An Autonomous Host-based Intrusion Detection and Prevention System for Android Mobile Devices
Author: José C. V. G. Ribeiro
Publisher:
Published: 2019
Total Pages:
ISBN-13:
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Intrusion Detection and Prevention for Mobile Ecosystems
Author: Georgios Kambourakis
Publisher: CRC Press
Published: 2017-09-06
Total Pages: 477
ISBN-13: 131530581X
DOWNLOAD EBOOKThis book presents state-of-the-art contributions from both scientists and practitioners working in intrusion detection and prevention for mobile networks, services, and devices. It covers fundamental theory, techniques, applications, as well as practical experiences concerning intrusion detection and prevention for the mobile ecosystem. It also includes surveys, simulations, practical results and case studies.
Intrusion Detection and Prevention for Mobile Ecosystems
Author: Georgios Kambourakis
Publisher: CRC Press
Published: 2017-09-06
Total Pages: 559
ISBN-13: 1315305828
DOWNLOAD EBOOKThis book presents state-of-the-art contributions from both scientists and practitioners working in intrusion detection and prevention for mobile networks, services, and devices. It covers fundamental theory, techniques, applications, as well as practical experiences concerning intrusion detection and prevention for the mobile ecosystem. It also includes surveys, simulations, practical results and case studies.
Using Computational Intelligence for the Dark Web and Illicit Behavior Detection
Author: Rawat, Romil
Publisher: IGI Global
Published: 2022-05-06
Total Pages: 336
ISBN-13: 1668464454
DOWNLOAD EBOOKThe Dark Web is a known hub that hosts myriad illegal activities behind the veil of anonymity for its users. For years now, law enforcement has been struggling to track these illicit activities and put them to an end. However, the depth and anonymity of the Dark Web has made these efforts difficult, and as cyber criminals have more advanced technologies available to them, the struggle appears to only have the potential to worsen. Law enforcement and government organizations also have emerging technologies on their side, however. It is essential for these organizations to stay up to date on these emerging technologies, such as computational intelligence, in order to put a stop to the illicit activities and behaviors presented in the Dark Web. Using Computational Intelligence for the Dark Web and Illicit Behavior Detection presents the emerging technologies and applications of computational intelligence for the law enforcement of the Dark Web. It features analysis into cybercrime data, examples of the application of computational intelligence in the Dark Web, and provides future opportunities for growth in this field. Covering topics such as cyber threat detection, crime prediction, and keyword extraction, this premier reference source is an essential resource for government organizations, law enforcement agencies, non-profit organizations, politicians, computer scientists, researchers, students, and academicians.
Advances in Security, Networks, and Internet of Things
Author: Kevin Daimi
Publisher: Springer Nature
Published: 2021-07-10
Total Pages: 854
ISBN-13: 3030710173
DOWNLOAD EBOOKThe book presents the proceedings of four conferences: The 19th International Conference on Security & Management (SAM'20), The 19th International Conference on Wireless Networks (ICWN'20), The 21st International Conference on Internet Computing & Internet of Things (ICOMP'20), and The 18th International Conference on Embedded Systems, Cyber-physical Systems (ESCS'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Authors include academics, researchers, professionals, and students. Presents the proceedings of four conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20); Includes the tracks on security & management, wireless networks, internet computing and IoT, and embedded systems as well as cyber-physical systems; Features papers from SAM’20, ICWN’20, ICOMP’20 and ESCS’20.
Mobile Agent Based Intrusion Detection for Smart and Connected Medical Devices
Author: Adedayo Odesile
Publisher:
Published: 2017
Total Pages: 64
ISBN-13:
DOWNLOAD EBOOKThe advent of wearable and implantable devices have fostered recent advances in healthcare. Medical devices equipped with wireless connectivity to remote monitoring features are increasingly becoming connected to each other and the internet. Such smart and connected medical devices referred to as the Internet of Medical Things have enabled continuous real-time patient monitoring, increase in diagnostic accuracy, and effective treatment. Inspite of their numerous benefits, these devices open up newer attack surfaces thereby introducing multitude of security and privacy concerns. In this research, we design and develop a mobile agent based intrusion detection system to secure the network of connected medical devices. In particular, the proposed system is hierarchical, autonomous, and employs machine and regression algorithms to detect network level intrusions as well as anomalies in sensor data. Our simulation results reflect a relatively high detection accuracy with minimal resource overhead.
Mobile Internet Security
Author: Ilsun You
Publisher: Springer Nature
Published: 2022-01-22
Total Pages: 428
ISBN-13: 9811695768
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 5th International Symposium on Mobile Internet Security, MobiSec 2021, held in Jeju Island, Republic of Korea, in October 2021. The 28 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in the topical sections: IoT and cyber security; blockchain security; digital forensic and malware analysis; 5G virtual Infrastructure, cryptography and network security.
Mobile Hybrid Intrusion Detection
Author: Álvaro Herrero
Publisher: Springer Science & Business Media
Published: 2011-01-19
Total Pages: 151
ISBN-13: 3642182984
DOWNLOAD EBOOKThis monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.
Autonomous Agents for Distributed Intrusion Detection in a Multi-Host Environment
Author: Dennis J. Ingram
Publisher:
Published: 1999-09-01
Total Pages: 81
ISBN-13: 9781423542421
DOWNLOAD EBOOKBecause computer security in today's networks is one of the fastest expanding areas of the computer industry, protecting resources from intruders is an arduous task that must be automated to be efficient and responsive. Most intrusion-detection systems currently rely on some type of centralized processing to analyze the data necessary to detect an intruder in real time. A centralized approach can be vulnerable to attack. If an intruder can disable the central detection system, then most, if not all, protection is subverted. The research presented here demonstrates that independent detection agents can be run in a distributed fashion, each operating mostly independent of the others, yet cooperating and communicating to provide a truly distributed detection mechanism without a single point of failure. The agents can run along with user and system software without noticeable consumption of system resources, and without generating an overwhelming amount of network traffic during an attack.
A Cloud-based Intrusion Detection and Prevention System for Mobile Voting in South Africa
Author: Moloiyatsana Dina Moloja
Publisher:
Published: 2018
Total Pages: 260
ISBN-13:
DOWNLOAD EBOOKInformation and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational resources of mobile phones, more work needs to be done to alleviate the false-negative alerts caused by the anomaly detection method. This study adopted Suricata as a suitable cloud-based analysis engine to protect a mobile voting application like XaP.
