June 21, 2018, 9:45 pm
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1 Philippine Peso = 0.06897 UAE Dirham
1 Philippine Peso = 2.04526 Albanian Lek
1 Philippine Peso = 0.03404 Neth Antilles Guilder
1 Philippine Peso = 0.52113 Argentine Peso
1 Philippine Peso = 0.02544 Australian Dollar
1 Philippine Peso = 0.03343 Aruba Florin
1 Philippine Peso = 0.03756 Barbados Dollar
1 Philippine Peso = 1.57728 Bangladesh Taka
1 Philippine Peso = 0.03184 Bulgarian Lev
1 Philippine Peso = 0.00709 Bahraini Dinar
1 Philippine Peso = 32.88225 Burundi Franc
1 Philippine Peso = 0.01878 Bermuda Dollar
1 Philippine Peso = 0.02522 Brunei Dollar
1 Philippine Peso = 0.12883 Bolivian Boliviano
1 Philippine Peso = 0.07009 Brazilian Real
1 Philippine Peso = 0.01878 Bahamian Dollar
1 Philippine Peso = 1.277 Bhutan Ngultrum
1 Philippine Peso = 0.19573 Botswana Pula
1 Philippine Peso = 375.96244 Belarus Ruble
1 Philippine Peso = 0.03752 Belize Dollar
1 Philippine Peso = 0.02494 Canadian Dollar
1 Philippine Peso = 0.01868 Swiss Franc
1 Philippine Peso = 12.01146 Chilean Peso
1 Philippine Peso = 0.12169 Chinese Yuan
1 Philippine Peso = 54.86948 Colombian Peso
1 Philippine Peso = 10.59718 Costa Rica Colon
1 Philippine Peso = 0.01878 Cuban Peso
1 Philippine Peso = 1.78854 Cape Verde Escudo
1 Philippine Peso = 0.41869 Czech Koruna
1 Philippine Peso = 3.33333 Djibouti Franc
1 Philippine Peso = 0.12088 Danish Krone
1 Philippine Peso = 0.93052 Dominican Peso
1 Philippine Peso = 2.20053 Algerian Dinar
1 Philippine Peso = 0.25367 Estonian Kroon
1 Philippine Peso = 0.33502 Egyptian Pound
1 Philippine Peso = 0.51117 Ethiopian Birr
1 Philippine Peso = 0.01621 Euro
1 Philippine Peso = 0.03897 Fiji Dollar
1 Philippine Peso = 0.01426 Falkland Islands Pound
1 Philippine Peso = 0.01425 British Pound
1 Philippine Peso = 0.08833 Ghanaian Cedi
1 Philippine Peso = 0.87962 Gambian Dalasi
1 Philippine Peso = 169.05164 Guinea Franc
1 Philippine Peso = 0.14052 Guatemala Quetzal
1 Philippine Peso = 3.88526 Guyana Dollar
1 Philippine Peso = 0.14739 Hong Kong Dollar
1 Philippine Peso = 0.44866 Honduras Lempira
1 Philippine Peso = 0.1197 Croatian Kuna
1 Philippine Peso = 1.23812 Haiti Gourde
1 Philippine Peso = 5.22103 Hungarian Forint
1 Philippine Peso = 261.46479 Indonesian Rupiah
1 Philippine Peso = 0.06819 Israeli Shekel
1 Philippine Peso = 1.27817 Indian Rupee
1 Philippine Peso = 22.23474 Iraqi Dinar
1 Philippine Peso = 796.99531 Iran Rial
1 Philippine Peso = 2.05333 Iceland Krona
1 Philippine Peso = 2.4507 Jamaican Dollar
1 Philippine Peso = 0.01331 Jordanian Dinar
1 Philippine Peso = 2.06607 Japanese Yen
1 Philippine Peso = 1.89577 Kenyan Shilling
1 Philippine Peso = 1.28255 Kyrgyzstan Som
1 Philippine Peso = 75.84601 Cambodia Riel
1 Philippine Peso = 7.92488 Comoros Franc
1 Philippine Peso = 16.90141 North Korean Won
1 Philippine Peso = 20.8492 Korean Won
1 Philippine Peso = 0.00568 Kuwaiti Dinar
1 Philippine Peso = 0.0154 Cayman Islands Dollar
1 Philippine Peso = 6.40488 Kazakhstan Tenge
1 Philippine Peso = 157.33333 Lao Kip
1 Philippine Peso = 28.26291 Lebanese Pound
1 Philippine Peso = 3.00282 Sri Lanka Rupee
1 Philippine Peso = 2.66254 Liberian Dollar
1 Philippine Peso = 0.2584 Lesotho Loti
1 Philippine Peso = 0.05725 Lithuanian Lita
1 Philippine Peso = 0.01165 Latvian Lat
1 Philippine Peso = 0.02546 Libyan Dinar
1 Philippine Peso = 0.17921 Moroccan Dirham
1 Philippine Peso = 0.31576 Moldovan Leu
1 Philippine Peso = 0.99324 Macedonian Denar
1 Philippine Peso = 25.69014 Myanmar Kyat
1 Philippine Peso = 45.33333 Mongolian Tugrik
1 Philippine Peso = 0.15181 Macau Pataca
1 Philippine Peso = 6.66667 Mauritania Ougulya
1 Philippine Peso = 0.65765 Mauritius Rupee
1 Philippine Peso = 0.29239 Maldives Rufiyaa
1 Philippine Peso = 13.39812 Malawi Kwacha
1 Philippine Peso = 0.3853 Mexican Peso
1 Philippine Peso = 0.07515 Malaysian Ringgit
1 Philippine Peso = 0.25797 Namibian Dollar
1 Philippine Peso = 6.74178 Nigerian Naira
1 Philippine Peso = 0.59151 Nicaragua Cordoba
1 Philippine Peso = 0.15379 Norwegian Krone
1 Philippine Peso = 2.0385 Nepalese Rupee
1 Philippine Peso = 0.0272 New Zealand Dollar
1 Philippine Peso = 0.00723 Omani Rial
1 Philippine Peso = 0.01878 Panama Balboa
1 Philippine Peso = 0.06164 Peruvian Nuevo Sol
1 Philippine Peso = 0.06142 Papua New Guinea Kina
1 Philippine Peso = 1 Philippine Peso
1 Philippine Peso = 2.28545 Pakistani Rupee
1 Philippine Peso = 0.06993 Polish Zloty
1 Philippine Peso = 106.70047 Paraguayan Guarani
1 Philippine Peso = 0.06835 Qatar Rial
1 Philippine Peso = 0.07565 Romanian New Leu
1 Philippine Peso = 1.1966 Russian Rouble
1 Philippine Peso = 15.95174 Rwanda Franc
1 Philippine Peso = 0.07042 Saudi Arabian Riyal
1 Philippine Peso = 0.14841 Solomon Islands Dollar
1 Philippine Peso = 0.25277 Seychelles Rupee
1 Philippine Peso = 0.33719 Sudanese Pound
1 Philippine Peso = 0.16718 Swedish Krona
1 Philippine Peso = 0.02548 Singapore Dollar
1 Philippine Peso = 0.01426 St Helena Pound
1 Philippine Peso = 0.41701 Slovak Koruna
1 Philippine Peso = 149.29577 Sierra Leone Leone
1 Philippine Peso = 10.57277 Somali Shilling
1 Philippine Peso = 397.4216 Sao Tome Dobra
1 Philippine Peso = 0.16432 El Salvador Colon
1 Philippine Peso = 9.67099 Syrian Pound
1 Philippine Peso = 0.25817 Swaziland Lilageni
1 Philippine Peso = 0.61446 Thai Baht
1 Philippine Peso = 0.04845 Tunisian Dinar
1 Philippine Peso = 0.04326 Tongan paʻanga
1 Philippine Peso = 0.08905 Turkish Lira
1 Philippine Peso = 0.12487 Trinidad Tobago Dollar
1 Philippine Peso = 0.56648 Taiwan Dollar
1 Philippine Peso = 42.59155 Tanzanian Shilling
1 Philippine Peso = 0.49596 Ukraine Hryvnia
1 Philippine Peso = 72.33803 Ugandan Shilling
1 Philippine Peso = 0.01878 United States Dollar
1 Philippine Peso = 0.59211 Uruguayan New Peso
1 Philippine Peso = 147.69953 Uzbekistan Sum
1 Philippine Peso = 1498.59155 Venezuelan Bolivar
1 Philippine Peso = 429.12676 Vietnam Dong
1 Philippine Peso = 2.02911 Vanuatu Vatu
1 Philippine Peso = 0.04869 Samoa Tala
1 Philippine Peso = 10.62592 CFA Franc (BEAC)
1 Philippine Peso = 0.0507 East Caribbean Dollar
1 Philippine Peso = 10.62592 CFA Franc (BCEAO)
1 Philippine Peso = 1.92432 Pacific Franc
1 Philippine Peso = 4.69202 Yemen Riyal
1 Philippine Peso = 0.25823 South African Rand
1 Philippine Peso = 97.4554 Zambian Kwacha
1 Philippine Peso = 6.79624 Zimbabwe dollar

CTO reflections: Beyond the appliance

Michael Xie,Founder, President and CTO, Fortinet

FOR anyone reading the news regularly, it’s not hard to grasp that cyber threats are getting more sophisticated and damaging by the day. From a security technology provider’s perspective, I can add that tackling them is a fast mounting challenge for the millions of businesses that come under attack daily.

Modern cybersecurity technologies – assuming you have already put in place the right professionals, policies and processes − are a must but organizations deploying them need to look beyond the boxes that sit on their racks.

What underpins the security appliances is invisible, but plays a pivotal role in ensuring that those boxes block the threats that imperil your business. Threat intelligence − or more specifically, the security appliances’ ability to know the ins-and-outs of the evolving threat landscape and respond to them appropriately – is the fuel that powers your cyber defenses.

Getting timely, accurate and predictive threat intelligence is much tougher than it sounds. It calls for a robust R&D set-up, which comprises a few components:

Divide and conquer − In many aspects of business, large teams equate to large outputs. When trying to outsmart well motivated cybercriminals, however, following conventional wisdom seldom works well. In my experience, an effective threat research organisation should be made up of many small teams, with each team dedicated to a particular type of threat. Creating such research focuses boosts each team’s specialization and competency − leading to faster discovery of threats, and the identification of more threats − while shortening customer response times to incidents.

Stay fleet-footed − Threat research teams must be nimble. The threat landscape is highly dynamic, changing by the day, or even hours and minutes. The teams must be able to adjust their priorities and refocus on the fly. At Fortinet, for instance, based on our projections of how the threat landscape will evolve, research plans are updated. From the new directions identified, researchers with the most appropriate skill sets are selected to join specific task forces to delve into those emerging threats.

Examples of such threats in recent times include IoT, ransomware and autonomous malware.

See the big picture − Researchers must be encouraged to think big and pursue their own interests, even if those interests don’t have a direct link to the company’s products. Research on IoT vulnerabilities, for instance, can deepen an enterprise security provider’s understanding of the threat landscape.

Hone your instincts − Research leaders must train their teams to develop the acumen to identify a threat as important before that fact becomes obvious to all. Good threat researchers, for instance, have been warning for years that IoT vulnerabilities are the next big menace − before the Mirai IoT botnet appeared last September and made it plain to the world. Threats emerge and evolve swiftly. If a security provider is slow to research on them and react, its customers will be slow to get protected.         
Amass data – The more data a threat research team has access to, the greater the potential of its research outcome. Enlightened research organizations share – not hoard – information. At Fortinet, for example, beyond tapping the 3 million sensors we have deployed around the globe, we actively exchange threat intelligence with organizations like INTERPOL, NATO, KISA and other security technology providers through the Cyber Threat Alliance. In recent months, we have also succeeded in bringing on board more government entities and carriers globally. That’s a positive development, as it helps all parties build a bigger threat database to monitor, block and trace malware back to their sources.

Invest in research technology – The days of manually analyzing threat information have long passed us by. Effective research teams need advanced tools to interpret and correlate the reams of data coming through to them every second. While today we have Content Pattern Recognition Languages (CPRLs) to help identify thousands of current and future virus variants with a single signature, the future belongs to technologies like big data analytics and artificial intelligence. Soon, AI in cybersecurity will constantly adapt to the growing attack surface. Today, human beings are performing the relatively complex tasks of connecting the dots, sharing data and applying that data to systems. In future, a mature AI system will be able to automate many of these complex decisions on its own.

No matter how advanced AI becomes, however, full automation – or the passing of 100% of the control to machines to make all the decisions all the time – is not attainable. Human intervention will still be needed. Big data and analytics platforms allow malware progression to be predicted but not malware mutation. Only the human mind could have foreseen that ransomware like Wannacry would embed the National Security Agency’s vulnerability exploits to propagate on unpatched systems.

Malware evolution will intrinsically follow human evolution and how people blend new technologies into their everyday life. If in the coming years, for instance, self-driving cars and wearable IoT find widespread adoption, cybercriminals will – as they have always done – find ways to ride the wave and exploit those cars and devices. Likewise, cryptocurrencies, if they continue to find favor at the rate they gained momentum this year, will attract herds of hackers.

The concept of automation is opening up many new possibilities for cybercriminals, and turning up the heat on organizations. As hackers step up the amount of automation in their malware, attacks will not only come at organizations faster, they will also reduce the time between breach and impact, and learn to avoid detection. Increasingly, firms will need to respond in near real time − in a coordinated fashion across the distributed network ecosystem, from IoT to the cloud. Not many enterprises have the capability to do this today, and that’s something CIOs should start worrying about.
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