A SURVEY OF IMPLEMENTATION OF OPPORTUNISTIC SPECTRUM ACCESS ATTACK WITH ITS PREVENTIVE SENSING PROTOCOLS IN COGNITIVE RADIO NETWORKS

Recently, the expansive growth of wireless services, regulated by governmental agencies assigning spectrum to licensed users, has led to a shortage of radio spectrum. Since the FCC (Federal Communications Commissions) approved unlicensed users to access the unused channels of the reserved spectrum, new research areas seeped in, to develop Cognitive Radio Networks (CRN), in order to improve spectrum efficiency and to exploit this feature by enabling secondary users to gain from the spectrum in an opportunistic manner via optimally distributed traffic demands over the spectrum, so as to reduce the risk for monetary loss, from the unused channels. However, Cognitive Radio Networks become vulnerable to various classes of threats that decrease the bandwidth and spectrum usage efficiency. Hence, this survey deals with defining and demonstrating framework of one such attack called the Primary User Emulation Attack and suggests preventive Sensing Protocols to counteract the same. It presents a scenario of the attack and its prevention using Network Simulator-2 for the attack performances and gives an outlook on the various techniques defined to curb the anomaly

Intrusion Detection in Mobile Ad-Hoc Networks using Bayesian Game Methodology

The dynamic and distributed nature of MANETs make them vulnerable to various types of attacks like black hole attack, traffic distortion, IP spoofing, DoS attack etc. Malicious nodes can launch attacks against other normal nodes and deteriorate the overall performance of the entire network [1�3]. Unlike in wired networks, there are no fixed checkpoints like router and switches in MANETs, where the Intrusion Detection System (IDS) can be deployed .However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node�s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency. Simulated results shows improvement in estimated delay and average bits transfer parameter

Intrusion Detection in Mobile Adhoc Network with Bayesian model based MAC Identification

Mobile Ad-hoc Networks (MANETs) are a collection of heterogeneous, infrastructure less, self-organizing and battery powered mobile nodes with different resources availability and computational capabilities. The dynamic and distributed nature of MANETs makes them suitable for deployment in extreme and volatile environmental conditions. They have found applications in diverse domains such as military operations, environmental monitoring, rescue operations etc. Each node in a MANET is equipped with a wireless transmitter and receiver, which enables it to communicate with other nodes within its wireless transmission range. However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node?s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency

Computational intelligence-enabled cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies

The effective combating of intrusion attacks through fuzzy logic and neural networks

The importance of properly securing an organization’s information and computing resources has become paramount in modern business. Since the advent of the Internet, securing this organizational information has become increasingly difficult. Organizations deploy many security mechanisms in the protection of their data, intrusion detection systems in particular have an increasingly valuable role to play, and as networks grow, administrators need better ways to monitor their systems. Currently, many intrusion detection systems lack the means to accurately monitor and report on wireless segments within the corporate network. This dissertation proposes an extension to the NeGPAIM model, known as NeGPAIM-W, which allows for the accurate detection of attacks originating on wireless network segments. The NeGPAIM-W model is able to detect both wired and wireless based attacks, and with the extensions to the original model mentioned previously, also provide for correlation of intrusion attacks sourced on both wired and wireless network segments. This provides for a holistic detection strategy for an organization. This has been accomplished with the use of Fuzzy logic and neural networks utilized in the detection of attacks. The model works on the assumption that each user has, and leaves, a unique footprint on a computer system. Thus, all intrusive behaviour on the system and networks which support it, can be traced back to the user account which was used to perform the intrusive behavior

A systematic literature review on Security of Unmanned Aerial Vehicle Systems

Unmanned aerial vehicles (UAVs) are becoming more common, and their operational range is expanding tremendously, making the security aspect of the inquiry essential. This study does a thorough assessment of the literature to determine the most common cyberattacks and the effects they have on UAV assaults on civilian targets. The STRIDE assault paradigm, the challenge they present, and the proper tools for the attack are used to categorize the cyber dangers discussed in this paper. Spoofing and denial of service assaults are the most prevalent types of UAV cyberattacks and have the best results. No attack style demands the employment of a hard-to-reach gadget, indicating that the security environment currently necessitates improvements to UAV use in civilian applications.Comment: 10 Pages, 4 Figure