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Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

Journal of cyber security and Mobility 2022年第3期11卷 279-320页

作者： Somesha, M. Pais, Alwyn R. Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology karnataka Karnataka Surathkal575025 India

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.

关键词： Embeddings

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Optimizing Diabetes Prediction: A Systematic Study of Machine Learning Techniques and Data Enhancements 5

Optimizing Diabetes Prediction: A Systematic Study of Machin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajender, R. Kothapalli, Chaitanya Deepthi Gorikapudi, Sateesh Pavan Kumar, V.S. Pachhala, Nagababu Neelima, P. Lendi Institute of Engineering and Technology Department of CSE Vizianagaram India Sir C R Reddy College of Engineering Department of CSE Eluru India Koneru Lakshmaiah Education Foundation Green Fields Department of CSE Vaddeswaram India Malla Reddy University Department of Cyber Security Hyderabad India Vasireddy Venkatadri Institute of Technology Department of IT Guntur India SRKR Engineering College Department of Computer Science and Artificial Intelligence Bhimavaram India

ISBN: (纸本)9798331530013

This study focuses on the application of different machine learning algorithms to derive an efficient and effective predictive model for screening for diabetes prediction. The main objective is to improve the model performance and time to learn and generalize by doing features engineering, feature selection, and hyperparameter tuning all appropriate. The approach starts with data pre-processing, where issues such as missing or wrong values are solved and duplicate records are removed on a given dataset from Kaggle. The ADSYAN algorithm is used for class imbalance;new samples from the minority class are synthesized, while the DBSCAN algorithm is used to detect outliers in datasets. The necessary features are obtained using the Grey Wolf Recursive Elimination (GWRE), the combination of the Grey Wolf Optimization (GWO) technique and Recursive Feature Elimination (RFE). The model is built through the Gradient-Enhanced Logistic Regression (GELR) model, which is a hybrid of both Gradient boosting and Logistic Regression where gradient boosting is used to derive a non-linear relationship, integrated with logistic regression for a linear decision boundary. A final tuning of the models is done using Grid Search as the Hyperparameter optimization technique. These show that the overall performance achieves 91.8 % accuracy while the precision, recall, and F1 values were at 89.6%, 82.1%, and 87.5% respectively. This paper has highlighted the suitability of the proposed methodology in enhancing predictive performance for diabetes detection to a considerable extent valuable to clinical application. © 2024 IEEE.

关键词： Logistic regression

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer Systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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CryptoPyt: Unraveling Python Cryptographic APIs Misuse with Precise Static Taint Analysis 40

CryptoPyt: Unraveling Python Cryptographic APIs Misuse with ...

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40th Annual computer security Applications Conference, ACSAC 2024

作者： Guo, Xiangxin Jia, Shijie Lin, Jingqiang Ma, Yuan Zheng, Fangyu Li, Guangzheng Xu, Bowen Cheng, Yueqiang Ji, Kailiang School of Cyber Security University of Science and Technology of China Hefei China Key Laboratory of Cyberspace Security Defense Institute of Information Engineering CAS Beijing China School of Cryptology University of Chinese Academy of Sciences Beijing China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China NIO China

ISBN: (纸本)9798331520885

Cryptographic APIs are essential for ensuring the security of software systems. However, many research studies have revealed that the misuse of cryptographic APIs is commonly widespread. Detecting such misuse in Python poses challenges due to its intricate features, including dynamic features and pass-by-object-reference. Existing tools lack the precision and accuracy to tackle these challenges, leading to both high false positives and false negatives. In this work, we propose a specific Python Cryptographic Abstract Syntax Tree (PCAST) to represent the structure of source code, which rewrites AST nodes to handle complex Python features. Based on PCAST, we design and implement CryptoPyt, a static code analysis tool that leverages precise taint analysis and 17 cryptographic misuse rules to automatically identify potential cryptographic APIs misuse in Python projects. We conduct an in-depth analysis of all the APIs within the popular 21 Python cryptographic libraries and design five kinds of taint detectors to perform intra-procedural and inter-function analysis on the APIs and arguments. To demonstrate the effectiveness of CryptoPyt, we conduct experiments with six state-of-the-art tools (i.e., Cryptolation, LICMA, Bandit, Dlint, Semgrep and CodeQL) on both the labeled benchmark PyCryptoBench and the real-world Python cryptographic projects datasets PCAMD. Our evaluations show that CryptoPyt achieves an F1 score of 0.80 on PyCryptoBench and a recall rate of 99.08% on PCAMD. Furthermore, we disclose the discovered critical issues to the developers and seven high-level CVE IDs have been assigned to these findings. Our tool contributes to enhancing the security of Python cryptographic software. © 2024 IEEE.

关键词： Python

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security Analysis of OAuth 2.0 Implementation

Security Analysis of OAuth 2.0 Implementation

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sharma, Swarag Jevitha, K.P. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Many users all over the world routinely use open authentication and authorization providers based on OAuth 2.0 framework such as Google, Facebook etc. to sign in to third-party websites (relying party). The authentication (OpenID connect) and authorization (OAuth) protocol based on OAuth 2.0 framework enables the relying party application to authenticate its users and gain access to their protected resources without storing any of the sensitive user credentials such as usernames and passwords. Hence, the implementation of these protocols by the relying parties and identity providers play an important role in security of the user information and their privacy. The risk of attacks on improper implementation of these protocols can cause unauthorized access to the user accounts. In this work, we focused on testing the OAuth implementations done by the relying parties. For the secure implementation of OAuth 2.0 we found that following parameters must be present while generating OAuth request: client_id, state, response_type, redirect_uri and scope. We have analyzed our work with around 75 websites that were using Google as an identity provider to provide access to its users. Out of 75 relying parties, 30% were found vulnerable to CSRF attack due to missing state parameter, 13% used implicit flow which reveals id_token or access_token in the OAuth URL and 2% had secret parameters in OAuth URL. We developed a browser extension which successfully identify and alert its user on these issues. © 2023 IEEE.

关键词： OAuth 2.0 security Analysis of OAuth 2.0 Tokens Unauthorized Access

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Network Intrusion Detection in Cloud Computing Environments 3

Network Intrusion Detection in Cloud Computing Environments

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Sreeramulu, Madhusudhan Dasari Suganyadevi, K. Neravetla, Anuteja Reddy Mohammed, Abdul Sajid American Express Dept of Cyber Security United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India University of the Cumberlands Department of Information Technology KY United States University of the Cumberlands Dept of School of Computer and Information Sciences KY United States

ISBN: (纸本)9798350384598

The increasing use of cloud computing has also resulted in a higher vulnerability to cyber attacks. Network Intrusion Detection Systems (NIDS) play a vital role in protecting cloud computing environments from these attacks. However, the unique characteristics of cloud computing, such as its dynamic and distributed nature, pose challenges for traditional NIDS. This paper provides an overview of the current state of NIDS in cloud computing environments, including their limitations and potential solutions. It also discusses the use of machine learning and artificial intelligence techniques in improving NIDS performance. The effectiveness of NIDS in detecting both known and unknown attacks is crucial for the security of cloud computing environments and requires continuous research and development. © 2024 IEEE.

关键词： Cloud security

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FedDiSC:A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

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Energy and AI 2023年第4期14卷 212-220页

作者： Muhammad Akbar Husnoo Adnan Anwar Haftu Tasew Reda Nasser Hosseinzadeh Shama Naz Islam Abdun Naser Mahmood Robin Doss Centre for Cyber Security Research and Innovation(CSRI) School of Information TechnologyDeakin UniversityGeelongVIC 3216Australia Centre for Smart Power and Energy Research(CSPER) School of EngineeringDeakin UniversityGeelongVIC 3216Australia Department of Computer Science&IT Latrobe UniversityBundooraVIC 3086Australia

With the growing concern about the security and privacy of smart grid systems,cyberattacks on critical power grid components,such as state estimation,have proven to be one of the top-priority cyber-related issues and have received significant attention in recent ***,cyberattack detection in smart grids now faces new challenges,including privacy preservation and decentralized power zones with strategic data *** address these technical bottlenecks,this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework,known as FedDiSC,that enables Discrimination between power System disturbances and ***,we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related ***,we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity ***,to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs,we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication *** simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related ***,we find that the gradient quantization scheme utilized improves communication efficiency by 40%when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

关键词： Federated Learning Anomaly detection cyber attack Internet of things(IoT) Smart grid

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The Internet of Things: a domain-specific security requirement classification 4

The Internet of Things: a domain-specific security requireme...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Mukalazi, Arafat Boyaci, Ali Istanbul Commerce University Department of Cyber Security Istanbul Turkey Istanbul Commerce University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9781665468350

Worldwide, societies are rapidly becoming more connected, owing primarily to the growing number of intelligent things and smart applications (e.g, smart automobiles, smart wearable devices, etc.) These have occurred in tandem with the Internet Of Things, a new method of connecting the physical and virtual worlds. It is a new promising paradigm whereby every 'thing' can connect to anything via the Internet. However, with IoT systems being deployed even on large-scale, security concerns arise amongst other challenges. Hence the need to allocate appropriate protection of resources. The realization of secure IoT systems could only be accomplished with a comprehensive understanding of the particular needs of a specific system. How-ever, this paradigm lacks a proper and exhaustive classification of security requirements. This paper presents an approach towards understanding and classifying the security requirements of IoT devices. This effort is expected to play a role in designing cost-efficient and purposefully secured future IoT systems. During the coming up with and the classification of the requirements, We present a variety of set-ups and define possible attacks and threats within the scope of IoT. Considering the nature of IoT and security weaknesses as manifestations of unrealized security requirements, We put together possible attacks and threats in categories, assessed the existent IoT security requirements as seen in literature, added more in accordance with the applied domain of the IoT and then classified the security requirements. An IoT system can be secure, scalable, and flexible by following the proposed security requirement classification. © 2022 IEEE.

关键词： Internet of things

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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