Article
Computer Science, Information Systems
Gustavo De Carvalho Bertoli, Lourenco Alves Pereira Junior, Osamu Saotome, Aldri L. Dos Santos, Filipe Alves Neto Verri, Cesar Augusto Cavalheiro Marcondes, Sidnei Barbieri, Moises S. Rodrigues, Jose M. Parente De Oliveira
Summary: The AB-TRAP framework is introduced to address challenges in network intrusion detection systems, utilizing up-to-date network traffic and attacks, and providing a reproducible solution. The implementation of this framework in local and global environments showed successful detection of TCP port scanning attacks, with emphasis on model deployment and performance evaluation.
Review
Computer Science, Information Systems
Ayesha S. Dina, D. Manivannan
Summary: Intrusions in computer networks have been on the rise in the past decade, leading researchers to propose signature-based and anomaly-based intrusion detection methods, with Machine Learning techniques playing a key role. This paper provides a comprehensive critical survey of ML-based intrusion detection approaches in the literature over the last ten years, highlighting some open issues for future research.
INTERNET OF THINGS
(2021)
Article
Computer Science, Theory & Methods
Souradip Roy, Juan Li, Bong-Jin Choi, Yan Bai
Summary: The increasing popularity of the Internet of Things has led to more security breaches associated with vulnerable IoT devices, emphasizing the importance of employing intrusion detection techniques. Traditional intrusion detection mechanisms may not work well for IoT environments, leading to the proposal of a novel intrusion detection model utilizing machine learning. Through optimizations such as removal of multicollinearity and dimensionality reduction, the model shows promising results with high detection rates and low false alarm rates in experiments on popular datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Bakht Sher Ali, Inam Ullah, Tamara Al Shloul, Izhar Ahmed Khan, Ijaz Khan, Yazeed Yasin Ghadi, Akmalbek Abdusalomov, Rashid Nasimov, Khmaies Ouahada, Habib Hamam
Summary: The growing volume of data, particularly imbalanced datasets, presents challenges in identifying cyberattacks on industrial control systems (ICS) networks. This study proposes an instance-based intrusion detection technique called ICS-IDS, specifically for SCADA networks in ICS systems. The technique utilizes data preparation and detection components to improve accuracy in detecting sophisticated attack vectors.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Multidisciplinary Sciences
Ebtihaj Alshahrani, Daniyal Alghazzawi, Reem Alotaibi, Osama Rabie
Summary: The research conducted experiments on adversarial machine learning, indicating that evasion attacks had a significant impact on the accuracy of machine learning-based network intrusion detection systems.
Article
Computer Science, Information Systems
Souradip Roy, Juan Li, Yan Bai
Summary: In this paper, the authors investigate intrusion detection techniques for IoT networks and propose a machine learning-based two-layer hierarchical intrusion detection mechanism. The proposed approach outperforms existing methods in terms of accuracy and performance, and offers advantages in improving service time, reducing delay, and optimizing energy utilization.
INTERNET OF THINGS
(2022)
Article
Computer Science, Hardware & Architecture
Christabelle Alvares, Dristi Dinesh, Syed Alvi, Tannish Gautam, Maheen Hasib, Ali Raza
Summary: This data article introduces a dataset for training intrusion detection and prevention system algorithms, suitable for the unified communications field of VoIP networks. It provides information on the design and implementation of real enterprise VoIP networks, presents attack tools and data results in sub-datasets, and offers guidance on utilizing the dataset.
Article
Computer Science, Hardware & Architecture
Mehrnoosh Monshizadeh, Vikramajeet Khatri, Raimo Kantola, Zheng Yan
Summary: This paper presents an architecture based on associated density clustering for analyzing non-labeled data in intrusion detection. The proposed architecture applies clustering techniques to categorize unknown traffic and only analyzes one packet from each cluster to generalize the result to all packets within the cluster. The architecture uses multiple unsupervised algorithms and a co-association matrix to detect attack clusters of any shape and automatically determines the best number of clusters.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Review
Green & Sustainable Science & Technology
Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer, Kuburat Oyeranti Adefemi Alimi
Summary: SCADA systems are crucial for remote access, monitoring, and control of critical infrastructures globally, but are exposed to security challenges. Effective detection and classification of SCADA system intrusions are pivotal for ensuring operational stability of national infrastructures.
Article
Computer Science, Theory & Methods
Zhiyan Chen, Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein T. Mouftah, Petar Djukic
Summary: Despite the technological benefits of the Internet of Things (IoT), there are cyber weaknesses due to vulnerabilities in the wireless medium. Machine Learning (ML)-based methods are effective against cyber threats in IoT networks. However, it is challenging to apply ML-based approaches to detect Advanced Persistent Threat (APT) attacks due to their low occurrence in normal traffic. Limited surveys have been conducted on APT attacks in IoT networks, mainly due to the lack of public datasets. This survey article reviews security challenges, well-known attacks, and intrusion detection methods for IoT networks, with a focus on ML-based approaches.
ACM COMPUTING SURVEYS
(2023)
Article
Mathematical & Computational Biology
Yue Li, Wusheng Xu, Wei Li, Ang Li, Zengjin Liu
Summary: This paper proposes a hybrid intrusion detection method that utilizes ADASYN and ID3 decision tree to improve the effectiveness of intrusion detection rate. The model based on ADASYN and ID3 decision tree achieves higher accuracy and lower false alarm rate, making it more suitable for intrusion detection tasks.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Review
Computer Science, Information Systems
Asmaa Halbouni, Teddy Surya Gunawan, Mohamed Hadi Habaebi, Murad Halbouni, Mira Kartiwi, Robiah Ahmad
Summary: This paper reviews intrusion detection systems and discusses the types of learning algorithms used by machine learning and deep learning to protect data from malicious behavior. It further discusses recent work on machine learning and deep learning, including various network implementations, applications, algorithms, learning approaches, and datasets, to develop an operational intrusion detection system.
Article
Computer Science, Artificial Intelligence
Mohammed Sayeeduddin Habeeb, T. Ranga Babu
Summary: Recent high data rate requirements have led to the expansion of communication systems and networks, resulting in increased security threats. To address these threats, researchers have proposed intrusion detection system (IDS) solutions based on artificial intelligence (AI). However, IDSs face a challenge of increased false alarm rate (FAR) in detecting zero-day attacks.
Article
Computer Science, Hardware & Architecture
Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Eider Iturbe, Erkuden Rios, Saturnino Martinez, Antonios Sarigiannidis, Georgios Eftathopoulos, Yannis Spyridis, Achilleas Sesis, Nikolaos Vakakis, Dimitrios Tzovaras, Emmanouil Kafetzakis, Ioannis Giannoulakis, Michalis Tzifas, Alkiviadis Giannakoulias, Michail Angelopoulos, Francisco Ramos
Summary: The advancement of smart grid technology has introduced new benefits and challenges. SIEM systems are a promising technology for enhancing cybersecurity in smart grids, but current systems do not address the unique characteristics of smart grids. The SPEAR SIEM focuses on enhancing security performance specifically for smart grids.
Article
Computer Science, Information Systems
Dania Herzalla, Willian Tessaro Lunardi, Martin Andreoni
Summary: This paper introduces the TII-SSRC-23 dataset, which is diverse and aligned with the contemporary network environment, providing an important tool for intrusion detection. Additionally, the study analyzes the importance of features and establishes benchmarks for intrusion detection methodologies through experiments.
Article
Computer Science, Software Engineering
MohammadReza HoseinyFarahabady, Javid Taheri, Albert Y. Zomaya, Zahir Tari
Summary: This article presents a CPU throttling control strategy to optimize the energy consumption of the Apache Storm platform, and validates its effectiveness in a multi-core system.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Theory & Methods
Mahdi Fahmideh, John Grundy, Aakash Ahmad, Jun Shen, Jun Yan, Davoud Mougouei, Peng Wang, Aditya Ghose, Anuradha Gunawardana, Uwe Aickelin, Babak Abedin
Summary: This article presents a systematic literature review of the state-of-the-art in Blockchain-based Software (BBS) engineering research from the perspective of software engineering discipline. It provides a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques in BBS engineering.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Software Engineering
Dulaji Hidellaarachchi, John Grundy, Rashina Hoda, Ingo Mueller
Summary: Requirements Engineering (RE)-related activities heavily rely on collaboration between various roles in software engineering (SE), making it highly human-dependent. This study aims to understand the influence of human aspects, particularly motivation and personality, on RE-related activities from the perspective of software practitioners. The findings highlight the importance of motivation, domain knowledge, attitude, communication skills, and personality in RE-related activities, providing insights into key motivational factors and desirable personality characteristics for effective involvement in RE. The study also identifies areas needing further investigation and provides recommendations for future research.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2023)
Article
Computer Science, Information Systems
Mohit Kumar, Avadh Kishor, Jitendra Kumar Samariya, Albert Y. Zomaya
Summary: The Internet of Things (IoT) has transformed the industry by providing various facilities and advancements. To meet the requirements of the industrial IoT system, an autonomic workload prediction and resource allocation framework is introduced. This framework efficiently allocates resources among fog nodes (FNs) based on workload prediction using a deep autoencoder (DAE) model and optimal FN selection using the crow search algorithm (CSA). The proposed scheme outperforms existing optimization models in terms of cost, delay, and workload execution.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Warodom Werapun, Tanakorn Karode, Tanwa Arpornthip, Jakapan Suaboot, Esther Sangiamkul, Pawita Boonrat
Summary: Decentralized finance (DeFi) has gained significant popularity and a billion-dollar market cap. However, uncollateralized lending, also known as a flash loan, has become a major tool used by attackers to exploit DeFi networks. This paper introduces the Flash loan Attack Analysis (FAA) framework, which provides a quantitative analysis of the impacts of different factors on the effectiveness of preventative measures in the DeFi system. The simulation results show that the existing strategy can fully protect the platform in a normal market environment but fails in a highly volatile market, causing significant financial losses.
Article
Computer Science, Software Engineering
Hourieh Khalajzadeh, Mojtaba Shahin, Humphrey O. Obie, Pragya Agrawal, John Grundy
Summary: Failure to consider the characteristics, limitations, and abilities of diverse end-users during mobile app development may lead to human-centric issues for end-users. This paper examines the human-centric issues reported by end-users through app reviews and discussed by developers on GitHub. It also investigates the feasibility and usefulness of an automated tool for detecting and classifying human-centric issues. The findings highlight the importance of addressing these issues and suggest possible future work to improve mobile app development.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Kashumi Madampe, Rashina Hoda, John Grundy
Summary: This study investigates the impact of requirements changes on software practitioners and identifies key challenges, emotional impacts, influencing factors, and practices for better handling changes. It emphasizes the importance of synergy between agility, emotional intelligence, and cognitive intelligence in handling changes with positive emotions in socio-technical environments.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Chehara Pathmabandu, John Grundy, Mohan Baruwal Chhetri, Zubair Baig
Summary: Smart Buildings (SBs) use IoT technologies to automate operations and services, aiming to increase efficiency, maximize comfort, and minimize environmental impact. However, these cloud-based smart devices can capture and share sensitive data about occupants, exposing them to privacy threats. Users often lack awareness and fail to protect their privacy due to the convenience offered by IoT devices, resulting in a privacy paradox. To address this, a novel solution for informed consent management in shared smart spaces is proposed. This solution increases user awareness, provides visibility into privacy conformance, and enables informed decision-making. A reference architecture and proof-of-concept prototype are provided, and the proposed solution is validated through expert interviews.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Software Engineering
Ulrike M. Graetsch, Hourieh Khalajzadeh, Mojtaba Shahin, Rashina Hoda, John Grundy
Summary: This article presents a socio-technical grounded theory study conducted through interviews with 24 practitioners in multi-disciplinary data-intensive software teams (MDSTs). The study aims to understand the challenges faced by MDSTs when delivering data-intensive software solutions. The findings highlight the key concern of data-related challenges and provide a theory that explains the challenges, the context in which they occur, the causes, and the consequences. The study also identifies strategies and contingencies applied to address these challenges. The findings have implications for practitioners and researchers in understanding and dealing with data challenges.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Information Systems
Nasrin Sohrabi, Zahir Tari, Gauthier Voron, Vincent Gramoli, Qiang Fu
Summary: SAZyzz is a leader-based Byzantine Fault Tolerant consensus protocol for partially synchronous networks that improves performance and scalability compared to existing protocols. It adopts a tree-based communication model and reduces communication complexity.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Information Systems
Wei-Kang Chung, Yun Li, Chih-Heng Ke, Sun-Yuan Hsieh, Albert Y. Zomaya, Rajkumar Buyya
Summary: BCube, a well-known network structure for data center networks (DCNs), provides multiple low-diameter paths and good fault-tolerance. This paper proposes two centralized dynamic parallel flow scheduling algorithms, CDPFS and CDPFSMP, to decrease collisions and improve bandwidth utilization in BCube topology. The simulation results demonstrate that our algorithms leverage the advantages of BCube structure and achieve high-performance solutions for load balancing problems, improving throughput by 44.1% in random bijective traffic pattern and 36.2% in data shuffle compared with the BSR algorithm.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Information Systems
Avadh Kishor, Rajdeep Niyogi, Anthony Theodore Chronopoulos, Albert Y. Zomaya
Summary: This paper addresses the problem of latency and energy-aware load balancing in a cloud computing system. It formulates the problem as a cooperative game and proposes an efficient algorithm called LEWIS to compute the solution. Experimental results show that LEWIS not only reduces response time and energy consumption but also improves fairness to end-users.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Automation & Control Systems
Victoria Huang, Gang Chen, Xingquan Zuo, Albert Y. Zomaya, Nasrin Sohrabi, Zahir Tari, Qiang Fu
Summary: Software-defined networking (SDN) enables flexible and centralized control in cloud data centers. To provide sufficient and cost-effective processing capacity, an elastic set of distributed SDN controllers is often required. However, the challenge arises in dispatching requests among the controllers by SDN switches. This article proposes MADRina, a Multiagent Deep Reinforcement Learning approach, to design adaptable and high-performance dispatching policies.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jun Wang, Chang Tang, Zhiguo Wan, Wei Zhang, Kun Sun, Albert Y. Zomaya
Summary: Multiview clustering algorithms have achieved superior performance in various fields, but most of them are difficult to apply to large-scale datasets due to their cubic complexity. Moreover, they often rely on a two-stage scheme to obtain clustering labels, which results in suboptimal solutions. Therefore, an efficient and effective one-step multiview clustering method is proposed to directly obtain clustering indicators with a small-time burden. The method constructs smaller similarity graphs and generates low-dimensional latent features to form a unified partition representation, from which a binary indicator matrix can be directly obtained. The fusion of latent information and the clustering task in a joint framework improve the clustering result.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)