Antenna directivity versus antenna gain.
\r\n\tThis welcomes submissions regarding the basic physics of ferromagnetism, such as the origin of magnetism and concept of ferromagnetism, magnetic anisotropy, exchange interactions, exchange bias, magnetic domain and domain-wall motion, hysteresis loops, temperature dependent magnetization as well as the recent progress in advanced ferromagnetism. Special emphasis will be given to the systems at the nanoscale and superparamagnetism.
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He has done post-doctorates in China and South Korea (Institute of Modern Physics, CAS, China and Sungkyunkwan University, South Korea). His research interests include the fabrication of nanomaterials and their structural, magnetic, optical, and electrical characterizations. He has authored more than 90 articles and served as the editor of nine books. Presently, he is serving as the Editor-In-Chief of 'Journal of Materials, Processing and Design” (USA) and the Executive Editor of 'International Journal of Nano Studies & Technology” (Canada), and also as the Editorial Board Member of few more journals of materials science.",institutionString:"Pakistan Institute of Nuclear Science and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"8",institution:{name:"Pakistan Institute of Nuclear Science and Technology",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1169",title:"Condensed Matter Physics",slug:"nanotechnology-and-nanomaterials-material-science-condensed-matter-physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"270935",firstName:"Rozmari",lastName:"Marijan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/270935/images/7974_n.png",email:"rozmari@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Low-profile compact antennas are crucial in the development of wireless communication and wearable biomedical systems. Compact, low-profile, and light-weight printed antennas are the best solution for communication, IoT, and medical systems. Printed antennas’ low production costs are crucial in development of low-cost communication systems. Moreover, the advantage of an integrated compact low-cost feed network is attained by integrating the antenna feed network with the antennas on the same printed board. Wireless communication and medical industry are in continuous growth in the last few years. Printed antennas are used in communication systems that employ MIC, MEMS, LTCC, and MMIC technologies.
Antennas are major components in communication systems [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. Mobile antenna systems are presented in [11]. Transmitting antennas efficiently radiate electromagnetic fields and match RF systems to space. Antennas may transmit or receive electromagnetic fields. Transmitting antennas convert electric current to electromagnetic fields. Receiving antennas convert electromagnetic fields to electric current. In transmitting antennas, an alternating current is created in the elements by applying a voltage at the antenna feed network, causing the antenna to radiate an electromagnetic energy. In receiving mode, an electromagnetic field from an outer source induces an alternating current in the receiving antenna and generates a voltage at the antenna’s feed network. Antennas are used in outdoor and indoor communication systems. Antennas can be used in under water communication systems. Antennas may be implanted inside human body and operate as wearable sensors. Receiving antennas such as parabolic and horn antennas incorporate shaped reflective surfaces to receive the electromagnetic fields and focus the fields to the conductive receiving elements.
The book consists of four sections presenting several types of novel antennas.
Section 1: Introduction
Section 2: Novel Antennas for 5G, IoT, and Medical Applications
Section 3: Novel RF Antennas Technologies
Section 4: Advanced Antenna Arrays
The design and electrical performance of several novel antennas are presented in this book.
Small antennas for communication systems.
Monopole—quarter wavelength wire antenna.
Dipole—Dipole antenna consists of two quarter wavelength wires. Dipole is a half wavelength wire antenna. The monopole and dipole antennas couple to the electric field of the electromagnetic wave in the region near the antenna.
Slot antenna—half wavelength slot: A slot antenna consists of a metal surface with a slot cut out.
Biconical antennas—biconical half wavelength wire antenna: Biconical antenna is a wideband antenna made of two conical conductive objects.
Loop antennas—Loop antenna is known as a magnetic loop. The loop antenna behaves as an inductor. The loop antenna couples the electromagnetic magnetic field in the region near the loop antenna. Monopole and dipole antennas couple to the electric field.
Helical antennas—helical wire antennas.
Printed antennas—antennas printed on a dielectric substrate.
Aperture antennas for base station communication systems
Horn and open waveguide
Reflector antennas
Antenna arrays
Microstrip and printed antenna arrays
Slot antenna arrays
A comparison of directivity and gain values for several antennas is given in Table 1.
Antenna type | Directivity (dBi) | Gain (dBi) |
---|---|---|
Isotropic radiator | 0 | 0 |
Dipole λ/2 | 2 | 2 |
Dipole above ground plane | 6–4 | 6–4 |
Microstrip antenna | 7–8 | 6–7 |
Yagi antenna | 6–18 | 5–16 |
Helix antenna | 7–20 | 6–18 |
Horn antenna | 10–30 | 9–29 |
Reflector antenna | 15–60 | 14–58 |
Antenna directivity versus antenna gain.
Phased arrays: Phased array antennas are electrically steerable. The physical antenna can be stationary. Phased arrays, smart antennas, incorporate active components for beam steering.
Arrays with switch-able elements. The array may be partially electronically and mechanically steerable arrays.
Hybrid antenna systems—for fully electronically steerable arrays. The design can be based on digital beam forming (DBF). These systems may consist of phase and amplitude shifters for each radiating element.
In digital beam forming, the steering is performed directly in a digital level. DBF allows the most powerful and flexible control of the antenna beam steering.
Monopole antenna is usually a one-quarter wavelength-long conductor mounted above a ground plane or the earth as shown in Figure 1. Based on the image theory behind the ground plane, the monopole image is located. The monopole antenna and the monopole image form a dipole antenna. Monopole antenna is half a dipole that radiates electromagnetic fields above the ground plane. The impedance of 0.5 λ monopole antenna is around 37 Ω. The beam width of a monopole, 0.25 λ long, is around 40°. The directivity of a monopole, 0.25 λ long, is around 3 dBi to 5dBi. Usually in wireless communication systems, very short monopole antenna is employed. The impedance of 0.05 λ monopole antenna is around 1 Ω with capacitive reactance. The beam width of a monopole, 0.05 λ long, is around 45°. The directivity of a monopole, 0.05 λ long, is around 3 dBi.
Monopole antenna.
Inverted monopole antenna is shown in Figure 2a. Loaded monopole antenna is shown in Figure 2b. Monopole antennas may be printed on a dielectric substrate as part of wearable communication devices.
(a) Inverted monopole antenna. (b) Loaded monopole antenna.
Monopole and dipole antennas are the most basic antennas. Evaluation of the electrical parameters of dipole antenna is used to define basic antenna parameters such as radiation pattern, directivity, and antenna impedance. Dipole antenna is a small wire antenna. It consists of two conductors excited by a voltage fed via a transmission line as presented in Figure 3. The center conductor of the transmission line is connected to one of the conductors and the outer conductor is connected to the second conductor. The half-wave dipole consists of two conductors, in which each conductor is approximately quarter wavelength long. We can compute the electromagnetic fields radiated from the dipole by defining a potential function, Eq. (1).
Dipole antenna.
The electric potential function is
The length of a small dipole is small compared to its wavelength and is called elementary dipole. The current along a small dipole is uniform. We can compute the electromagnetic fields radiated from the dipole in spherical coordinates by using the potential function given in Eq. (1). The electromagnetic field at a point P(r, θ, φ) is listed in Eq. (3). The electromagnetic fields in Eq. (3) vary as
The antenna radiation pattern represents the radiated fields in space at a point P(r, θ, φ) as a function of θ and φ. The antenna radiation pattern is three dimensional. When φ is constant and θ varies, we get the E plane radiation pattern. When φ varies and θ is constant, usually θ = π/2, we get the H plane radiation pattern.
The dipole E plane radiation pattern is given in Eq. (2) and presented in Figure 4.
Dipole E plane radiation pattern.
At a given point P(r, θ, φ), the dipole E plane radiation pattern is given in Eq. (7).
Dipole E plane radiation pattern in spherical coordinate system is shown in Figure 5.
Dipole E plane radiation pattern in spherical coordinate system.
For θ = π/2, the dipole H plane radiation pattern is given in Eq. (8) and presented in Figure 6.
Dipole H plane radiation pattern for θ = π/2.
The dipole H plane radiation pattern in xy plane is a circle with r = 1. At a given point P(r, θ, φ), the dipole H plane radiation pattern is given in Eq. (9). The radiation pattern of a vertical dipole is omnidirectional. It radiates equal power in all azimuthal directions perpendicular to the axis of the antenna.
An antenna radiation pattern is shown in Figure 7. The antenna main beam is defined between the points that the maximum relative field level E decays to 0.707E. Half of the radiated power, −3 dB points, is concentrated in the antenna main beam. The antenna main beam is defined as the 3 dB beam width. Radiation to undesired direction is concentrated. The antenna side lobes present radiation to undesired direction.
Antenna typical radiation pattern.
For a dipole, the power intensity varies as
Directivity is defined as the ratio between the amounts of energy propagating in a certain direction compared to the average energy radiated to all directions over a sphere as written in Eqs. (10) and (11).
The radiated power from a dipole is calculated by computing the pointing vector P as given in Eq. (12).
The overall radiated energy is WT. WT is computed as written in Eq. (12), by integration of P over an imaginary sphere surrounding the dipole. The power flow of an isotropic element equal to the overall radiated energy divided by the area of the sphere,
Antenna losses degrade the antenna efficiency. Antenna losses consist of conductor loss, dielectric loss, radiation loss, and mismatch losses. For resonant small antennas,
Antenna impedance determines the efficiency of transmitting and receiving energy in antennas. The dipole impedance is given in Eq. (16).
Loop antennas are compact, low-profile, and low-cost antennas. Loop antennas are employed in wearable wireless communication systems. The loop antenna is referred to as the dual of the dipole antenna, see Figure 8. A small dipole has magnetic current flowing (as opposed to electric current as in a regular dipole), the loop antenna fields are similar to that of a small loop. The short dipole has a capacitive impedance and the impedance of a small loop is inductive. A photo of loop antennas is shown in Figure 9.
Duality relationship between dipole and loop antennas.
Photo of loop antennas.
Printed antennas have been analyzed and presented in several papers and books in the last 30 years [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. Microstrip antennas are the most popular type of compact printed antennas. Printed Slot, PIFA, and dipole antennas are employed in several wireless communication systems. Printed antennas are used in wireless communication systems, wearable devices, IoT applications, novel 5G communication links, seekers, and medical systems.
Printed antennas possess attractive features such as light weight, compactness, flexibility, and are cheap compared to other similar antennas.
Medical
Wireless communication
WLAN
GPS
Military applications
Microstrip antennas are printed on a on a dielectric substrate with low dielectric losses. Cross section of the microstriop antenna is shown in Figure 10.
Microstrip antenna cross section.
Microstrip antennas are thin patches etched on a dielectric substrate εr. The substrate thickness, H, is less than 0.1λ. Microstrip antennas are widely presented in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].
Advantages of microstrip antennas:
Flexible
Low-profile compact uniform arrays
Compact
Low-cost antennas
Disadvantages of microstrip antennas:
Narrow bandwidth (usually 0.5–5%). However, wider bandwidth may be achieved by using multilayer structure and novel technologies.
Limited power handling up to 50 W.
High feed network losses at high frequencies.
The electric field along the radiating edges is presented in Figure 11. The magnetic field is perpendicular to the electric field according to Maxwell’s equations. At the edge of the strip (X/L = 0 and X/L = 1), the magnetic field drops to zero, because there is no conductor to carry the electromagnetic current, it is maximum in the patch center. The electric field is at maximum level (with opposite polarity) at the patch edges (X/L = 0 and X/L = 1) and zero at the patch center. The ratio of electric to the magnetic field is proportional to the impedance that we measure when we feed the patch. Microstrip antennas may be fed by a microstrip line or by a coaxial line or probe feed. By varying the location of the antenna feed point between the patch center and the patch edge, we can get a 50 Ω impedance or any other desired impedance. Microstrip antenna may have any arbitrary shape such as square, triangle, circle, ring, rectangular, and fractal shape as presented in Figure 12.
Rectangular microstrip antenna.
Microstrip antenna shapes.
The antenna dimension W is given by Eq. (17). The antenna bandwidth is given in Eq. (18).
The gain of microstrip patch is between 0 and 5 dBi. The microstrip patch gain is a function of the antenna configuration, structure, and dimensions. Microstrip antenna arrays are used to get gain up to 30 dB. In microstrip patch arrays, a low-profile and low-cost feed and matching network is attained by integrating the antenna feed network with the radiating patches on the same substrate. Microstrip antenna feed networks are shown in Figure 13. A parallel feed network is shown in Figure 13a. A parallel–series feed network is shown in Figure 13b.
MM wave microstrip antenna array. (a) Parallel array feed network. (b) Parallel-series array feed network.
This chapter presents electrical parameters of several basic antennas. Antennas are part of radio and television broadcasting, point-to-point radio communication systems, wireless LAN, cell phones, radar, medical systems, and spacecraft communication. Compact wideband efficient antennas are crucial in communication systems.
Today, the world has numerous inventions and technological developments with proliferation of the Internet. Advances in business forced the organizations and governments worldwide to invent and use sophisticated and modern networks. These networks mix a variety of security aspects such as encryption, data integrity, authentication, and technologies like distributed storage systems, voice over Internet protocol (VoIP), wireless access, and web services.
Enterprises are more available to these systems. For instance, numerous business associations enable access to their administration on the system through intranet and web to their partners; endeavors empower clients to connect with the systems by means of web-based business exchanges that enable representatives to get to data by methods for virtual private systems. This usage makes it more vulnerable to attacks and intrusions. A security threat comes not only from the external intruders but also from internal user in the form of abuse and misuse. A firewall simply blocks the network but cannot protect against intrusion attempts. In contrast, intrusion detection system (IDS) can monitor the abnormal activities on the network.
Intrusion detection systems play a vital role in research and development with an increase in attacks on computers and networks [1]. Intrusion detection systems monitor the events occurring in a computer system or networks for analyzing the patterns of intrusions. IDS examine a host or network to spot the potential intrusions. Host-based systems explore the system calls and process identifiers mainly related to the operating system data. On the other hand, network-based systems analyze network-related events like traffic volume, IP address, service ports, and protocol used. Intrusion detection systems will
analyze and monitor the system and user activities;
assess the integrity of critical system and data files; and
provide statistical analysis of activity patterns.
The intrusion detection systems are broadly classified as
misuse detection systems and
anomaly-based detection systems.
A misuse detection system is also called as signature-based detection that uses recognized patterns [2]. These patterns describe suspect, collection of sequences of activities or operations that can be possibly be harmful and stored in database. It uses well-defined patterns of the attack that exploits the weaknesses in system. The time taken to match with the patterns stored in the database is minimal. A key benefit of these systems is that the patterns or signatures can easily develop and understand the network behavior if familiar. It is more efficient to handle the attacks whose patterns are already maintained in the database.
The major restriction of these signature-based approaches is that they can only detect the intrusions whose attack patterns are already stored in the database. For every attack, its signature is to be created. Attacks whose patterns are not present in the database cannot be detected. Such technique can be easily deceived as they are dependent on a specific set of expressions and string matching. In addition, the signature works well only against fixed behavioral patterns; they fail to handle the attacks with human interference or attacks with inherent self-modifying behavioral characteristics.
These detection systems are also ineffective in cases where client works on new technology platforms such as no operation (NoP) generators, encoding, and decoding payloads. The efficiency of the signature-based systems decreases due to the need of creating dynamic signatures for different variations. With growing volume of signatures, the performance of the engine also might lose the momentum. Because of this, intrusion detection frameworks are conducted on multiprocessors and Gigabit cards. IDS developers develop new signatures before the attackers develop solutions, in order to prevent any new kind of attacks on the system.
Network behavior is the major parameter on which the anomaly detection systems rely upon. If the network behavior is within the predefined behavior, then the network transaction is accepted or else it triggers the alert in the anomaly detection system [3]. Acceptable network performance can be either predetermined or learned through specifications or conditions defined by the network administrator.
The crucial stage of behavior determination is regarding the ability of detection system engine toward multiple protocols at each level. The IDS engine must be able to understand the process of protocols and its goal. Despite the fact that the protocol analysis is very expensive in terms of computation, the benefits like increasing rule set assist in lesser levels of false-positive alarms.
Defining the rule sets is one of the key drawbacks of anomaly-based detection. The efficiency of the system depends on the effective implementation and testing of rule sets on all the protocols. In addition, a variety of protocols that are used by different vendors impact the rule defining the process.
In addition to the aforesaid, custom protocols also add complexity to the process of rule defining. For accurate detection, the administration should clearly understand the acceptable network behavior. However, with strong incorporation of rules and protocol, the anomaly detection procedure would likely to perform more efficiently.
However, if the malicious behavior falls under the accepted behavior, in such conditions it might get unnoticed. The major benefit of the anomaly-based detection system is about the scope for detection of novel attacks. This type of intrusion detection approach could also be feasible, even if the lack of signature patterns matches and also works in the condition that is beyond regular patterns of traffic.
In Figure 1, common intrusion detection framework (CIDF) integrated with Internet Engineering Tasks Force (IETF) and Intrusion Detection Working Group (IDWG) has successfully achieved efficient performance in representing the framework. This group defines a basic IDS structural design based on four functional modules.
Common intrusion detection framework architecture.
Event modules (E-Modules) are defined as a combination of sensing elements and are engaged in continuous monitoring of the end system. In addition, these modules are also involved in processing the information events to the bottom three modules for further analysis.
Analysis modules (A-Modules) analyze the events and detect probable aggressive behavior, in order to ensure that some kind of alarm generated in essential conditions.
Data storage modules (D-modules) store the data from the E-Modules for further processing by the other modules.
Response modules (R-Modules) are used to provide the response to the transactions based on the information obtained from the analysis module.
Figure 2 represent the Common anomaly-based network IDS. The functional stages normally adopted in the anomaly-based network intrusion detection systems (ANIDS) are as follows:
Common anomaly-based network IDS.
Formation of attributes: In this stage, preprocessing of the attributes is done based on the target system.
Observation stage: A model that is built on the basis of behavioral features of the specified system where observations of intrusions can be carried out either through automatically or by manual detection procedure.
Functional stage: It is also called as detection stage. If the characterizing system model is available, it will match with the observed traffic.
Figure 3 represents the taxonomy of anomaly-based intrusion detection techniques. They are statistical based, cognitive based or knowledge based, machine learning or soft computing based, data mining based, user intention identification, and computer immunology.
Classification of anomaly-based intrusion detection techniques.
Statistical-based techniques use statistical properties such as mean and variance on normal transaction to build the normal profile [4]. The statistical tests are employed to determine whether the observed transaction deviates from the normal profile. The IDS assigns a score to the transactions whose profile deviates from the normal. If the score reaches the threshold, alarm is raised. The threshold value is set based on count of events that occur over a period of time.
Statistical-based techniques are further classified into operational model or threshold metric, time series model, Markov process model or Marker model, parametric approaches, statistical moments or mean and standard deviation model, multivariate model, and nonparametric approaches.
The main advantages of statistical-based techniques are as follows:
They do not require any prior knowledge about the signatures of the attacks. So, they can detect zero-day attacks.
As the system is not depended on any of the signatures, updating is not required. Hence it is easy to maintain.
The intrusion activities that were occurred over extended period of time can be identified accurately and are good at detecting DoS attacks.
The disadvantages of statistical-based techniques are as follows:
They need accurate statistical distributions.
The learning process of statistical-based techniques takes days or weeks to become accurate and effective.
Knowledge-based techniques are used to extract the knowledge from the specific attacks and system vulnerabilities. This knowledge can be further used to identify the intrusions or attacks happening in the network or system. They generate alarm as soon as an attack is detected. They can be used for both misuse and anomaly-based detection [5].
The knowledge-based techniques are broadly classified as state transition analysis, expert systems, and signature analysis.
The knowledge-based techniques possess good accuracy and very low false alarm rates. The knowledge gathered makes security analyst easier to take preventive or corrective action.
The knowledge-based techniques are maintaining the knowledge of each attack based on the careful and detailed analysis performed; it is a time-consuming task. A prior knowledge to update the each attack is a difficult task.
The knowledge-based IDS can detect the attacks whose patterns are known, but it is difficult to detect the inside attacks. One of the solutions is data mining techniques. The core idea is to extract the useful patterns and also the previously ignored patterns from the dataset [6].
The data mining-based techniques are further classified into clustering, association rule discovery, classification, K-nearest neighbor, and decision tree methods.
The key advantages of data mining-based techniques are as follows:
They can handle high dimensional data.
As the precomputed models are designed in the training phase, comparing each instance at the testing phase can be done in faster way.
They can generate the patterns in unsupervised mode.
The key disadvantages of data mining-based techniques are as follows:
These methods identify abnormalities as a by-product of clustering and as are not optimized for anomaly detection.
They require high storage and are slow in classifying due to high dimensionality.
Machine learning can be characterized as the capacity of a program or potentially a framework to learn and improve their performance on a specific task or group of tasks over a time [7]. Machine learning strategies emphasize on building a framework that enhances its execution based on previous results, that is, it can change their execution strategy based on recently acquired data.
Machine learning-based techniques are broadly classified as Bayesian approaches, support vector machines, neural networks, fuzzy logic, and genetic algorithms. Their key advantage is flexibility, adaptability, and capture of interdependencies. The disadvantage is high algorithmic complexity and long training times.
Intrusion detection system can be built based on the features that categorize the user or the system usage, to distinguish the abnormal activities from normal activities. During the early investigation of anomaly detection, the main emphasis was on profiling system or user behavior from monitored system log or accounting log data. The log data or system log may contain UNIX shell commands, system calls, key strokes, audit events, and network packages used.
Computer immunology is a field of science that includes high-throughput genomic and bioinformatics approaches to immunology. The main objective is to convert immunological data into computational problems, solve these problems using statistical and computational approaches, and then convert the results into immunologically meaningful interpretations.
The NSL-KDD [8] dataset is a refined version of its predecessor KDD99 dataset. NSL-KDD dataset comprises close to 4,900,000 unique connection vectors, where every connection vector consists of 41 features of which 34 are continuous features and 07 are discrete features. Each vector is labeled as either normal or attack. There are four major categories of attacks labeled in NSL-KDD: denial of service attack, probing attack, users-to-root attack, and remote-to-local attack.
Denial of service attack (DoS): Denial of service is an attack category, which exhausts the victim’s assets, thereby making it unable to handle legitimate requests. Examples of DoS attacks are “teardrop,” “neptune,” “ping of death (pod),” “mail bomb,” “back,” “smurf,” and “land.”
Probing attack (PROBE): Objective of surveillance and other probing attacks is to gain information about the remote victim. Examples of probing attacks are “nmap,” “satan,” “ipsweep,” and “portsweep.”
Users-to-root attack (U2R): The attacker enters into the local system by using the authorized credentials of the victim user and tries to exploit the vulnerabilities to gain the administrator privileges. Examples of U2R attacks are “load module,” “buffer overflow,” “rootkit,” and “perl.”
Remote-to-local attack (R2L): The attackers access the targeted system or network from the remote machine and try to gain the local access of the victim machine. Examples of R2L attacks are “phf,” “warezmaster,” “warezclient,” “spy,” “imap,” “ftp write,” “multihop,” and “guess passwd.”
Although many methods and systems have been developed by the research community, there are still a number of open research issues and challenges. Some of the research issues and challenges of AIDS are as follows:
A network anomaly-based IDS should reduce the false alarm rate. But, totally mitigating the false alarm is not possible. Developing an intrusion detection system independent of the environment is another challenge task for the network anomaly-based intrusion detection system development community [9, 10, 11, 12, 13].
Developing a general methodology or a set of parameters that can be used to evaluate the intrusion detection system is another challenging task [12, 13].
When new patterns are identified in ANIDS, updating the database without compromise of performance is another challenging task [9, 13].
Another task to be addressed is to reduce the computational complexities of data preprocessing in the training phase and also in the deployment phase [9, 10].
Developing a suitable method for selecting the attributes for each category of attack is another important task [9, 10, 11].
Identifying a best classifier from a group of classifiers that is nonassociated and unbiased to build an effective ensemble approach for anomaly detection is another challenge [9, 10, 11].
The preprocessed set of network transactions are partitioned based on its labeling (“normal” transactions as one set, “DoS” transactions as the other set and similar other range of sets). Unique values of each feature value set
The procedure for feature optimization for each attack
Consider the transactions set
For every feature
The process is used to generate the feature values vector
The process is applied for all feature values set in network transactions of attack
Find the canonical correlation between
It is imperative from the implementation of the above procedure that optimal features of a specific attack
The approach for measuring the proposed feature association support
Let
Categorical values of the set of features related to every network transaction shall be considered as transaction value set
In the description above in Eq. 2,
Step 1: The edge weight between the features
Step 2: The edge weight between transaction value sets and its corresponding set of feature categorical values can be measured as:
Step 3: Further assuming the transaction value sets of the given duplex graph as pivots and the feature categorical values as pure prerogatives, the pivot and prerogative values are measured.
Step 3.1: Consider matrix u, which denotes pivot initial value as 1.
Step 3.2: Transpose the matrix A as A′.
Step 3.3: Calculate prerogative weights by multiplying A′ with u.
Step 3.4: Calculate original pivot weights using matrix multiplication between A and V.
Step 4: Calculate the feature categorical value
Step 5: the Feature Association Impact Scale
Step 6: The Feature Association Impact Scale threshold
Step 7: Calculate the standard deviation as:
Step 8: The Feature Association Impact Scale range can be explored as Step 8.1 and Step 8.2:
Step 8.1: Calculate lower threshold of faist as
Step 8.2: Calculate higher threshold of faist as
The total number of records chosen for the test is 25% of the actual dataset, that is, 34,361. The combination of test records chosen is from various categories such as Probe, DoS, U2R, R2L, and Normal. The difference between CC average and standard deviation of CC is called as lower bound of CC threshold. The sum of CC average and standard deviation of CC is called as upper bound of CC threshold.
The records that identified to be normal are 19.8% of the total test data records, with observations of 4.7% of it as “false negatives” and 15.1% of it as “true negatives.” The cumulative number of records that are detected as “intruded transactions” is 80.2%, with 75.3% of them being “truly intruded transactions” of test data records and the “false positive” percentage of 4.9% of test data records.
FCAAIS | FAIS | ||
---|---|---|---|
Total number of records tested | 34,361 | 34,361 | |
TP (true positive) | The number of transactions identified as normal, which are actually normal | 29,379 | 27,889 |
FP (false positive) | The number of transactions identified as normal, which are actually intruded | 1968 | 2752 |
TN (true negative) | The number of transactions identified as intruded, which are actually intruded | 1901 | 2375 |
FN (false negative) | The number of transactions identified as intruded, which are actually normal | 1113 | 1345 |
Precision | TP/(TP + FP) | 0.937218873 | 0.910185699 |
Recall/sensitivity | TP/(TP + FN) | 0.963498623 | 0.953991927 |
Specificity | TN/(FP + TN) | 0.491341432 | 0.46323386 |
Accuracy | (TP + TN)/(TP + TN + FP + FN) | 0.910334391 | 0.880765985 |
F-measure | 2 × (PRECISION × RECALL)/(PRECISION + RECALL) | 0.951646837 | 0.91131588 |
Comparison of performance metrics of FCAAIS and FAIS.
As per the results obtained, the proposed model is found to be accurate up to 90.4%. The experiments are conducted on the same dataset using “anomaly-based network intrusion detection through assessing Feature Association Impact Scale (FAIS)” [14]. The results depict that the proposed model is also scalable and effective for detecting the scope of intrusion from a network transaction. Despite the fact that the FAIS model proposed shows 88% accuracy, the major limitation is process complexity in training the system. Such process complexities of designing the scale using FAIS are due to the number of features selected for assessing the scale. The issue of selecting the optimal features for training the Intrusion Detection System using Association Impact Scale is significantly addressed in the FCAAIS [15] model.
Table 1 indicates the comparison of performance metrics such as precision, recall/sensitivity, specificity, accuracy, and F-measure of FCAAIS over FAIS. Figure 4 indicates that the accuracy of FCAAIS with optimal features is 91%, whereas the FAIS accuracy with all features is 88%. The precision of the FCAAIS model with optimal features and FAIS with all features is 92%. The other performance metrics such as sensitivity, specificity, and F-measure is calculated on FCAAIS over FAIS. The sensitivity, specificity, and F-measure are 96, 49, and 95%, respectively, for FCAAIS, whereas sensitivity, specificity, and F-measure are 95, 46, and 91%, respectively, for FAIS.
The performance metrics observed for FCAAIS over FAIS.
According to the results, the accuracy of FCAAIS (selected feature set using canonical correlation) minimized the process complexity of designing the scale using FAIS (Figure 5 and Table 2).
The process computational time observed for FCAAIS over FAIS.
Number of transactions | FCAAIS (s) | FAIS (s) |
---|---|---|
500 | 0.397 | 0.527 |
1000 | 0.611 | 0.714 |
2000 | 0.723 | 0.882 |
4000 | 1.012 | 1.139 |
8000 | 1.275 | 1.439 |
16,000 | 1.578 | 1.703 |
25,000 | 1.891 | 2.031 |
Process computational time of FCAAIS and FAIS.
The observed time complexity is adaptable, as the completion time is not directly related to the ratio of features count, which is due to the higher CC threshold as shown in Figure 6. Hence it is obvious to conclude that the applying canonical correlation toward optimized attribute selection is significant improvement to the FAIS model (shown in Figure 6).
The FCAAIS consumption of time under divergent canonical correlation thresholds.
It is observed that applying canonical correlation toward optimized attribute selection results in 3% improvement in the accuracy of FAIS [14]. Table 3 indicates precision, recall, and F-measure values calculated under divergent canonical correlation threshold values (Figure 7).
Precision | F-measure | Recall | |
---|---|---|---|
Less than the upper bound of CC threshold | 0.989 | 0.987998988 | 0.987 |
Less than the lower bound of CC threshold | 0.98 | 0.984974619 | 0.99 |
Less than the CC threshold | 0.985 | 0.985 | 0.985 |
Precision, recall, and F-measure values calculated under divergent canonical correlation threshold.
Performance analysis of the prediction accuracy of FCAAIS under divergent canonical correlation threshold value.
It is desirable for anomaly-based network intrusion detection system to achieve high classification accuracy and reduce the process complexity of extracting the rules from training data. In this chapter, a canonical correlation analysis is proposed to optimize the features toward designing the scale to detect the intrusions. The selection of optimal features simplifies the process of FAIS. The experiments were conducted using a benchmark NSL-KDD dataset. The results indicate that the accuracy of FCAAIS with optimal features is 91%, whereas the FAIS accuracy with all features is 88%. The precision of the FCAAIS model with optimal features and FAIS with all features is almost close to 92%. It is observed that applying canonical correlation toward optimized attribute selection has 3% improvement in the accuracy of FAIS. The other performance metrics such as sensitivity, specificity, and F-measure is calculated on FCAAIS over FAIS. The sensitivity, specificity, and F-measure are 96, 49, and 95%, respectively, for FCAAIS, whereas they are 95, 46, and 91%, respectively, for FAIS.
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