XAI-AMD-DL: An Explainable AI Approach for Android Malware Detection System Using Deep Learning DOI
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar

et al.

2022 IEEE World Conference on Applied Intelligence and Computing (AIC), Journal Year: 2023, Volume and Issue: unknown

Published: July 29, 2023

Efficient malware identification is essential to safe the system resources and privacy of data for cybersecurity system. The use android smartphones has increased tremendously that attracting various types attacks. Nowadays, writers Artificial Intelligence (AI)-enabled attack techniques bypass detection malicious activities. Hence, designing an efficient, effective robust identify variants remains a critical problem challenge. However, number deep learning (DL) models applied in existing methods at large scale, but these actually lacks interpretability explain contribution each features Therefore, this paper propose Explainable (XAI) based hybrid Convolutional Neural network (CNN) Bi-Gated Recurrent Unit (Bi-GRU) Android Malware Detection (AMD) System using DL named as XAI-AMD-DL. proposed model evaluated CICAndMal2019 dataset. results obtained by XAI-AMD-DL 97.98% accuracy, 97.75 %, 97.76%, 97.75% precision, recall f1score, respectively outperforms models.

Language: Английский

A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks DOI Creative Commons
Arun Kumar Dey, Govind P. Gupta, Satya Prakash Sahu

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100206 - 100206

Published: March 24, 2023

Internet of Things (IoT) enabled networks are highly vulnerable to cyber threats due insecure wireless communication, resource constraint architecture, different types IoT devices, and a high volume sensor data being transported across the network. Therefore, IoT-compatible cybersecurity solutions required. An intrusion detection system is one most common for detecting in IoT-enabled networks. However, existing threat suffer from many issues like poor accuracy, learning complexity, low scalability, false positive rate (FPR). We propose metaheuristic-based intelligent novel framework using ensemble feature selection classification approaches overcome these issues. First, designed Binary Gravitational Search Algorithm (BGSA) Grey Wolf Optimization (BGWO) get an optimized set features avoid curse dimensionality efficient learning. Next, Decision Tree learning-based techniques such as AdaBoost Random Forest (RF) employed separately detect classify threats. The UNSW-NB15 dataset assesses effectiveness proposed framework, its performance evaluated against recent state-of-the-art frameworks. Based on result analysis, it found that RF outperforms modern methods subset (4 out 42), maximum accuracy (99.41%), (99.09%), F1-score (99.33%) with lowest FPR (0.03%).

Language: Английский

Citations

44
Advancing cybersecurity: a comprehensive review of AI-driven detection techniques DOI Creative Commons

A Salem,

Safaa M. Azzam,

O. E. Emam

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 4, 2024

Abstract As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways detect prevent them. Recognizing cyber threats quickly accurately is crucial because they can cause severe damage individuals businesses. This paper takes a close look at how we use artificial intelligence (AI), including machine learning (ML) deep (DL), alongside metaheuristic algorithms better. We've thoroughly examined over sixty recent studies measure effective these AI tools are identifying fighting wide range threats. Our research includes diverse array cyberattacks such as malware attacks, network intrusions, spam, others, showing that ML DL methods, together with algorithms, significantly improve well find respond We compare methods out what they're where could improve, especially face new changing cyber-attacks. presents straightforward framework for assessing Methods in threat detection. Given complexity threats, enhancing regularly ensuring strong protection critical. evaluate effectiveness limitations current proposed models, addition algorithms. vital guiding future enhancements. We're pushing smart flexible solutions adapt challenges. The findings from our suggest protecting against will rely on continuously updating stay ahead hackers' latest tricks.

Language: Английский

Citations

36
RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques DOI Creative Commons
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 10, 2024

Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection exacerbated by the dimensionality and complexity gene expression data, which complicates classification process.

Language: Английский

Citations

13
Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review DOI Creative Commons
Shubhkirti Sharma, Vijay Kumar, Kamlesh Dutta

et al.

Internet of Things and Cyber-Physical Systems, Journal Year: 2024, Volume and Issue: 4, P. 258 - 267

Published: Jan. 1, 2024

The significance of intrusion detection systems in networks has grown because the digital revolution and increased operations. method classifies network traffic as threat or normal based on data features. Intrusion system faces a trade-off between various parameters such accuracy, relevance, redundancy, false alarm rate, other objectives. paper presents systematic review Internet Things (IoT) using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities reducing chances attacks. MOAs provide set optimized solutions for process highly complex IoT networks. This identification multiple objectives detection, comparative analysis their approaches, datasets used evaluation. show encouraging potential enhance conflicting detection. Additionally, current challenges future research ideas are identified. In addition demonstrating new advancements techniques, this study gaps that can be addressed while designing

Language: Английский

Citations

11
A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks DOI Creative Commons
Aya G. Ayad, Nehal A. Sakr, Noha A. Hikal

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(19), P. 26942 - 26984

Published: Aug. 29, 2024

Abstract The exponential growth of Internet Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in IoT community has centered on effective traffic detection models, with a particular focus anomaly intrusion systems (AIDS). This paper specifically addresses preprocessing stage datasets and feature selection approaches to reduce complexity data. goal is develop an efficient AIDS that strikes balance between high accuracy low time. To achieve this goal, we propose hybrid approach combines filter wrapper methods. integrated into two-level system. At level 1, our classifies network packets normal or attack, 2 further classifying attack determine its specific category. One critical aspect consider imbalance these datasets, which addressed using Synthetic Minority Over-sampling Technique (SMOTE). evaluate how selected features affect performance machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, k-Nearest Neighbor, employ benchmark datasets: BoT-IoT, TON-IoT, CIC-DDoS2019. Evaluation metrics encompass accuracy, precision, recall, F1-score. Results indicate decision tree achieves ranging 99.82 100%, short times 0.02 0.15 s, outperforming existing architectures networks establishing superiority achieving both times.

Language: Английский

Citations

10
HYRIDE: HYbrid and Robust Intrusion DEtection Approach for Enhancing Cybersecurity in Industry 4.0 DOI Creative Commons
Shubham Srivastav, Amit K. Shukla,

Sandeep Kumar

et al.

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101492 - 101492

Published: Jan. 1, 2025

Language: Английский

Citations

1
Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks DOI Creative Commons

Saad Said Alqahtany,

Asadullah Shaikh, Ali Alqazzaz

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 14, 2025

Smart devices are enabled via the Internet of Things (IoT) and connected in an uninterrupted world. These pose a challenge to cybersecurity systems due attacks network communications. Such have continued threaten operation end-users. Therefore, Intrusion Detection Systems (IDS) remain one most used tools for maintaining such flaws against cyber-attacks. The dynamic multi-dimensional threat landscape IoT increases Traditional IDS. focus this paper aims find key features developing IDS that is reliable but also efficient terms computation. Enhanced Grey Wolf Optimization (EGWO) Feature Selection (FS) implemented. function EGWO remove unnecessary from datasets intrusion detection. To test new FS technique decide on optimal set based accuracy achieved feature taking filters, recent approach relies NF-ToN-IoT dataset. selected evaluated by using Random Forest (RF) algorithm combine multiple decision trees create accurate result. experimental outcomes procedures demonstrate capacity recommended classification methods determine Analysis results presents performs more effectively than other techniques with optimized (i.e., 23 out 43 features), high 99.93% improved convergence.

Language: Английский

Citations

1
Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System DOI Creative Commons
Yakub Kayode Saheed,

Temitope Olubanjo Kehinde,

Mustafa Ayobami Raji

et al.

Journal of Information and Telecommunication, Journal Year: 2023, Volume and Issue: 8(2), P. 189 - 207

Published: Nov. 6, 2023

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed the past for attribute selection IDS performance improvement. However, these methods often fallen short terms of accuracy, rate, precision, F-score. To tackle issues, paper presents a novel hybrid feature approach combining Bat metaheuristic algorithm with Residue Number System (RNS). Initially, is utilized partition training data eliminate irrelevant attributes. Recognizing algorithm's slower testing times, RNS incorporated processing speed. Additionally, principal component analysis (PCA) employed extraction. In second phase, excluded selection, allowing perform this task while PCA handles Subsequently, classification conducted using naive bayes, k-Nearest Neighbors. Experimental results demonstrate remarkable effectiveness algorithm, achieving outstanding rates, F-scores. Notably, fusion doubles The findings are further validated through benchmarking against methods, establishing their competitiveness.

Language: Английский

Citations

17
A Hybrid Meta-heuristics Algorithm: XGBoost-Based Approach for IDS in IoT DOI
Soumya Bajpai, Kapil Sharma, Brijesh Kumar Chaurasia

et al.

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(5)

Published: May 10, 2024

Language: Английский

Citations

6
Leveraging Metaheuristics for Feature Selection With Machine Learning Classification for Malicious Packet Detection in Computer Networks DOI Creative Commons

Aganith Shanbhag,

Shweta Vincent,

Bore Gowda S B

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 21745 - 21764

Published: Jan. 1, 2024

Robust Intrusion Detection Systems (IDS) are increasingly necessary in the age of big data due to growing volume, velocity, and variety generated by modern networks. Metaheuristic algorithms offer a promising approach enhance IDS performance terms optimal feature selection. Combining these along with Machine learning (ML) for creation an makes it possible improve detection accuracy, reduce false positives negatives, efficiency network monitoring. Our study proposes using metaheuristic machine classifiers selection optimize number features from set computer traffic. We have tested several combinations viz., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Grey Wolf Optimizer (GWO) ML Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB) Logistic Regression (LR). The been over NSS-KDD kddcupdata_10% sets. drawn insights on scores respect test scores, FI recall precision various algorithm combinations. time has also highlighted showcase fastest-performing Ultimately, we presented three depending organizational requirements provided separate solutions each.

Language: Английский

Citations

5