the possible data remnants on this cloud application is the first step in introducing the indicator of cloud usage that can assist the forensic investigation at the early phase.

ACS Style

Nur Hayati Ahmad; Ameerah Saeedatus Syaheerah Abdul Hamid; Nur Solehah Sorfina Shahidan; Khairul Akram Zainol Ariffin. Cloud Forensic Analysis on pCloud: From Volatile Memory Perspectives. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 3 -15.

AMA Style

Nur Hayati Ahmad, Ameerah Saeedatus Syaheerah Abdul Hamid, Nur Solehah Sorfina Shahidan, Khairul Akram Zainol Ariffin. Cloud Forensic Analysis on pCloud: From Volatile Memory Perspectives. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():3-15.

Chicago/Turabian Style

Nur Hayati Ahmad; Ameerah Saeedatus Syaheerah Abdul Hamid; Nur Solehah Sorfina Shahidan; Khairul Akram Zainol Ariffin. 2020. "Cloud Forensic Analysis on pCloud: From Volatile Memory Perspectives." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 3-15.

Journal article
Published: 02 July 2020 in IEEE Access
Reads 0
Downloads 0

The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost. Feature selection technique has a critical role as a preprocessing step in reducing these issues. It works by eliminating the features that may negatively influence the classifiers’ performance, such as irrelevant, redundant and less informative features. This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite opposite-based learning and proposing a new search mechanism. Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems. Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities. Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate. To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikit-feature project. The datasets represent different levels of feature dimensionality, such as low, moderate, and high. Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests. Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed. The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.

ACS Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin; Mohammad Tubishat. Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection. IEEE Access 2020, 8, 121127 -121145.

AMA Style

Rami Sihwail, Khairuddin Omar, Khairul Akram Zainol Ariffin, Mohammad Tubishat. Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection. IEEE Access. 2020; 8 (99):121127-121145.

Chicago/Turabian Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin; Mohammad Tubishat. 2020. "Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection." IEEE Access 8, no. 99: 121127-121145.

Journal article
Published: 08 April 2020 in IEEE Access
Reads 0
Downloads 0

Cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA’s sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.

ACS Style

Muataz Salam Al Daweri; Salwani Abdullah; K. A. Zainol Ariffin. A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems. IEEE Access 2020, 8, 70270 -70292.

AMA Style

Muataz Salam Al Daweri, Salwani Abdullah, K. A. Zainol Ariffin. A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems. IEEE Access. 2020; 8 (99):70270-70292.

Chicago/Turabian Style

Muataz Salam Al Daweri; Salwani Abdullah; K. A. Zainol Ariffin. 2020. "A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems." IEEE Access 8, no. 99: 70270-70292.

Journal article
Published: 05 September 2019 in Applied Sciences
Reads 0
Downloads 0

The need to detect malware before it harms computers, mobile phones and other electronic devices has caught the attention of researchers and the anti-malware industry for many years. To protect users from malware attacks, anti-virus software products are downloaded on the computer. The anti-virus mainly uses signature-based techniques to detect malware. However, this technique fails to detect malware that uses packing, encryption or obfuscation techniques. It also fails to detect unseen (new) ones. This paper proposes an integrated malware detection approach that applies memory forensics to extract malicious artifacts from memory and combines them to features extracted during the execution of malware in a dynamic analysis. Pre-modeling techniques were also applied for feature engineering before training and testing the data set on the machine learning models. The experimental results show a significant improvement in both detection accuracy rate and false positive rate, 98.5% and 1.7% respectively, by applying the support vector machine. The results verify that our integrated analysis approach outperforms other analysis methods. In addition, the proposed approach overcomes the limitation of single path file execution in dynamic analysis by adding more relevant memory artifacts that can reveal the real intention of malicious files.

ACS Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin; Sanad Al Afghani. Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis. Applied Sciences 2019, 9, 3680 .

AMA Style

Rami Sihwail, Khairuddin Omar, Khairul Akram Zainol Ariffin, Sanad Al Afghani. Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis. Applied Sciences. 2019; 9 (18):3680.

Chicago/Turabian Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin; Sanad Al Afghani. 2019. "Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis." Applied Sciences 9, no. 18: 3680.

Conference paper
Published: 01 January 2019 in Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia
Reads 0
Downloads 0
ACS Style

M. Majid; Khairul Akram Zainol Ariffin. Success Factors for Cyber Security Operation Center (SOC) Establishment. Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia 2019, 1 .

AMA Style

M. Majid, Khairul Akram Zainol Ariffin. Success Factors for Cyber Security Operation Center (SOC) Establishment. Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia. 2019; ():1.

Chicago/Turabian Style

M. Majid; Khairul Akram Zainol Ariffin. 2019. "Success Factors for Cyber Security Operation Center (SOC) Establishment." Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITEST 2019, 18 July 2019, Bandung, Indonesia , no. : 1.

Conference paper
Published: 01 November 2018 in 2018 Cyber Resilience Conference (CRC)
Reads 0
Downloads 0

Teenagers can easily expose to criminal activities through media social, surrounding, and peers. They tend to imitate some of these criminal activities such as cyber bullying, fighting, cyber grooming and cyber harassment without thinking of his or communities' side-effect. Therefore, we develop a self-crime prevention modules encompassing faith, positivity, social relationship, role model and reflection tailored for school teens. Then, we make a list of survey questions using 4 Likert scale to measure their acceptance and adaptation after participating our self-crime module. About 105 school children aged 13-17 participated from Sekolah Menengah Titiwangsa and they responded positively with approximately 3.63 of average score.

ACS Style

Siti Norul Huda Sheikh Abdullah; Wan Fariza Fauzi; Muhammad Nuruddin Sudin; Nurul Nadhirah Zahari; Zaizul Rahman; A Dawiyah Ismail; Nur Riza Mohd. Suradi; Azmin Sham Rambely; Norulhuda Binti Sarnon Kusenin; Azianura Hani Shaari; Samruhaizad Samian; Norhana Arsad; Mohamad Hanif Md Saad; Masnizah Mohd; Masri Binti Ayob; Kok Ven Jyn; Khairul Akram Zainol Ariffin; Mohd Zakree Ahmad Nazri; Mohd Ridzwan Yaakub. Assessment of Self-Identity Among Teens Towards Self-Crime Prevention Program. 2018 Cyber Resilience Conference (CRC) 2018, 1 -4.

AMA Style

Siti Norul Huda Sheikh Abdullah, Wan Fariza Fauzi, Muhammad Nuruddin Sudin, Nurul Nadhirah Zahari, Zaizul Rahman, A Dawiyah Ismail, Nur Riza Mohd. Suradi, Azmin Sham Rambely, Norulhuda Binti Sarnon Kusenin, Azianura Hani Shaari, Samruhaizad Samian, Norhana Arsad, Mohamad Hanif Md Saad, Masnizah Mohd, Masri Binti Ayob, Kok Ven Jyn, Khairul Akram Zainol Ariffin, Mohd Zakree Ahmad Nazri, Mohd Ridzwan Yaakub. Assessment of Self-Identity Among Teens Towards Self-Crime Prevention Program. 2018 Cyber Resilience Conference (CRC). 2018; ():1-4.

Chicago/Turabian Style

Siti Norul Huda Sheikh Abdullah; Wan Fariza Fauzi; Muhammad Nuruddin Sudin; Nurul Nadhirah Zahari; Zaizul Rahman; A Dawiyah Ismail; Nur Riza Mohd. Suradi; Azmin Sham Rambely; Norulhuda Binti Sarnon Kusenin; Azianura Hani Shaari; Samruhaizad Samian; Norhana Arsad; Mohamad Hanif Md Saad; Masnizah Mohd; Masri Binti Ayob; Kok Ven Jyn; Khairul Akram Zainol Ariffin; Mohd Zakree Ahmad Nazri; Mohd Ridzwan Yaakub. 2018. "Assessment of Self-Identity Among Teens Towards Self-Crime Prevention Program." 2018 Cyber Resilience Conference (CRC) , no. : 1-4.

Conference paper
Published: 01 November 2018 in 2018 Cyber Resilience Conference (CRC)
Reads 0
Downloads 0

Aviation-related information management system as well as other information systems suffer from the problem of security risk. This security risk is not an aviation-specific problem and this paper aims to study on the existing risks for the Malaysian Aeronautical Information Management System in the Civil Aviation Authority of Malaysia. The aims of this paper is to provide an overall risk level rating for the future implementation of information security risk management systems, while highlighting the advantages of ISO 27005 standards. The risk assessment activities will start with risk identification, followed by risk estimation. The third step is the risk evaluation which addresses the result analysis and several contributions of the study as efforts toward initiating ISO certification process.

ACS Style

Alfian Alwi; Khairul Akram Zainol Ariffin. Information Security Risk Assessment for the Malaysian Aeronautical Information Management System. 2018 Cyber Resilience Conference (CRC) 2018, 1 -4.

AMA Style

Alfian Alwi, Khairul Akram Zainol Ariffin. Information Security Risk Assessment for the Malaysian Aeronautical Information Management System. 2018 Cyber Resilience Conference (CRC). 2018; ():1-4.

Chicago/Turabian Style

Alfian Alwi; Khairul Akram Zainol Ariffin. 2018. "Information Security Risk Assessment for the Malaysian Aeronautical Information Management System." 2018 Cyber Resilience Conference (CRC) , no. : 1-4.

Journal article
Published: 29 September 2018 in International Journal on Advanced Science, Engineering and Information Technology
Reads 0
Downloads 0

Now a day the threat of malware is increasing rapidly. A software that sneaks to your computer system without your knowledge with a harmful intent to disrupt your computer operations. Due to the vast number of malware, it is impossible to handle malware by human engineers. Therefore, security researchers are taking great efforts to develop accurate and effective techniques to detect malware. This paper presents a semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based. The Signature-based technique is largely used today by anti-virus software to detect malware, is fast and capable to detect known malware. However, it is not effective in detecting zero-day malware and it is easily defeated by malware that use obfuscation techniques. Likewise, a considerable false positive rate and high amount of scanning time are the main limitations of heuristic-based techniques. Alternatively, memory analysis is a promising technique that gives a comprehensive view of malware and it is expected to become more popular in malware analysis. The main contributions of this paper are: (1) providing an overview of malware types and malware detection approaches, (2) discussing the current malware analysis techniques, their findings and limitations, (3) studying the malware obfuscation, attacking and anti-analysis techniques, and (4) exploring the structure of memory-based analysis in malware detection. The detection approaches have been compared with each other according to their techniques, selected features, accuracy rates, and their advantages and disadvantages. This paper aims to help the researchers to have a general view of malware detection field and to discuss the importance of memory-based analysis in malware detection.

ACS Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin. A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis. International Journal on Advanced Science, Engineering and Information Technology 2018, 8, 1662 -1671.

AMA Style

Rami Sihwail, Khairuddin Omar, Khairul Akram Zainol Ariffin. A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis. International Journal on Advanced Science, Engineering and Information Technology. 2018; 8 (4-2):1662-1671.

Chicago/Turabian Style

Rami Sihwail; Khairuddin Omar; Khairul Akram Zainol Ariffin. 2018. "A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis." International Journal on Advanced Science, Engineering and Information Technology 8, no. 4-2: 1662-1671.

Journal article
Published: 26 September 2018 in International Journal on Advanced Science, Engineering and Information Technology
Reads 0
Downloads 0

Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of a new image representation and the classification. The first phase comprises image binarization process and energy image generation using gait average image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event and our proposed method also outperforms Dalal Method.

ACS Style

Siti Zaharah A. Rahman; Siti Norul Huda Sheikh Abdullah; Khairul Akram Zainol Ariffin. Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient. International Journal on Advanced Science, Engineering and Information Technology 2018, 8, 1402 -1410.

AMA Style

Siti Zaharah A. Rahman, Siti Norul Huda Sheikh Abdullah, Khairul Akram Zainol Ariffin. Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient. International Journal on Advanced Science, Engineering and Information Technology. 2018; 8 (4-2):1402-1410.

Chicago/Turabian Style

Siti Zaharah A. Rahman; Siti Norul Huda Sheikh Abdullah; Khairul Akram Zainol Ariffin. 2018. "Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient." International Journal on Advanced Science, Engineering and Information Technology 8, no. 4-2: 1402-1410.

Journal article
Published: 01 December 2017 in International Journal of Electrical and Computer Engineering (IJECE)
Reads 0
Downloads 0

Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low numberof laser data were obtained.

ACS Style

Abdul Hadi Abd Rahman; Khairul Akram Zainol Ariffin; Nor Samsiah Sani; Hairi Zamzuri. Pedestrian Detection using Triple Laser Range Finders. International Journal of Electrical and Computer Engineering (IJECE) 2017, 7, 3037 .

AMA Style

Abdul Hadi Abd Rahman, Khairul Akram Zainol Ariffin, Nor Samsiah Sani, Hairi Zamzuri. Pedestrian Detection using Triple Laser Range Finders. International Journal of Electrical and Computer Engineering (IJECE). 2017; 7 (6):3037.

Chicago/Turabian Style

Abdul Hadi Abd Rahman; Khairul Akram Zainol Ariffin; Nor Samsiah Sani; Hairi Zamzuri. 2017. "Pedestrian Detection using Triple Laser Range Finders." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6: 3037.

Journal article
Published: 03 November 2017 in IEEE Access
Reads 0
Downloads 0

Wireless sensor networks, due to their nature, are more prone to security threats than other networks. Developments in WSNs have led to the introduction of many protocols specially developed for security purposes. Most of these protocols are not efficient in terms of putting an excessive computational and energy consumption burden on small nodes in WSNs. This paper proposes a knowledge-based context-aware approach for handling the intrusions generated by malicious nodes. The system operates on a knowledge base, located at the base station, which is used to store the events generated by the nodes inside the network. The events are categorized and the cluster heads (CHs) are acknowledged to block maliciously repeated activities generated. The CHs can also get informational records about the maliciousness of intruder nodes by using their inference engines. The mechanism of events logging and analysis by the base station greatly affects the performance of nodes in the network by reducing the extra security-related load on them.

ACS Style

Amjad Mehmood; Akbar Khanan; Muhammad Muneer Umar; Salwani Abdullah; Khairul Akram Zainol Ariffin; Houbing Song. Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks. IEEE Access 2017, 6, 5688 -5694.

AMA Style

Amjad Mehmood, Akbar Khanan, Muhammad Muneer Umar, Salwani Abdullah, Khairul Akram Zainol Ariffin, Houbing Song. Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks. IEEE Access. 2017; 6 (99):5688-5694.

Chicago/Turabian Style

Amjad Mehmood; Akbar Khanan; Muhammad Muneer Umar; Salwani Abdullah; Khairul Akram Zainol Ariffin; Houbing Song. 2017. "Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks." IEEE Access 6, no. 99: 5688-5694.

Conference paper
Published: 01 October 2015 in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing
Reads 0
Downloads 0

With the advance in technology, the computer storage will become cheaper for the larger sizes. Previously, it allows the user to store more data at a lower cost. In context of digital forensic investigation, the traditional approach such as analysis on the hard disk will become inefficient in handling the huge data that is stored within it. The research on retrieving the open files from computer memory only focused on tracking the Virtual Address Descriptor (VAD) and Object Table. Thus, only the active object's open files can be retrieved from the computer memory. The aim of this paper is to present algorithms to track the metadata of file from the well-known file system for Windows system such as File Allocation Table (FAT) and New Technologies File System (NTFS). The algorithms encompass the signature search to retrieve the boot sector and then capture the metadata about the file from the computer memory. The algorithm will be independent of address translation algorithm and able to capture the information from various file's extension, not limited to .EXE and .DLL.

ACS Style

Khairul Akram Zainol Ariffin; Ahmad Kamil Mahmood; Jafreezal Jaafar; Solahuddin Shamsuddin. Tracking File's Metadata from Computer Memory Analysis. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing 2015, 975 -980.

AMA Style

Khairul Akram Zainol Ariffin, Ahmad Kamil Mahmood, Jafreezal Jaafar, Solahuddin Shamsuddin. Tracking File's Metadata from Computer Memory Analysis. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. 2015; ():975-980.

Chicago/Turabian Style

Khairul Akram Zainol Ariffin; Ahmad Kamil Mahmood; Jafreezal Jaafar; Solahuddin Shamsuddin. 2015. "Tracking File's Metadata from Computer Memory Analysis." 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing , no. : 975-980.