Comparison of performance metrics of FCAAIS and FAIS.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"985",leadTitle:null,fullTitle:"Prostate Biopsy",title:"Prostate Biopsy",subtitle:null,reviewType:"peer-reviewed",abstract:"Prostate Biopsy represents the standard procedure for diagnosing Prostate Cancer. 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\r\n\tToday, in the fields of psychology, medicine, biology, sociology, and anthropology, there is a great deal of research on the factors that are related to happiness and the effects of happiness on people's mental and physical health.
\r\n\r\n\tThis book aims to provide a comprehensive overview of this topic by exploring research, theories, biopsychosocial perspectives, and cross-cultural studies about happiness. The objectives of the project are: to analyze in-depth updates from research and clinic on how specific biological, psychological, and social factors are related to happiness, and how they develop in different contemporary cultural and anthropological contexts to further analyze their associations with mental health and, to investigate the interaction of demographic variables along different psychological and social trajectories, and thus obtain detailed information on the factors that influence the development of happiness.
\r\n\r\n\tFinally, this book aims to provide an overview to promote happiness and psychological well-being in the general population.
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She is among the authors of several books, including A Fresh Look at Anxiety Disorders and Psychopathy - New Updates on an Old Phenomenon, and has published articles for the Journal of Affective Disorders, Research in Psychotherapy, and the Journal for Psychiatric and Mental Health Nursing. She has participated in numerous conferences, seminars, and congresses. 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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.
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.
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.
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,
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.
Streptococcal skin and skin-structure infection (SSTI) is associated with significant morbidity all over the world and the impact is felt predominantly in resource-poor areas with inadequate personal hygiene and over-crowded living conditions. While exact numbers are difficult to estimate on account of the lack of systematic reporting, a literature search conducted by Sims and colleagues [1] reported an estimated prevalence of 18 million cases, with an incidence rate of around 1.78 million cases per year of invasive
Skin infections have been variously classified based on different criteria like depth of infection or the bacterial agents causing the infections or as primary infection in contrast to infection of pre-existing wounds or skin conditions. A very practical classification of patients hospitalized with skin infections (cellulitis versus abscess versus skin infections with additional complicating factors) has been described by Jenkins et al. [6]. The authors found in their study that cutaneous abscesses were primarily caused by
The clinical features of common streptococcal SSTIs and the antibiotics used in the management of these conditions will be further elaborated in this chapter.
Superficial skin infection has been described as
Impetigo secondary to infected contact dermatitis.
Antiseptic soaks and antibacterial creams are the mainstay of therapy for impetigo. A wide variety of topical antimicrobial agents are available including silver-based products, iodides, hydrogen peroxide, zinc, chlorhexidine and potassium permanganate. There is very little data in the literature comparing benefits of one product versus the other [7, 8]. Antibacterial creams: mupirocin, Na-fusidate and bacitracin are also available for use in localized superficial skin infections [9]. Drawbacks of topical therapy include development of resistance, risk of irritant or allergic dermatitis (sensitization), and if used in high concentrations, these could cause burn injuries.
When skin infection results in erythematous (red in color), edematous (raised above the surface) and well demarcated (sharp boundary between involved and uninvolved skin) areas of involvement, it is referred to as
Erysipelas with sharply-defined edematous red skin lesions.
When streptococcal infection involves the skin as well as the subcutaneous tissue, it results in ill-defined areas of erythema that are rapidly spreading and this is called
Cellulitis with irregular and ill-defined borders.
Fungal infection in the webspace of the toes, also called “athlete’s foot.”
Lymphangitic streaking of the upper extremity.
Lymphangitic streaking (double) of the lower extremity.
In some patients there is an overlap between erysipelas and cellulitis and the clinical differences are not so clear. Importantly, management of both conditions is similar.
Mild localized infections are treated with oral antibiotics, while more extensive infections or infections with systemic symptoms are treated with parenteral (intravenous) antibiotic therapy [14]. Patients with signs of sepsis: fever or hypothermia, tachycardia and hypotension, and patients with underlying conditions like uncontrolled diabetes, liver cirrhosis, severe peripheral vascular disease or severe lymphedema and patients with immunocompromising conditions like HIV, or patients on chemotherapy should be admitted to the hospital for antibiotics as well as aggressive management of the underlying conditions. Penicillins and β-lactams are considered the antibiotics of choice for treatment of streptococcal cellulitis. The addition of a second antibiotic like trimethoprim/sulfamethoxazole (TMP/SMX) or clindamycin has been shown to provide no additional benefit [6, 15, 16, 17, 18]. Penicillins are available in the form of oral as well as intravenous preparations (Table 1). Extended spectrum penicillins: dicloxacillin, amoxicillin, ampicillin, oxacillin and nafcillin can be used if there is associated methicillin susceptible
Name | Dosage | Comments |
---|---|---|
Oral agents | ||
Penicillin VK | 250–500 mg, 4 times a day | |
Dicloxacillin | 250–500 mg, 4 times a day | Effective also against MSSA |
Amoxicillin | 500 mg, 3 times a day | Effective also against MSSA |
Intravenous agents | ||
Penicillin G | 2–4 million units, q 4–6 h | Also available as continuous infusion via pump |
Ampicillin | 2 g, q 4–6 h | Effective also against MSSA |
Oxacillin, Nafcillin | 1–2 g, q 4–6 h | Effective also against MSSA |
Piperacillin-tazobactam* | 4.5 g, q 8 h | Effective also against MSSA, |
Penicilins.
Require dose adjustment in patients with kidney disease.
Name | Dosage | |
---|---|---|
Oral cephalosporins | ||
1st generation | cephalexin | 500 mg, 4 time a day |
2nd generation | cefaclor | 500 mg, 3 times a day |
cefuroxime | 500 mg, 2 times a day | |
3rd generation | cefpodoxime | 200 mg, 2 times a day |
Intravenous cephalosporins | ||
1st generation | cefazolin | 1–2 g, q 8 h |
3rd generation | ceftriaxone | 1–2 g, q 24 h |
5th generation | Ceftaroline | 600 mg, q 12 h |
Carbapenems (Intravenous) | ||
Imipenem | 0.5–1 g q 6 h | |
Meropenem | 1–2 g, q 8 h | |
Ertapenem | 1 g, q 24 h |
β-Lactam antibiotics used for streptococcal skin infections.
Effective also against MSSA. Ceftaroline is also effective against MRSA.
All (except ceftriaxone) require dose adjustment in patients with kidney disease.
Name | Drug class | Dose | Comments |
---|---|---|---|
Oral agents | |||
TMP/SMX* (160 mg/800 mg) | Sulphonamide | 1–2 tabs, 2 times a day | Effective also against MSSA, MRSA |
Watch for rash, monitor cbc, creatinine | |||
Doxycycline, Minocycline (100 mg) | Tetracycline derivative | 1 tab, 2 times a day | Effective also against MSSA, MRSA |
Risk for sunburn, pill esophagitis | |||
Linezolid (600 mg) | Oxazolidinone | 1 tab, 2 times a day | Effective also against MSSA, MRSA |
Avoid co-administration with SSRI, MAO inhibitors | |||
Risk for cytopenias, neuropathy | |||
Excellent oral-parenteral bioavailability | |||
Clindamycin (300 mg) | Lincosamide | 300–450 mg, 4 times a day | Effective also against MSSA, MRSA |
Highest risk for CDiff infection | |||
Ciprofloxacin, levofloxacin, moxifloxacin | Fluoroquinolone* | Different doses for different agents | Effective also against MSSA |
Risk for tendon injury, CNS side effects in the elderly, CDiff infection | |||
Intravenous agents | Effective also against MSSA, MRSA | ||
Vancomycin* | Glycopeptide | 15–20 mg/kg q 12 h | Close monitoring of levels to avoid nephrotoxicity. Red-man syndrome if administered too fast |
Daptomycin* | Cyclic lipopeptide | 4–6 mg/kg q 24 h | Risk of rhabdomyolysis, Esosinophilic pneumonia |
Linezolid | Oxazolidinone | 600 mg q 12 h | Avoid co-administration with SSRI, MAO inhibitors |
Risk for cytopenias, neuropathy | |||
Tigecycline | Tetracycline derivative (glycylcycline) | 100 mg X 1, then 50 mg q 12 h | Effective also against anaerobes |
Risk for Nausea |
Non β-lactam antibiotics used for streptococcal skin infections.
Require dose adjustment in patients with kidney disease.
Name | Drug class | Dose | Comments |
---|---|---|---|
Dalbavancin | Lipo-glycopeptide | Intravenous: 1.5 g single dose | One dose IV provides 2 weeks of therapy |
Oritavancin | Lipo-glycopeptide | Intravenous: 1.2 g single dose | One dose IV provides 2 weeks of therapy |
Delafloxacin | Fluoroquinolone | Intravenous: 300 mg q 12 h Oral: 450 mg twice a day | Allows transition from IV to oral. Risks as with other FQ |
Omadacycline | Tetracycline derivative | Intravenous: 200 mg X 1, then 100 mg daily Oral: 450 mg once a day for 2 days, then 300 mg once a day | Allows transition from IV to oral. Gastrointestinal side effects. Effective also against anaerobes |
Tedizolid | Oxazolidinone | Intravenous: 200 mg, q 24 h Oral: 200 mg once a day | Allows transition from IV to oral. Risk for cytopenias, neuropathy |
Newer antibiotics approved for treatment of skin infections.
Effective also against MSSA, MRSA.
When streptococcal infection spreads deep beyond the subcutaneous tissue, it can result in extensive necrosis (gangrene) of the overlying skin and inflammation and necrosis of underlying fascia
Necrotizing fasciitis of the lower extremity.
Clinical photograph showing erythema, peeling skin, dusky hue and areas of necrosis.
Necrotic areas with skip lesions on leg of patient who is abusing self with injection drugs.
When infection spreads beyond the fascial planes into the underlying muscles it is called myositis.
Necrosis of skin, soft tissue and muscle with exposure of tendon.
Patients need admission to the hospital often to the intensive care unit. They require management by a team of experts involving medical, surgical, infectious diseases and critical care specialties. They often present with septic shock and require pressors like epinephrine, norepinephrine and vasopressin to maintain adequate blood pressure in order to perfuse critical organs. Patients require broad spectrum antibiotic coverage, aggressive fluid resuscitation, as well as emergent aggressive debridement of the infected areas. Surgical removal of infected/necrotic tissue is essential in order to reduce bacterial burden and hence remove the source of toxins. Often patients require a second or even third visit to the operating room because of extensive tissue necrosis not amenable to removal in a single operation [14]. Operative tissue is sent for microbiology (cultures) to help determine the infectious agent and obtain an antibiotic sensitivity profile to help guide appropriate antibiotic choices. While awaiting the results of cultures, the antibiotics chosen should cover Gram-positive bacteria including
TSS is associated with a dramatic widespread skin rash and severe systemic symptoms. This condition is not due to direct inoculation of the skin with
Clinical photograph of sheet of erythema seen in acute phase of toxic shock syndrome.
Toxic shock syndrome with desquamation in the recovery phase.
As with other severe streptococcal infection, patients with TSS require admission to the hospital. If they are hypotensive or experience multi-organ failure, management is in the intensive care unit where patients are treated with aggressive fluid resuscitation, broad antibiotic therapy (choices similar to that as described for management of necrotizing fasciitis) and pressor support. Surgery may be required if a deep focus of infection is identified. Rarely patients do not respond to standard therapy and may require intravenous immunoglobulins (IVIG) [36].
Streptococcal SSTIs respond very well to antibiotic therapy. A wide range of antibiotics with excellent skin penetration are now available as noted in Table 1–4. All antibiotics carry the potential for side effects like allergic reactions and gastrointestinal disturbances. There are some side effects that are unique to certain antibiotics and patients need to be monitored for these toxicities. For example: β-lactam antibiotics have the potential for hepatotoxicity, vancomycin is associated with nephrotoxicity, daptomycin can cause rhabdomyolysis and eosinophilic pneumonitis and clindamycin is one of the most common antibiotics associated with
Mild infections should be treated with oral antibiotics.
Severe infections (severe local skin infection with systemic symptoms like fever, tachycardia, hypotension or leukocytosis and bacteremia, or more extensive skin infections even without systemic symptoms) will require parenteral therapy, with step-down to oral therapy as the patient improves [40]. Antibiotics like the floroquinolones: ciprofloxacin, levofloxacin, delafloxacin [41, 42], moxifloxacin [43], the oxazolidinones: linezolid, tedizolid and the new tetracycline: omadacycline [44] have excellent oral bioavailability and allow early conversion from intravenous to oral therapy.
In the most serious cases: sepsis, septic shock, necrotizing fasciitis, myositis, toxic shock syndrome: broad-spectrum antibiotics are required initially (most often with more than one antimicrobial agent) to cover
Duration of antibiotics: This depends on the severity of the infection as well as the clinical response to therapy. Mild infections or even severe infections in an otherwise healthy host that respond rapidly to antibiotics could be treated for as short as 5 days [14, 45, 46]. More severe infections or infections with a delayed response to therapy may need longer courses like 7, 10 or 14 days, depending upon the clinical picture. Shorter courses may be possible with some of the newer antibiotics including single dose antibiotics like dalbavancin [47] and oritavancin [28]. Relapses are found to be more common in patients with shorter courses of therapy [45]. Patients with bacteremia are usually treated for 14 days.
Dose adjustments: Antibiotics are cleared by the liver or kidney and hence dosage needs to be reduced in patients with liver or kidney disease in order to avoid toxicity. Conversely, patients who are obese require a higher dose of the antibiotic to achieve therapeutic levels in the skin [25, 48].
Suppressive therapy is attempted for patients with multiple recurrences [45, 49, 50]. Oral penicillin twice daily showed a 70–80% reduction in episodes—but recurrences occurred after discontinuation of prophylaxis. Treatment of underlying factors like athlete’s foot, chronic lymphedema, peripheral vascular disease and uncontrolled diabetes is also very important in the prevention of recurrences [11, 44, 45, 51]
Streptococcal skin infections cause significant morbidity all over the world, and severe infections like necrotizing fasciitis and toxic shock syndrome can be fatal. There is a wide spectrum of manifestations of skin infections ranging from mild superficial disease to deep necrotic and life-threatening infections. Skin infection is one of the most common reasons for prescriptions of antibiotics in the community as well as in hospitalized patients. Some of the most commonly used antibiotics have excellent skin penetration and hence the armamentarium to treat skin infections is quite large. Over the last few years there have been multiple new antibiotics approved for the treatment of skin infections and these should be reserved for treatment of severe infections not responding to the common antibiotics and for infections with multi-drug-resistant organisms. A thorough understanding of the different types of skin infections, as well as a detailed knowledge of the different antibiotics are essential for the early diagnosis and selection of the most appropriate antibiotic for the management of simple as well as complex skin infections.
These Terms and Conditions outline the rules and regulations pertaining to the use of IntechOpen’s website www.intechopen.com and all the subdomains owned by IntechOpen located at 5 Princes Gate Court, London, SW7 2QJ, United Kingdom.
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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. 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Dr. Suzuki currently serves as a visiting researcher at Kogakuin University, Japan, and also a vice president of the Japan Firefly Society.",institutionString:"Kogakuin University",institution:null}]}]},openForSubmissionBooks:{},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/67618",hash:"",query:{},params:{id:"67618"},fullPath:"/chapters/67618",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()