Intelligent Network Management and Control. Badr Benmammar

Intelligent Network Management and Control - Badr Benmammar


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confidentiality, integrity and availability triad is a fundamental concept of information security. Each organization strives to ensure these three elements of the information system. Confidentiality prevents unauthorized disclosure of sensitive information (Kumar et al. 2018). Integrity prevents any unauthorized modification of information, thus ensuring information accuracy. Cryptographic hashing functions (such as SHA-1 or SHA-2) can be used to ensure data integrity. Availability prevents loss of access to resources and information (Kumar et al. 2018).

      Capgemini Research Institute has conducted a survey of 850 managers of seven large industrial companies: among the top management members included in this survey, 20% are information systems managers and 10% are responsible for information systems security. Companies headquartered in France, Germany, United Kingdom, the United States, Australia, India and Italy are mentioned in the report (Capgemini Research Institute 2019). Capgemini noted that, as digital companies develop, their cyberattack risk increases exponentially. It has been noted that 21% of companies declared one cybersecurity breach experience leading to unauthorized access in 2018. The price paid by companies for cybersecurity breaches is heavy (20% declared losses of over 50 million dollars). According to this survey, 69% of the companies estimate a need for AI to counteract cyberattacks. The majority of telecommunications companies (80%) declared that they relied on AI to identify the threats and counteract the attacks. According to the Capgemini report, the telecommunications sector declared the highest losses of over 50 million dollars, which led to AI being considered a priority in counteracting the costly breaches in this sector. Understandably, consumer goods sellers (78%) and banks (75%) came second and third, respectively, in this ranking, as these sectors increasingly rely on digital models. Companies based in the United States have as their top priority AI-based cybersecurity applications and platforms.

      Figure 1.2. Organizations and countries relying on artificial intelligence to identify threats and counteract attacks

      An ideal cyberdefense would offer full protection to users, while preserving system performances. Although this ideal cyberdefense may currently seem very distant, steps could be taken toward it by rendering cyberdefense more intelligent. The idea of using AI techniques in cybersecurity is not new. Landwehr (2008) states that, at their start, computer security and AI did not seem to have much in common. Researchers in the field of AI wanted computers to do by themselves what humans were able to do, whereas the researchers in the security field tried to solve the leakages in the computer systems, which they considered vulnerable. According to Schneier (2008), “The Internet is the most complex machine ever built. We barely understand how it works, not to mention how to secure it”. Given the rapid multiplication of new web applications and the increasing use of wireless networks (Barth and Mitchell 2008) and the Internet of Things, cybersecurity has become the most complex threat to society.

      The need for securing web applications against attacks (such as Cross Site Scripting [XSS], Cross Site Request Forgery [CSRF] and code injection) is increasingly obvious and pressing. Over time, XSS and CSRF scripts have been used to conduct various attacks. Some of them can be interpreted as direct bypasses of the original security policy. The same security policy was similar to a simple and efficient protection, but it turned out it could be easily bypassed and certain functionalities of modern websites could be blocked. According to Crockford (2015), the security policies adopted by most browsers “block useful contents and authorize dangerous contents”. These policies are currently being reviewed. However, the detection of attacks such as XSS, CSRF or code injection requires more than a simple rule, namely a context-dependent reasoning capacity.

      The use of AI in cybersecurity generally involves certain smart tools and their application to intrusion detection (Ahmad et al. 2016; Kalaivani et al. 2019) or other aspects of cybersecurity (Ahlan et al. 2015). This approach involves the use of other AI techniques developed for problems that are entirely different from cybersecurity; this may work in certain cases, but it has inherent and strict limitations. Cybersecurity has specific needs, and meeting them requires new specifically developed AI techniques. Obviously, AI has substantially evolved in certain fields, but there is still a need for learning and developing new intelligent techniques adapted to cybersecurity. In this context, according to Landwehr (2008) one “AI


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