- I have completed my MS-Computer Science degree from University of Lahore- Pakistan since 2016. I got my Master Degree in Information Technology (MSc- IT) from University of the Punjab since 2007.I have more than ten years Web / Desktop Applications Programming and Data Science experience.Recently... moreI have completed my MS-Computer Science degree from University of Lahore- Pakistan since 2016. I got my Master Degree in Information Technology (MSc- IT) from University of the Punjab since 2007.I have more than ten years Web / Desktop Applications Programming and Data Science experience.Recently, I am working as Programmer in reputed educational organisation named as BISE, Lahore.edit
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the... more
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the terms and conditions of an agreement that lies between untrustworthy parties in which the trusted third parties cannot interfere. The cryptography logic used in smart contract enables the blockchain network to provide trust and authority to all parties in transaction. Network decentralization, data immutability and transparency, resiliency and security make blockchain technology more versatile. Recently, it has become a potential quality and capability of IoT to connect uncountable electronic objects or devices at the same time. The most prominent feature of blockchain-based IoT applications is the integration of smart contracts between blockchain and IoT.A brief comparison has been given in the paper that how the smart contracts react on multiple blockchain platforms with respect to scalability, system complexity and consensus protocol factors. Furthermore, the context of Smart contract integration between blockchain and IoT with highlighting the integration opportunities and challenges along with future research directions. Therefore, we have concluded in the current paper that amalgamation of Blockchain with IoT through Smart Contract can provide a strong framework for distributed application and the newly introduced business communities.
Research Interests:
Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for... more
Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.
Research Interests:
Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for... more
Intrusion detection is one of the most prominent
and challenging problem faced by cybersecurity organizations.
Intrusion Detection System (IDS) plays a vital role in identifying
network security threats. It protects the network for vulnerable
source code, viruses, worms and unauthorized intruders for
many intranet/internet applications. Despite many open source
APIs and tools for intrusion detection, there are still many
network security problems exist. These problems are handled
through the proper pre-processing, normalization, feature
selection and ranking on benchmark dataset attributes prior to
the enforcement of self-learning-based classification algorithms.
In this paper, we have performed a comprehensive comparative
analysis of the benchmark datasets NSL-KDD and CIDDS-001.
For getting optimal results, we have used the hybrid feature
selection and ranking methods before applying self-learning
(Machine / Deep Learning) classification algorithmic approaches
such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and
DAE. We have analyzed the performance of IDS through some
prominent performance indicator metrics such as Accuracy,
Precision, Recall and F1-Score. The experimental results show
that k-NN, SVM, NN and DNN classifiers perform approx. 100%
accuracy regarding performance evaluation metrics on the NSLKDD
dataset whereas k-NN and Naïve Bayes classifiers perform
approx. 99% accuracy on the CIDDS-001 dataset.
and challenging problem faced by cybersecurity organizations.
Intrusion Detection System (IDS) plays a vital role in identifying
network security threats. It protects the network for vulnerable
source code, viruses, worms and unauthorized intruders for
many intranet/internet applications. Despite many open source
APIs and tools for intrusion detection, there are still many
network security problems exist. These problems are handled
through the proper pre-processing, normalization, feature
selection and ranking on benchmark dataset attributes prior to
the enforcement of self-learning-based classification algorithms.
In this paper, we have performed a comprehensive comparative
analysis of the benchmark datasets NSL-KDD and CIDDS-001.
For getting optimal results, we have used the hybrid feature
selection and ranking methods before applying self-learning
(Machine / Deep Learning) classification algorithmic approaches
such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and
DAE. We have analyzed the performance of IDS through some
prominent performance indicator metrics such as Accuracy,
Precision, Recall and F1-Score. The experimental results show
that k-NN, SVM, NN and DNN classifiers perform approx. 100%
accuracy regarding performance evaluation metrics on the NSLKDD
dataset whereas k-NN and Naïve Bayes classifiers perform
approx. 99% accuracy on the CIDDS-001 dataset.
Research Interests:
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the... more
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the terms and conditions of an agreement that lies between untrustworthy parties in which the trusted third parties cannot interfere. The cryptography logic used in smart contract enables the blockchain network to provide trust and authority to all parties in transaction. Network decentralization, data immutability and transparency, resiliency and security make blockchain technology more versatile. Recently, it has become a potential quality and capability of IoT to connect uncountable electronic objects or devices at the same time. The most prominent feature of blockchain-based IoT applications is the integration of smart contracts between blockchain and IoT. A brief comparison has been given in the paper that how the smart contracts react on multiple blockchain platforms with respect to scalability, system complexity and consensus protocol factors. Furthermore, the context of Smart contract integration between blockchain and IoT with highlighting the integration opportunities and challenges along with future research directions. Therefore, we have concluded in the current paper that amalgamation of Blockchain with IoT through Smart Contract can provide a strong framework for distributed application and the newly introduced business communities. Index Terms-Smart Contracts, Internet of Things (IoT), Blockchain, Decentralize Applications (DApps).
Research Interests:
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the... more
A Smart Contract is self-executable and self-enforceable program code that runs on the top of blockchain to manage complex business logic. It eliminates the need of extrinsic enforcement of legal agreements. Furthermore, it enforces the terms and conditions of an agreement that lies between untrustworthy parties in which the trusted third parties cannot interfere. The cryptography logic used in smart contract enables the blockchain network to provide trust and authority to all parties in transaction. Network decentralization, data immutability and transparency, resiliency and security make blockchain technology more versatile. Recently, it has become a potential quality and capability of IoT to connect uncountable electronic objects or devices at the same time. The most prominent feature of blockchain-based IoT applications is the integration of smart contracts between blockchain and IoT. A brief comparison has been given in the paper that how the smart contracts react on multiple blockchain platforms with respect to scalability, system complexity and consensus protocol factors. Furthermore, the context of Smart contract integration between blockchain and IoT with highlighting the integration opportunities and challenges along with future research directions. Therefore, we have concluded in the current paper that amalgamation of Blockchain with IoT through Smart Contract can provide a strong framework for distributed application and the newly introduced business communities. Index Terms-Smart Contracts, Internet of Things (IoT), Blockchain, Decentralize Applications (DApps).