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| 1 | A Prospective Randomized, Controlled Study Comparing Low Pressure Versus High Pressure Pneumoperitoneum During Laparoscopic Cholecystectomy显示文摘 | Vismit Pradyumna Joshipura Sanjiv P. Haribhakti Nitin R. Patel Rahul P. Naik Harshad N. Soni Bhavin Patel Mahendra S. Bhavsar Mahendra B. Narwaria Rashmi Thakker | 2009 | Surgical Laparoscopy, Endoscopy & Percutaneous Techniques2009，，3： | 1 |
| 2 | Classification of Adversarial Attacks Using Ensemble Clustering Approach显示文摘As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of attacks.To safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known intrusions.There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive traffics.The current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network applications.In contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class labels.These ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their combinations.Based on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the literature.This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated intrusions.In addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use. | Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 3 | An Immunization Scheme for Ransomware显示文摘In recent years,as the popularity of anonymous currencies such as Bitcoin has made the tracking of ransomware attackers more difficult,the amount of ransomware attacks against personal computers and enterprise production servers is increasing rapidly.The ransomware has a wide range of influence and spreads all over the world.It is affecting many industries including internet,education,medical care,traditional industry,etc.This paper uses the idea of virus immunity to design an immunization solution for ransomware viruses to solve the problems of traditional ransomware defense methods(such as anti-virus software,firewalls,etc.),which cannot meet the requirements of rapid detection and immediate prevention of new outbreaks attacks.Our scheme includes two parts:server and client.The server provides an immune configuration file and configuration file management functions,including a configuration file module,a cryptography algorithm module,and a display module.The client obtains the immunization configuration file from server in real time,and performs the corresponding operations according to the configuration file to make the computer have an immune function for a specific ransomware,including an update module,a configuration file module,a cryptography algorithm module,a control module,and a log module.This scheme controls mutexes,services,files and registries respectively,to destroy the triggering conditions of the virus and finally achieve the purpose of immunizing a computer from a specific ransomware. | Jingping Song Qingyu Meng Chenke Luo Nitin Naik Jian Xu | 2020 | Computers, Materials & Continua2020，，8： | 0 |
| 4 | Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption显示文摘Outsourcing the k-Nearest Neighbor(kNN)classifier to the cloud is useful,yet it will lead to serious privacy leakage due to sensitive outsourced data and models.In this paper,we design,implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption(kNNCM-MKHE).We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan(BGV)for collaborative evaluation of the kNN classifier provided by multiple model owners.Analyze the operations of kNN and extract basic operations,such as addition,multiplication,and comparison.It supports the computation of encrypted data with different public keys.At the same time,we further design a new scheme that outsources evaluation works to a third-party evaluator who should not have access to the models and data.In the evaluation process,each model owner encrypts the model and uploads the encrypted models to the evaluator.After receiving encrypted the kNN classifier and the user’s inputs,the evaluator calculated the aggregated results.The evaluator will perform a secure computing protocol to aggregate the number of each class label.Then,it sends the class labels with their associated counts to the user.Each model owner and user encrypt the result together.No information will be disclosed to the evaluator.The experimental results show that our new system can securely allow multiple model owners to delegate the evaluation of kNN classifier. | Chen Wang Jian Xu Jiarun Li Yan Dong Nitin Naik | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
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