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Vehicular Ad Hoc Networks (VANETs) are among the main enablers for future Intelligent Transportation Systems (ITSs) as they facilitate information sharing, which improves road safety, traffic efficiency, and provides passengers’ comfort. Due to the dynamic nature of VANETs, vehicles need to exchange the Cooperative Awareness Messages (CAMs) more frequently to maintain network agility and preserve applications’ performance. However, in many situations, broadcasting at a high rate leads to congest the communication channel, rendering VANET unreliable. Existing broadcasting schemes designed for VANET use partial context variables to control the broadcasting rate. Additionally, CAMs uncertainty, which is context-dependent has been neglected and a predefined fixed certainty threshold has been used instead, which is not suitable for the highly dynamic context. Consequently, vehicles disseminate a high rate of unnecessary CAMs which degrades VANET performance. A good broadcasting scheme should accurately determine which and when CAMs are broadcasted. To this end, this study proposes a Context-Aware Adaptive Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) using combinations of Adaptive Kalman Filter, Autoregression, and Sequential Deep Learning and Fuzzy inference system. Four context variables have been used to represent the vehicular context, namely, individual driving behaviors, CAMs uncertainty, vehicle density, and traffic flow. Kalman Filter and Autoregression are used to estimate and predict the CAMs messages respectively. The deep learning model has been constructed to estimate the CAMs’ uncertainties which is an important context variable that has been neglected in the previous research. Fuzzy Inference System takes context variables as input and determines an accurate broadcasting threshold and broadcasting interval. Extensive simulations have been conducted to evaluate the proposed scheme. Results show that the proposed scheme improves the CAMs delivery ratio and decreases the CAMs prediction errors.
Fuad A. Ghaleb; Bander Ali Saleh Al-Rimy; Abdulmohsen Almalawi; Abdullah Marish Ali; Anazida Zainal; Murad A. Rassam; Syed Zainudeen Mohd Shaid; Mohd Aizaini Maarof. Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach. IEEE Access 2020, 8, 217744 -217761.
AMA StyleFuad A. Ghaleb, Bander Ali Saleh Al-Rimy, Abdulmohsen Almalawi, Abdullah Marish Ali, Anazida Zainal, Murad A. Rassam, Syed Zainudeen Mohd Shaid, Mohd Aizaini Maarof. Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach. IEEE Access. 2020; 8 (99):217744-217761.
Chicago/Turabian StyleFuad A. Ghaleb; Bander Ali Saleh Al-Rimy; Abdulmohsen Almalawi; Abdullah Marish Ali; Anazida Zainal; Murad A. Rassam; Syed Zainudeen Mohd Shaid; Mohd Aizaini Maarof. 2020. "Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach." IEEE Access 8, no. 99: 217744-217761.
Crypto-ransomware is a type of malware whose effect is irreversible even after detection and removal. Thus, early detection is crucial to protect user files from being encrypted and held to ransom. Several studies have proposed early detection solutions based on the data acquired during the pre-encryption phase of the attacks. However, the lack of sufficient data in the early phases of the attack adversely affects the ability of feature selection techniques in these models to perceive the common characteristics of the attack features, which makes it challenging to reduce the redundant features, consequently decreasing the detection accuracy. Therefore, this study proposes a novel Redundancy Coefficient Gradual Upweighting (RCGU) technique that makes better redundancy–relevancy trade-offs during feature selection. Unlike existing feature significance estimation techniques that rely on the comparison between the candidate feature and the common characteristics of the already-selected features, RCGU compares the mutual information between the candidate feature and each feature in the selected set individually. Therefore, RCGU increases the weight of the redundancy term proportional to the number of already selected features. By integrating the RCGU into the Mutual Information Feature Selection (MIFS) technique, the Enhanced MIFS (EMIFS) was developed. Further improvement was achieved by proposing MM-EMIFS which incorporates the MaxMin approximation with EMIFS to prevent the redundancy overestimation that RCGU could cause when the number of features in the already-selected set increases. The experimental evaluation shows that the proposed techniques achieved accuracy higher than that in related works, which confirms the ability of RCGU to make better redundancy–relevancy trade-offs and select more discriminative pre-encryption attack features compared to existing solutions.
Bander Ali Saleh Al-Rimy; Mohd Aizaini Maarof; Mamoun Alazab; Syed Zainudeen Mohd Shaid; Fuad A. Ghaleb; Abdulmohsen Almalawi; Abdullah Marish Ali; Tawfik Al-Hadhrami. Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection. Future Generation Computer Systems 2020, 115, 641 -658.
AMA StyleBander Ali Saleh Al-Rimy, Mohd Aizaini Maarof, Mamoun Alazab, Syed Zainudeen Mohd Shaid, Fuad A. Ghaleb, Abdulmohsen Almalawi, Abdullah Marish Ali, Tawfik Al-Hadhrami. Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection. Future Generation Computer Systems. 2020; 115 ():641-658.
Chicago/Turabian StyleBander Ali Saleh Al-Rimy; Mohd Aizaini Maarof; Mamoun Alazab; Syed Zainudeen Mohd Shaid; Fuad A. Ghaleb; Abdulmohsen Almalawi; Abdullah Marish Ali; Tawfik Al-Hadhrami. 2020. "Redundancy Coefficient Gradual Up-weighting-based Mutual Information Feature Selection technique for Crypto-ransomware early detection." Future Generation Computer Systems 115, no. : 641-658.
Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.
Abdulmohsen Almalawi; Adil Fahad; Zahir Tari; Asif Irshad Khan; Nouf Alzahrani; Sheikh Tahir Bakhsh; Madini O. Alassafi; Abdulrahman Alshdadi; Sana Qaiyum. Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data. Electronics 2020, 9, 1017 .
AMA StyleAbdulmohsen Almalawi, Adil Fahad, Zahir Tari, Asif Irshad Khan, Nouf Alzahrani, Sheikh Tahir Bakhsh, Madini O. Alassafi, Abdulrahman Alshdadi, Sana Qaiyum. Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data. Electronics. 2020; 9 (6):1017.
Chicago/Turabian StyleAbdulmohsen Almalawi; Adil Fahad; Zahir Tari; Asif Irshad Khan; Nouf Alzahrani; Sheikh Tahir Bakhsh; Madini O. Alassafi; Abdulrahman Alshdadi; Sana Qaiyum. 2020. "Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data." Electronics 9, no. 6: 1017.
Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.
Sana Qaiyum; Izzatdin Aziz; Mohd Hilmi Hasan; Asif Irshad Khan; Abdulmohsen Almalawi. Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method. Sensors 2020, 20, 3210 .
AMA StyleSana Qaiyum, Izzatdin Aziz, Mohd Hilmi Hasan, Asif Irshad Khan, Abdulmohsen Almalawi. Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method. Sensors. 2020; 20 (11):3210.
Chicago/Turabian StyleSana Qaiyum; Izzatdin Aziz; Mohd Hilmi Hasan; Asif Irshad Khan; Abdulmohsen Almalawi. 2020. "Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method." Sensors 20, no. 11: 3210.
Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from -5 to +5. This requires recruitment of subjects and matching for gender, age etc. In this study, we have tried to predict the thermal comfort of occupants by observing their real behavior inside the test room fitted with a novel thermoelectric air duct (TE-AD) cooling system rather than a conventional air conditioning system. Firstly, real experimental data were collected for more than two months from the test room equipped with the TE-AD cooling system operated at an input power supply of 6 A and 5 V. After that, the ANN model was developed based on the Levenberg-Marquardt algorithm by taking experimental parameters such as air temperature, relative humidity, globe temperature, wind speed, metabolic rate, and clothing value as model input. The ANN model is optimized by developing different models with different data points as a starting input in the training and validation process. The neuron optimization has been carried out in these models to minimize the mean square error (MSE) for the ANN model. The result shows that among the three models M1, M2, and M3, the optimum predictive mean value (PMV) was obtained from M1 at 10 neurons with MSE of 0.07956, while for predicted percentage dissatisfied (PPD), M3 gives optimum accuracy at 10 neurons with MSE value of 5.1789. The ANN model is then generalized to predict thermal comfort for one week and then for one month. Finally, all the model results were validated with the experimental data.
Kashif Irshad; Asif Irshad Khan; Sayed Ameenuddin Irfan; Mottahir Alam; Abdulmohsen Almalawi; Hasan Zahir. Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System. IEEE Access 2020, 8, 99709 -99728.
AMA StyleKashif Irshad, Asif Irshad Khan, Sayed Ameenuddin Irfan, Mottahir Alam, Abdulmohsen Almalawi, Hasan Zahir. Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System. IEEE Access. 2020; 8 (99):99709-99728.
Chicago/Turabian StyleKashif Irshad; Asif Irshad Khan; Sayed Ameenuddin Irfan; Mottahir Alam; Abdulmohsen Almalawi; Hasan Zahir. 2020. "Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System." IEEE Access 8, no. 99: 99709-99728.
This study investigates the performance of the thermoelectric air conditioning (TE-AC) system smartly controlled by the Internet of Things (IoT)-based configuration for real tropical climatic application. Air cooling management was done through thermoelectric coolers, and an Arduino microcontroller with various sensors such as a temperature sensor, simple RF modules, and actuators was used to control the indoor climatic conditions based on outdoor conditions. The result shows that when the input power supply to the IoT-based TE-AC system is increased, the cooling capacity of the framework is also enhanced. Significant power and carbon emission reduction was observed for the IoT-based TE-AC system as compared to the TE-AC system without IoT. The IoT-incorporated system also ensures better microclimatic temperature control. Additionally, the system cooling capacity improves by 14.0%, and the coefficient of performance is increased by 46.3%. Thus, this study provides a smart solution to the two major energy harvesting issues of traditional air conditioners—an increase in energy efficiency by employing a TE-AC system and a further improvement in efficiency by using an IoT-based thermal management system.
Kashif Irshad; Abdulmohsen Almalawi; Asif Irshad Khan; Mottahir Alam; Hasan Zahir; Amjad Ali. An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate. Sustainability 2020, 12, 1564 .
AMA StyleKashif Irshad, Abdulmohsen Almalawi, Asif Irshad Khan, Mottahir Alam, Hasan Zahir, Amjad Ali. An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate. Sustainability. 2020; 12 (4):1564.
Chicago/Turabian StyleKashif Irshad; Abdulmohsen Almalawi; Asif Irshad Khan; Mottahir Alam; Hasan Zahir; Amjad Ali. 2020. "An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate." Sustainability 12, no. 4: 1564.
Network traffic classification is an essential component for service differentiation, network design and management and security systems. The limitations of traditional port-based and payload methods have been addressed by recent promising studies which rely on the analysis of the statistics of traffic flows and the use of machine learning techniques. However, due to the high cost of manual labeling, it is hard to obtain sufficient, reliable and up-to-date labeled data for effective IP traffic classification. This paper proposes a novel semi-supervised approach, called SemTra, which automatically alleviates the shortage of labeled flows for machine learning by exploiting the advantages of both supervised and unsupervised models. In particular, SemTra involves the following: (i) generating multi-view representations of the original data based on dimensionality reduction methods to have strong discrimination ability, (ii) incorporating the generated representations into the ensemble clustering model to provide a combined clustering output with better quality and stability, (iii) adapting the concept of self-training to iteratively utilize the few labeled data along with unlabeled within local and global viewpoints; and (iiii) obtaining the final class decision by combining the decisions of mapping strategy of clusters, the local self-training and global self-training approaches. Extensive experiments were carried out to compare the effectiveness of SemTra over representative semi-supervised methods using sixteen network traffic datasets.
Adil Fahad; Abdulmohsen Almalawi; Zahir Tari; Kurayman Alharthi; Fawaz S. Al Qahtani; Mohamed Cheriet. SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort. Pattern Recognition 2019, 91, 1 -12.
AMA StyleAdil Fahad, Abdulmohsen Almalawi, Zahir Tari, Kurayman Alharthi, Fawaz S. Al Qahtani, Mohamed Cheriet. SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort. Pattern Recognition. 2019; 91 ():1-12.
Chicago/Turabian StyleAdil Fahad; Abdulmohsen Almalawi; Zahir Tari; Kurayman Alharthi; Fawaz S. Al Qahtani; Mohamed Cheriet. 2019. "SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort." Pattern Recognition 91, no. : 1-12.