September 23, 2017, 3:14 pm
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1 Philippine Peso = 0.07205 UAE Dirham
1 Philippine Peso = 2.19737 Albanian Lek
1 Philippine Peso = 0.03473 Neth Antilles Guilder
1 Philippine Peso = 0.33883 Argentine Peso
1 Philippine Peso = 0.02472 Australian Dollar
1 Philippine Peso = 0.03508 Aruba Florin
1 Philippine Peso = 0.03924 Barbados Dollar
1 Philippine Peso = 1.60624 Bangladesh Taka
1 Philippine Peso = 0.03223 Bulgarian Lev
1 Philippine Peso = 0.0074 Bahraini Dinar
1 Philippine Peso = 34.03414 Burundi Franc
1 Philippine Peso = 0.01962 Bermuda Dollar
1 Philippine Peso = 0.02647 Brunei Dollar
1 Philippine Peso = 0.13537 Bolivian Boliviano
1 Philippine Peso = 0.06149 Brazilian Real
1 Philippine Peso = 0.01962 Bahamian Dollar
1 Philippine Peso = 1.26104 Bhutan Ngultrum
1 Philippine Peso = 0.20051 Botswana Pula
1 Philippine Peso = 392.78006 Belarus Ruble
1 Philippine Peso = 0.03919 Belize Dollar
1 Philippine Peso = 0.02419 Canadian Dollar
1 Philippine Peso = 0.01905 Swiss Franc
1 Philippine Peso = 12.25231 Chilean Peso
1 Philippine Peso = 0.12921 Chinese Yuan
1 Philippine Peso = 57.14342 Colombian Peso
1 Philippine Peso = 11.22072 Costa Rica Colon
1 Philippine Peso = 0.01962 Cuban Peso
1 Philippine Peso = 1.81263 Cape Verde Escudo
1 Philippine Peso = 0.42857 Czech Koruna
1 Philippine Peso = 3.49225 Djibouti Franc
1 Philippine Peso = 0.12231 Danish Krone
1 Philippine Peso = 0.92211 Dominican Peso
1 Philippine Peso = 2.19774 Algerian Dinar
1 Philippine Peso = 0.25715 Estonian Kroon
1 Philippine Peso = 0.34589 Egyptian Pound
1 Philippine Peso = 0.45831 Ethiopian Birr
1 Philippine Peso = 0.01644 Euro
1 Philippine Peso = 0.03953 Fiji Dollar
1 Philippine Peso = 0.01454 Falkland Islands Pound
1 Philippine Peso = 0.01447 British Pound
1 Philippine Peso = 0.08679 Ghanaian Cedi
1 Philippine Peso = 0.87895 Gambian Dalasi
1 Philippine Peso = 174.63213 Guinea Franc
1 Philippine Peso = 0.14311 Guatemala Quetzal
1 Philippine Peso = 3.97705 Guyana Dollar
1 Philippine Peso = 0.15314 Hong Kong Dollar
1 Philippine Peso = 0.45756 Honduras Lempira
1 Philippine Peso = 0.12286 Croatian Kuna
1 Philippine Peso = 1.19973 Haiti Gourde
1 Philippine Peso = 5.08986 Hungarian Forint
1 Philippine Peso = 260.48656 Indonesian Rupiah
1 Philippine Peso = 0.0688 Israeli Shekel
1 Philippine Peso = 1.27132 Indian Rupee
1 Philippine Peso = 22.89582 Iraqi Dinar
1 Philippine Peso = 658.62271 Iran Rial
1 Philippine Peso = 2.10712 Iceland Krona
1 Philippine Peso = 2.56229 Jamaican Dollar
1 Philippine Peso = 0.01388 Jordanian Dinar
1 Philippine Peso = 2.20489 Japanese Yen
1 Philippine Peso = 2.02178 Kenyan Shilling
1 Philippine Peso = 1.3433 Kyrgyzstan Som
1 Philippine Peso = 79.4585 Cambodia Riel
1 Philippine Peso = 8.05435 Comoros Franc
1 Philippine Peso = 17.65745 North Korean Won
1 Philippine Peso = 22.18972 Korean Won
1 Philippine Peso = 0.00592 Kuwaiti Dinar
1 Philippine Peso = 0.01609 Cayman Islands Dollar
1 Philippine Peso = 6.67785 Kazakhstan Tenge
1 Philippine Peso = 162.84088 Lao Kip
1 Philippine Peso = 29.53698 Lebanese Pound
1 Philippine Peso = 2.99588 Sri Lanka Rupee
1 Philippine Peso = 2.29351 Liberian Dollar
1 Philippine Peso = 0.26015 Lesotho Loti
1 Philippine Peso = 0.05981 Lithuanian Lita
1 Philippine Peso = 0.01217 Latvian Lat
1 Philippine Peso = 0.02654 Libyan Dinar
1 Philippine Peso = 0.18329 Moroccan Dirham
1 Philippine Peso = 0.34501 Moldovan Leu
1 Philippine Peso = 1.00647 Macedonian Denar
1 Philippine Peso = 26.68236 Myanmar Kyat
1 Philippine Peso = 48.14597 Mongolian Tugrik
1 Philippine Peso = 0.15773 Macau Pataca
1 Philippine Peso = 7.0826 Mauritania Ougulya
1 Philippine Peso = 0.65097 Mauritius Rupee
1 Philippine Peso = 0.30135 Maldives Rufiyaa
1 Philippine Peso = 14.05376 Malawi Kwacha
1 Philippine Peso = 0.34969 Mexican Peso
1 Philippine Peso = 0.08232 Malaysian Ringgit
1 Philippine Peso = 0.2598 Namibian Dollar
1 Philippine Peso = 6.92564 Nigerian Naira
1 Philippine Peso = 0.58623 Nicaragua Cordoba
1 Philippine Peso = 0.15332 Norwegian Krone
1 Philippine Peso = 2.01197 Nepalese Rupee
1 Philippine Peso = 0.02683 New Zealand Dollar
1 Philippine Peso = 0.00755 Omani Rial
1 Philippine Peso = 0.01962 Panama Balboa
1 Philippine Peso = 0.06369 Peruvian Nuevo Sol
1 Philippine Peso = 0.06268 Papua New Guinea Kina
1 Philippine Peso = 1 Philippine Peso
1 Philippine Peso = 2.06494 Pakistani Rupee
1 Philippine Peso = 0.07028 Polish Zloty
1 Philippine Peso = 111.25171 Paraguayan Guarani
1 Philippine Peso = 0.07269 Qatar Rial
1 Philippine Peso = 0.0755 Romanian New Leu
1 Philippine Peso = 1.13354 Russian Rouble
1 Philippine Peso = 16.2576 Rwanda Franc
1 Philippine Peso = 0.07357 Saudi Arabian Riyal
1 Philippine Peso = 0.15204 Solomon Islands Dollar
1 Philippine Peso = 0.2669 Seychelles Rupee
1 Philippine Peso = 0.13067 Sudanese Pound
1 Philippine Peso = 0.15655 Swedish Krona
1 Philippine Peso = 0.02649 Singapore Dollar
1 Philippine Peso = 0.01455 St Helena Pound
1 Philippine Peso = 0.43567 Slovak Koruna
1 Philippine Peso = 147.14538 Sierra Leone Leone
1 Philippine Peso = 10.928 Somali Shilling
1 Philippine Peso = 402.77613 Sao Tome Dobra
1 Philippine Peso = 0.17167 El Salvador Colon
1 Philippine Peso = 10.10359 Syrian Pound
1 Philippine Peso = 0.2598 Swaziland Lilageni
1 Philippine Peso = 0.64921 Thai Baht
1 Philippine Peso = 0.04791 Tunisian Dinar
1 Philippine Peso = 0.0432 Tongan paʻanga
1 Philippine Peso = 0.06876 Turkish Lira
1 Philippine Peso = 0.13239 Trinidad Tobago Dollar
1 Philippine Peso = 0.59217 Taiwan Dollar
1 Philippine Peso = 43.90818 Tanzanian Shilling
1 Philippine Peso = 0.51422 Ukraine Hryvnia
1 Philippine Peso = 70.57092 Ugandan Shilling
1 Philippine Peso = 0.01962 United States Dollar
1 Philippine Peso = 0.56582 Uruguayan New Peso
1 Philippine Peso = 158.34804 Uzbekistan Sum
1 Philippine Peso = 0.19569 Venezuelan Bolivar
1 Philippine Peso = 445.73278 Vietnam Dong
1 Philippine Peso = 2.0155 Vanuatu Vatu
1 Philippine Peso = 0.04907 Samoa Tala
1 Philippine Peso = 10.773 CFA Franc (BEAC)
1 Philippine Peso = 0.05297 East Caribbean Dollar
1 Philippine Peso = 10.75142 CFA Franc (BCEAO)
1 Philippine Peso = 1.95017 Pacific Franc
1 Philippine Peso = 4.90386 Yemen Riyal
1 Philippine Peso = 0.25991 South African Rand
1 Philippine Peso = 101.81479 Zambian Kwacha
1 Philippine Peso = 7.10025 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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